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Journal Cover Control Engineering Practice
  [SJR: 1.354]   [H-I: 84]   [43 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0967-0661
   Published by Elsevier Homepage  [3089 journals]
  • Adaptive soft sensors for quality prediction under the framework of
           Bayesian network
    • Abstract: Publication date: March 2018
      Source:Control Engineering Practice, Volume 72
      Author(s): Ziwei Liu, Zhiqiang Ge, Guangjie Chen, Zhihuan Song
      Soft sensor is widely used to predict quality-relevant variables which are usually hard to measure timely. Due to model degradation, it is necessary to construct an adaptive model to follow changes of the process. Adaptive models—moving windows (MW), time difference (TD), and locally weighted regression (LWR) under the framework of Bayesian network (BN) are proposed in this paper. BN shows great superiorities over other traditional methods, especially in dealing with missing data and the ability of learning causality. Furthermore, the acquisition of variances in BN makes it possible to perform fault detection, on the basis of 3-sigma criterion. A debutanizer column and CO2 absorption column are provided as two industrial examples to validate the effectiveness of our proposed techniques. In a debutanizer column, RMSE of MW-BN is decreased by 40% in comparison to MW-PLS. In a CO2 absorption column, the largest absolute prediction error of TD-BN is reduced by approximate 7% when compared with that of TD-PLS. Furthermore, about 38% improvements of prediction precision can be achieved in LW-BN in contrast to LW-PLS.

      PubDate: 2017-12-13T02:45:14Z
       
  • Model-based multi-component adaptive prognosis for hybrid dynamical
           systems
    • Abstract: Publication date: March 2018
      Source:Control Engineering Practice, Volume 72
      Author(s): Om Prakash, Arun Kumar Samantaray, Ranjan Bhattacharyya
      A bond graph model-based prognosis method for multiple components with unknown degradation patterns in a hybrid dynamical system is proposed. The traditional approach for remaining useful life prediction with single degradation model is inappropriate for hybrid systems where the dynamics changes according to operating mode. Therefore, multiple degradation models are suggested and these are adapted with new information of the degradation states of the monitored system. Sensitivity-based dynamic signature matrix is utilized for degradation hypothesis generation which provides the deviation directions of fewer hypothesized degradation parameters and thereby accelerates parameter and degradation trend estimation. The results are supported by experiments.

      PubDate: 2017-12-13T02:45:14Z
       
  • Internal model-based feedback control design for inversion-free
           feedforward rate-dependent hysteresis compensation of piezoelectric
           cantilever actuator
    • Abstract: Publication date: March 2018
      Source:Control Engineering Practice, Volume 72
      Author(s): Mohammad Al Janaideh, Micky Rakotondrabe, Isam Al-Darabsah, Omar Aljanaideh
      This study proposes a new rate-dependent feedforward compensator for compensation of hysteresis nonlinearities in smart materials-based actuators without considering the analytical inverse model. The proposed rate-dependent compensator is constructed with the inverse multiplicative structure of the rate-dependent Prandtl–Ishlinskii (RDPI) model. The study also presents an investigation for the compensation error when the proposed compensator is applied in an open-loop feedforward manner. Then, an internal model-based feedback control design is applied with the proposed feedforward compensator to a piezoelectric cantilever actuator. The experimental results illustrate that the proposed feedforward–feedback control scheme can be used in micro-positioning motion control applications to enhance the tracking performance of the piezoelectric cantilever actuator under different operating conditions.

      PubDate: 2017-12-13T02:45:14Z
       
  • Multiplexed extremum seeking for calibration of spark timing in a
           CNG-fuelled engine
    • Abstract: Publication date: March 2018
      Source:Control Engineering Practice, Volume 72
      Author(s): Jalil Sharafi, William H. Moase, Chris Manzie
      The compositional variability of many alternative fuels, coupled with fuel agnostic behaviour like engine ageing and vehicle-to-vehicle differences, leads to the desire for some form of online calibration in order to optimise fuel efficiency. This has led to the incorporation of extremum seeking techniques within the field in order to continually fine tune engine performance. These typically address steady state engine performance and are characterised by slow convergence times, hindering their deployment in typical dynamic driving scenarios. To address this potential shortcoming, in this paper a novel multiplexed extremum seeking scheme is proposed to track a time-varying extremum caused by a measurable disturbance. It consists of multiple extremum seeking agents that are individually activated based on the disturbance. The multiplexed approach accommodates the rigorous practical stability results of the “traditional” extremum seeking approaches, but offers improved results in dynamic scenarios. The proposed approach is implemented both in simulation and experimentally on a compressed natural gas (CNG) engine operating over a drive cycle. The experimental results show that under proper tuning, the proposed controller can improve the engine fuel efficiency for unknown natural gas compositions without requiring gas composition sensing at little additional calibration effort.

      PubDate: 2017-12-13T02:45:14Z
       
  • LPV-based power system stabilizer: Identification, control and field tests
    • Abstract: Publication date: March 2018
      Source:Control Engineering Practice, Volume 72
      Author(s): Fabrício Gonzalez Nogueira, Walter Barra Junior, Carlos Tavares da Costa Junior, Janio José Lana
      This paper shows the design and tests of an LPV power system stabilizer aimed at improving the damping of electromechanical oscillations in power systems. In order to capture the dynamic model for control design, LPV models were estimated from experimental data. The generator active and reactive powers were used as scheduling parameters. The control problem is formulated as a parameterized linear matrix inequality, which the positivity condition is relaxed through a sum-of-squares decomposition. The controller ensures stability and H ∞ performance for a set of operating conditions. Field tests were carried out on a 10-kVA machine and on a 350-MVA hydroelectric generator.

      PubDate: 2017-12-13T02:45:14Z
       
  • Variable selection for nonlinear soft sensor development with enhanced
           Binary Differential Evolution algorithm
    • Abstract: Publication date: March 2018
      Source:Control Engineering Practice, Volume 72
      Author(s): Le Yao, Zhiqiang Ge
      In this paper, two enhanced Binary Differential Evolution (BDE) algorithms are proposed to select variables for nonlinear process soft sensor development. Firstly, the Parallel BDE (PBDE) algorithm is presented to extract the optimal individuals of several parallel short evolution paths of basic BDE, where the spurious variables are effectively eliminated. And the most relevant variables are selected through a double-layer selection strategy with the validating Root Mean Square Error (RMSE) for evaluating criterion. Secondly, the Boosting BDE (BBDE) algorithm is proposed through applying the boosting technique to the parallel evolution paths. The performance of the previous path needs to be taken into account when conducting the current evolution path. The selected probabilities of variables are given through the weighted summation of the selection results of all paths. Also, a double-layer selection is conducted on BBDE algorithm. The feasibility and effectiveness of the proposed methods are demonstrated through a nonlinear numerical example and a real industrial process.

      PubDate: 2017-12-13T02:45:14Z
       
  • Disturbance-observer based control for magnetically suspended wheel with
           synchronous noise
    • Abstract: Publication date: March 2018
      Source:Control Engineering Practice, Volume 72
      Author(s): Yuanjin Yu, Zhaohua Yang, Chao Han, Hu Liu
      A disturbance-observer based method is proposed to attenuate the synchronous vibration of a magnetically suspended wheel (MSW). When the rotary speed is nonzero, the synchronous vibration exists. To analyze this vibration, a precise dynamic of the MSW is researched and the synchronous vibrations model is established. A novel vibration attenuation method is proposed by combining a disturbance observer and a state-feedback method. Using Lyapunov’s stability theorem, parameters of the controller are determined. Finally, results of numerical simulations and experiments indicate that the proposed method dramatically reduces the synchronous jitter and thus significantly improves precision of the deflection angle.

      PubDate: 2017-12-13T02:45:14Z
       
  • Self-tuning MIMO disturbance feedforward control for active hard-mounted
           vibration isolators
    • Abstract: Publication date: March 2018
      Source:Control Engineering Practice, Volume 72
      Author(s): M.A. Beijen, M.F. Heertjes, J. Van Dijk, W.B.J. Hakvoort
      This paper proposes a multi-input multi-output (MIMO) disturbance feedforward controller to improve the rejection of floor vibrations in active vibration isolation systems for high-precision machinery. To minimize loss of performance due to model uncertainties, the feedforward controller is implemented as a self-tuning generalized FIR filter. This filter contains a priori knowledge of the poles, such that relatively few parameters have to be estimated which makes the algorithm computationally efficient. The zeros of the filter are estimated using the filtered-error least mean squares (FeLMS) algorithm. Residual noise shaping is used to reduce bias. Conditions on convergence speed, stability, bias, and the effects of sensor noise on the self-tuning algorithm are discussed in detail. The combined control strategy is validated on a 6-DOF Stewart platform, which serves as a multi-axis and hard-mounted vibration isolation system, and shows significant improvement in the rejection of floor vibrations.

      PubDate: 2017-12-13T02:45:14Z
       
  • Robust fault detection with Interval Valued Uncertainties in Bond Graph
           Framework
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Mayank-Shekhar Jha, Genevieve Dauphin-Tanguy, Belkacem Ould-Bouamama
      This paper describes a novel formalism for modelling uncertain system parameters and measurements, as interval models in a Bond Graph (BG) modelling framework. The main scientific interest remains in integrating the benefits of BG modelling technique and properties of Interval Analysis (IA), for efficient diagnosis of uncertain systems. Structural properties of Bond graphs in Linear Fractional transformation (BG-LFT) are exploited to model interval-valued uncertainties over a BG model in order to form an uncertain BG. The inherent causal properties are exploited to generate interval-valued fault indicators. Then, various properties of IA are used to generate point valued residual and interval-valued thresholds. The latter must contain the point valued residuals under nominal system functioning. A systematic procedure is proposed for passive-type fault detection method which is robust to uncertain system parameters and measurements. The viability of the method is shown through experimental study of a steam generator system. The limitations associated with existing fault detection method based on BG-LFT are alleviated by the proposed approach. Moreover, it is shown that proposed approach generalizes the BG-LFT method. This work forms the initial step towards integrating interval analysis based capabilities in BG framework for fault detection and health monitoring of uncertain systems.

      PubDate: 2017-12-13T02:45:14Z
       
  • Robust fulfillment of constraints in robot visual servoing
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Pau Muñoz-Benavent, Luis Gracia, J. Ernesto Solanes, Alicia Esparza, Josep Tornero
      In this work, an approach based on sliding mode ideas is proposed to satisfy constraints in robot visual servoing. In particular, different types of constraints are defined in order to: fulfill the visibility constraints (camera field-of-view and occlusions) for the image features of the detected object; to avoid exceeding the joint range limits and maximum joint speeds; and to avoid forbidden areas in the robot workspace. Moreover, another task with low-priority is considered to track the target object. The main advantages of the proposed approach are low computational cost, robustness and fully utilization of the allowed space for the constraints. The applicability and effectiveness of the proposed approach is demonstrated by simulation results for a simple 2D case and a complex 3D case study. Furthermore, the feasibility and robustness of the proposed approach is substantiated by experimental results using a conventional 6R industrial manipulator.

      PubDate: 2017-12-13T02:45:14Z
       
  • Analysis and design of time-deadbands for univariate alarm systems
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Muhammad Shahzad Afzal, Tongwen Chen, Ali Bandehkhoda, Iman Izadi
      Time-deadbands (or alarm latches) are popular alarm configuration methods used in industry to improve the alarm system performance. In this paper, time-deadband based configurations for the case of univariate alarm systems are analyzed. Mathematical models are developed based on Markov processes, and analytical expressions for performance indices (the false alarm rate, missed alarm rate, and expected detection delay) are derived. Systematic design procedures are also proposed, and the utility of the methods is illustrated through design examples.

      PubDate: 2017-12-13T02:45:14Z
       
  • Optimal online selection of type 1 diabetes-glucose metabolism models
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Berno J.E. Misgeld, Philipp G. Tenbrock, Eyal Dassau, Francis J. Doyle, Steffen Leonhardt
      We address an optimal experimental design (OED) procedure for the online selection of type-1-diabetes (T1D) mellitus glucose metabolistic models. A fully observable reduced-order nonlinear dynamic model is presented and subsequently parameterised for Göttingen Minipigs and patients, that were both subject to an automatic insulin delivery. A bank of continuous–discrete unscented Kalman filters (CDUKF) is designed and parameterised for Göttingen Minipigs and patients. Based on this filter bank of CDUKF, a novel online OED design procedure is developed, that is used to identify the correct parameter set out of several available sets for measured blood glucose concentrations. The procedure utilises forward model simulations to calculate optimal system inputs. This leads to the identification of the correct parameter set under arbitrary conditions. Results are presented for both subgroups.

      PubDate: 2017-12-13T02:45:14Z
       
  • An application of economic model predictive control to inventory
           management in hospitals
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): J.M. Maestre, M.I. Fernández, I. Jurado
      In this paper, we present experimental results from the application of model predictive control (MPC) to inventory management in a real hospital. In particular, the stock levels of ten different drugs that belong to the same laboratory have been controlled by using an MPC policy. The results obtained after four months show that the adopted approach outperforms the method employed by the hospital and reduces both the average stock levels and the work burden of the pharmacy department. This paper also paper presents some practical insights regarding the application of advanced control methods in this context.

      PubDate: 2017-12-13T02:45:14Z
       
  • Model-fusion-based online glucose concentration predictions in people with
           type 1 diabetes
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Xia Yu, Kamuran Turksoy, Mudassir Rashid, Jianyuan Feng, Nicole Hobbs, Iman Hajizadeh, Sediqeh Samadi, Mert Sevil, Caterina Lazaro, Zacharie Maloney, Elizabeth Littlejohn, Laurie Quinn, Ali Cinar
      Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.

      PubDate: 2017-12-13T02:45:14Z
       
  • High-accuracy robotized industrial assembly task control schema with force
           overshoots avoidance
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Loris Roveda, Nicola Pedrocchi, Manuel Beschi, Lorenzo Molinati Tosatti
      The presented paper proposes an analytical force overshoots free control architecture for standard industrial manipulators involved in high-accuracy industrial assembly tasks (i.e., with tight mounting tolerances). As in many industrial scenarios, the robot manipulates components through (compliant) external grippers and interacts with partially unknown compliant environments. In such a context, a force overshoot may result in task failures (e.g., gripper losses the component, component damages), representing a critical control issue. To face such problem, the proposed control architecture makes use of the force measurements as a feedback (obtained using a force/torque sensor at the robot end-effector) and of the estimation of the equivalent interacting elastic system stiffness (i.e., force sensor– compliant gripper–compliant environment equivalent stiffness) defining two control levels: (i) an internal impedance controller with inner position and orientation loop and (ii) an external impedance shaping force tracking controller. A theoretical analysis of the method has been performed. Then, the method has been experimentally validated in an industrial-like assembly task with tight mounting tolerances (i.e., H7/h6 mounting). A standard industrial robot (a Universal Robot UR 10 manipulator) has been used as a test-platform, equipped with an external force/torque sensor Robotiq FT 300 at the robot end-effector and with a Robotiq Adaptive Gripper C-Model to manipulate target components. ROS framework has been adopted to implement the proposed control architecture. Experimental results show the avoidance of force overshoots and the achieved target dynamic performance.

      PubDate: 2017-12-13T02:45:14Z
       
  • Practical dynamic matrix control for thermal power plant coordinated
           control
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Un-Chul Moon, Youngjun Lee, Kwang Y. Lee
      This paper proposes three practical strategies for the coordinated control (CC) of a thermal power plant using dynamic matrix control (DMC) that can be directly applied to industrial power plants. The three strategies are the replacement of conventional CC using DMC, the inclusion of disturbance variables, and a supplementary reference correction of the conventional CC. The performance during wide range operation of the three DMC–CCs is compared and discussed with the simulation results of a large-scale power plant model.

      PubDate: 2017-12-13T02:45:14Z
       
  • Iterative Pole–Zero model updating: A combined sensitivity approach
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): M. Dorosti, R.H.B. Fey, M.F. Heertjes, H. Nijmeijer
      A crucial step in the control of a weakly damped high precision motion system is having an accurate dynamic model of the system from actuators to sensors and to the unmeasured performance variables. A (reduced) Finite Element (FE) model may be a good candidate apart from the fact that it often does not sufficiently match with the real system especially when it comes to machine-to-machine variation. To improve the dynamic properties of the FE model toward the dynamic properties of a specific machine, an Iterative Pole–Zero (IPZ) model updating procedure is used that updates numerical poles and zeros of Frequency Response Functions (FRFs) toward measured poles and zeros, which can be extracted from the measured FRFs. It is assumed that in a practical situation, the model (physical) parameters that cause discrepancy with the real structure are unknown. Therefore, the updating parameters will be the eigenvalues of the stiffness and/or damping (sub)matrix. In this paper, an IPZ model updating is introduced which combines the sensitivity functions of both poles and zeros (with respect to the corresponding updating parameters) together with the cross sensitivity functions between poles and zeros. The procedure is verified first using simulated experiments of a pinned-sliding beam structure and then using non-collocated FRF measurement results from a cantilever beam setup.

      PubDate: 2017-12-13T02:45:14Z
       
  • Performance improvement of an NCS closed over the internet with an
           adaptive Smith Predictor
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Ana Paula Batista, Fábio G. Jota
      In this paper, the potential improvement in performance of an NCS (Networked Control System) subject to variable delays controlled by means of an adaptive system is analyzed. An adaptive Smith Predictor has been used to compensate the effects of varying delays measured on the network. The NCS, closed over the Internet, has been implemented using a platform called NCS-CMUF, composed of local and remote stations. The plant used to carry out the experimental tests is consisted of an optical oven in which the luminosity loop is the main controlled variable whose time constant lies in the range of milliseconds. This paper highlights the importance of synchronization between the clocks of the local and remote stations to perform consistent measurements of network delays, in order to provide an active compensation of the effects of delays. The results show the effectiveness of adaptive Smith Predictor in coping with different delays and the performance improvements achieved with the proposed scheme.

      PubDate: 2017-11-16T16:01:45Z
       
  • Computation of eco-driving cycles for Hybrid Electric Vehicles:
           Comparative analysis
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): D. Maamria, K. Gillet, G. Colin, Y. Chamaillard, C. Nouillant
      In this paper, the calculation of eco-driving cycles for a Hybrid Electric Vehicle (HEV), using Dynamic Programming (DP), is investigated from the complexity-solving method viewpoint. The study is based on a comparative analysis of four optimal control problems formulated using distinct levels of modeling. Starting with three state dynamics (vehicle position and speed, battery state-of-charge) and three control variables (engine and electric machine torque, gear-box ratio), the number of state variables is reduced to two in a first simplification. The other two simplifications are based on decoupling the optimization of the control variables into two steps: an eco-driving cycle is calculated assuming that the vehicle is propelled only by the engine. Then, knowing that the vehicle follows the eco-driving cycle calculated in the first step, an off-line energy management strategy (torque split) for an HEV is calculated to split the requested power at the wheels between the electric source and the engine. As is shown, the decreased complexity and the decoupling optimization lead to a sub-optimality in fuel economy while the computation time is noticeably reduced. Quantitative results are provided to assess these observations.

      PubDate: 2017-11-16T16:01:45Z
       
  • Parallel distributed compensation for improvement of level control in
           carbonization column for soda production
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Snejana Yordanova, Milen Slavov, Branimir Gueorguiev
      The liquid level control is essential in many production installations but the classic approaches often fail to ensure the desired performance. The reasons are the plant nonlinearity, the level oscillations and the plant model uncertainties. The aim of the present investigation is to improve the existing linear control of the level in the carbonization columns for soda ash production by employing fuzzy logic using parallel distributed compensation (PDC). The design of the PDC is based on a nonlinear Takagi–Sugeno–Kang (TSK) plant model which is derived via genetic algorithms optimization and validated using the data from the real time linear level control. The PDC control performs soft blending of the outputs of several parallel local linear controllers each developed for the local linear plant of the TSK model. The fuzzy rules are represented by ordinary logics conditions to enable the PDC programming and use by an industrial programmable logic controller. The PDC increases the dynamic accuracy in the level control and reduces the frequency of the control oscillations compared to the previous linear control thus prolonging the lifetime of the expensive pneumatic actuators used.
      Graphical abstract image

      PubDate: 2017-11-16T16:01:45Z
       
  • Applicability of available Li-ion battery degradation models for system
           and control algorithm design
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Xing Jin, Ashish Vora, Vaidehi Hoshing, Tridib Saha, Gregory Shaver, Oleg Wasynczuk, Subbarao Varigonda
      Within electrified vehicle powertrains, lithium-ion battery performance degrades with aging and usage, resulting in a loss in both energy and power capacity. As a result, models used for system design and control algorithm development would ideally capture the impact of those efforts on battery capacity degradation, be computationally efficient, and simple enough to be used for algorithm development. This paper provides an assessment of the state-of-the-art in lithium-ion battery degradation models, including accuracy, computational complexity, and amenability to control algorithm development. Various aging and degradation models have been studied in the literature, including physics-based electrochemical models, semi-empirical models, and empirical models. Some of these models have been validated with experimental data; however, comparisons of pre-existing degradation models across multiple experimental data sets have not been previously published. Three representative models, a 1-d electrochemical model (a combination of performance model and degradation model), a semi-empirical degradation model (the performance is predicted by an equivalent circuit model) and an empirical degradation model (the performance is predicted by an equivalent circuit model), are compared against four published experimental data sets for a 2.3-Ah commercial graphite/LiFePO 4 cell. Based on simulation results and comparisons to experimental data, the key differences in the aging factors captured by each of the models are summarized. The results show that the physics-based model is best able to capture results across all four representative data sets with an error less than 10%, but is 20 x slower than the empirical model, and 134 x slower than the semi-empirical model, making it unsuitable for powertrain system design and model-based algorithm development. Despite being computationally efficient, the semi-empirical and empirical models, when used under conditions that lie outside the calibration data set, exhibit up to 71% error in capacity loss prediction. Such models require expensive experimental data collection to recalibrate for every new application. Thus, in the author’s opinion, there exists a need for a physically-based model that generalizes well across operating conditions, is computationally efficient for model-based design, and simple enough for control algorithm development.

      PubDate: 2017-11-08T15:25:35Z
       
  • Constrained nonlinear filter for vehicle sideslip angle estimation with no
           a priori knowledge of tyre characteristics
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Salvatore Strano, Mario Terzo
      The vehicle sideslip angle is one of the most functional feedbacks for the actual control systems of vehicle dynamics. The measurement of the sideslip angle is expensive and unsuitable for common vehicles. Consequently, its estimation is nowadays an important task. This paper focuses on the vehicle sideslip angle estimation adopting a constrained unscented Kalman filter (CUKF) that takes into account state constrains during the estimation process. State boundaries are useful in real-world applications to prevent unphysical results and to improve the estimator robustness. The proposed technique fully takes into account the measurement noise and nonlinearities. A vehicle model with single track has been adopted for the design of the estimator. Simulations have been carried out and comparisons with the unscented Kalman filter (UKF) are illustrated. Performance of the estimators have been checked through the application to experimental data. The results show the goodness of the CUKF, able to give an estimate fully in accordance with the measurement. Moreover, the results show that the CUKF, due to the presence of the boundaries, outperforms the UKF.

      PubDate: 2017-11-08T15:25:35Z
       
  • Bringing probabilistic analysis capability from planning to operation
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Yousu Chen, Pavel Etingov, Erin Fitzhenry, Poorva Sharma, Tony Nguyen, Yuri Makarov, Mark Rice, Craig Allwardt, Steve Widergren
      The dynamic behavior of smart grid technologies requires the transition from a deterministic to a probabilistic control paradigm. This necessitates a smoother, better-integrated interplay between the functional roles of planning and operations to leverage the capabilities of probabilistic analysis in both realms. This paper presents two power system probabilistic analysis tools and how they are integrated into the GridOPTICS Software System (GOSS), a middleware platform facilitating deployment of new applications for the future power grid. Case study results show the developed tools provide better prediction of the power system balancing requirements, better transmission congestions management, and better system reliability.

      PubDate: 2017-11-08T15:25:35Z
       
  • Model predictive control for offset-free reference tracking of fractional
           order systems
    • Abstract: Publication date: February 2018
      Source:Control Engineering Practice, Volume 71
      Author(s): Sotiris Ntouskas, Haralambos Sarimveis, Pantelis Sopasakis
      In this paper an offset-free model predictive control scheme is presented for fractional-order systems using the Grünwald–Letnikov derivative. The infinite-history fractional-order system is approximated by a finite-dimensional state-space system and the modeling error is cast as a bounded disturbance term. Using a state observer, it is shown that the unknown disturbance at steady state can be reconstructed and modeling errors and other persistent disturbances can be attenuated. The effectiveness of the proposed controller–observer ensemble is demonstrated in the optimal administration of an anti-arrhythmic medicine with fractional-order pharmacokinetics.

      PubDate: 2017-11-08T15:25:35Z
       
  • Data-driven root-cause fault diagnosis for multivariate non-linear
           processes
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Bahador Rashidi, Dheeraj Sharan Singh, Qing Zhao
      In a majority of multivariate processes, propagating nature of malfunctions makes the fault diagnosis a challenging task. This paper presents a novel data-driven strategy for real-time root-cause fault diagnosis in multivariate (non-)linear processes by estimating the strength of causality using normalized transfer entropy (NTE) between measured process variables and variations of a residual signal. In this paper, a new framework for root-cause fault diagnosis applicable for multivariate nonlinear processes is proposed, which can reduce the necessary number of calculation for causality analysis among time-series. More specially, a new and fast symbolic dynamic-based normalized transfer entropy (SDNTE) technique is proposed to enable real-time application of transfer entropy, which has been considered as a burdensome approach for causality analysis. The concept of SDNTE is built upon principles of time-series symbolization, xD-Markov machine and Shannon entropy. This paper also introduces a new concept of joint xD-Markov machine to capture dynamic interactions between two time-series. The proposed root-cause fault diagnosis framework is applied on Tennessee Eastman process benchmark and its computational advantages are shown by comparing with conventional kernel PDF-based method. Moreover, the proposed strategy is applied to health monitoring of a big scale industry centrifuge to corroborates its effectiveness and feasibility in industrial applications.

      PubDate: 2017-11-08T15:25:35Z
       
  • Causal direction inference for network alarm analysis
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Yulai Zhang, Yuefeng Cen, Guiming Luo
      Automatic alarm analysis is important for network operation. Numerous alarms from different layers of a network may be caused by one single fault. Finding the correct causal direction between two sets of correlated alarms helps to locate the original fault correctly. Causal direction inference can be taken as a task of feature extraction. Generalized Gaussian Distribution (GGD) is used in this work to approximate the distributions of the observations and the unit entropy of GGD is extracted to determine the causal direction. Experiments of the novel method gives satisfactory results on the data from real networks.

      PubDate: 2017-11-08T15:25:35Z
       
  • Modeling for drivability and drivability improving control of HEV
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Christian Jauch, Santhosh Tamilarasan, Katherine Bovee, Levent Güvenc, Giorgio Rizzoni
      A hybrid electric vehicle has more than one energy source and switching between the two sources leads to significant drivability problem. Drivability is used to describe the comfort of driving a vehicle under a wide variety of operating conditions like tip-in, tip-out, and gear shifting. Vehicle drivability is one of the keystones of product quality and is refined aggressively to achieve product differentiation and market position. Specific aspects of drivability problems can be improved using modern control techniques. In this paper, a generic model of the driveline of a plug-in hybrid vehicle is developed. Drivability evaluation indices have been developed and were used for evaluating the drivability performance. Analysis and measurements of the actual components of this vehicle followed by parameter identification based on experimental data are used to obtain a validated version of the driveline model. A comparison of the driveline under consideration with changes in critical parameters is presented to demonstrate the effect of these parameters on drivability. The paper then focuses on improving the tip-in and tip-out drivability problem by proposing a control architecture consisting of an input shaping feedforward control filter and a feedback controller around an inner disturbance observer loop. The proposed controller is tested in both simulations and in experimental road tests and is shown to improve the drive quality of the vehicle significantly.

      PubDate: 2017-11-02T15:15:36Z
       
  • First principle based control oriented model of a gasoline engine
           including multi-cylinder dynamics
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Ahmed Yar, Aamer Iqbal Bhatti, Qadeer Ahmed
      The growing complexity of vehicle powertrain systems and stringent emissions requirements placed on these systems has necessitated the introduction of accurate and generalizable engine models that will be suitable for control and diagnostics. A first principle based control oriented model of a multi-cylinder gasoline engine is developed and shown to be also suitable for fault diagnosis. This model takes into cylinder-to-cylinder behavior and spatial orientation while maintaining a simple structure suitable for real time control. A model of the torque production mechanism is coupled with an analytical cylinder pressure model to capture the engine torque. The model of the torque production mechanism is derived using the Constrained Lagrangian Equation of Motion and simplified to a form suitable for integration in an overall engine model. The analytical cylinder pressure model is taken from literature and extended to a four cylinder engine. While it is common to model torque production subsystem as a continuously operating volumetric pump, the methods used in this work allow details including angular speed fluctuations of the crankshaft to be captured. In addition, the engine model is able to describe the dynamics of the system under faultless as well as faulty conditions, which is demonstrated for misfire. The proposed model is also leveraged for a novel fault tolerant control framework and is tuned and successfully validated for a 1 . 3 L four cylinder gasoline engine.

      PubDate: 2017-11-02T15:15:36Z
       
  • Periodic disturbance rejection to the Nyquist frequency and beyond
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Weili Yan, Chee Khiang Pang, Chunling Du
      In this paper, we address periodic disturbance rejection for sampled-data control systems, where the frequency of the disturbance can be beyond the Nyquist frequency of the limited measured plant output sampling rate. The disturbance effect on steady-state response of the fictitious fast-rate plant output including intersample information is obtained using discrete-time Fourier series. A sufficient condition for disturbance rejection together with a sufficient and necessary condition for perfect disturbance elimination are provided by employing the steady-state response. A simple but effective controller design procedure combining H ∞ loop shaping, Youla–Kucera parameterization, and gradient methods is provided to achieve the two conditions. The proposed approach is applied on vibration control beyond the Nyquist frequency in a commercial hard disk drive. The effectiveness of the proposed approach is verified by our simulation results showing disturbance rejection of  62 % and perfect disturbance elimination, as well as by our experimental results showing disturbance rejection of  54 % .

      PubDate: 2017-11-02T15:15:36Z
       
  • A decentralized control strategy for the coordination of AGV systems
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Maria Pia Fanti, Agostino M. Mangini, Giovanni Pedroncelli, Walter Ukovich
      This paper deals with the complex problem of controlling and coordinating Autonomous Guided Vehicles (AGV) by a decentralized approach. Each AGV selects its route by a consensus algorithm based on some Integer Linear Programming problem solutions. Moreover, the AGVs move inside a zone-controlled guidepath network and coordinate their movements by a decentralized protocol based on a zone-controlled approach, which guarantees the avoidance of deadlocks and collisions. The proposed decentralized strategy is applied to a guidepath network by means of a simulation software.

      PubDate: 2017-11-02T15:15:36Z
       
  • Closed-loop volume flow control algorithm for fast switching pneumatic
           valves with PWM signal
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Miha Pipan, Niko Herakovic
      In this paper, a closed-loop volume flow PWM control algorithm of fast switching pneumatic solenoid valves is studied on the basis of experimental results of fluid flow valve characteristics. Dynamic nonlinear behavior of fast switching valves is analyzed using state-of-the-art mass flow sensors. Minimal Pulse Width Modulation (PWM) pulse width and nonlinear flow characteristics depending on pulse width and pressure difference are observed. Based on experimental data, different approaches to mathematically describe correlation of volume flow, pressure difference and pulse width are given. Bilinear interpolation is found out to have the best correlation and is used to develop a closed-loop control algorithm. The algorithm was tested with controlling of Pneumatic Artificial Muscle (PAM) contraction/position with two fast switching valves and minimal PLC / microcontroller requirements were determined.

      PubDate: 2017-11-02T15:15:36Z
       
  • An MINLP model to support the movement and storage decisions of the Indian
           food grain supply chain
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): D.G. Mogale, Sri Krishna Kumar, Manoj Kumar Tiwari
      This paper addresses the novel three stage food grain distribution problem of Public Distribution System (PDS) in India which comprises of farmers, procurement centers, base silos and field silos. The Indian food grain supply chain consists of various activities such as procurement, storage, transportation and distribution of food grain. In order to curb transportation and storage losses of food grain, the Food Corporation of India (FCI) is moving towards the modernized bulk food grain supply chain system. This paper develops a Mixed Integer Non-Linear Programming (MINLP) model for planning the movement and storage of food grain from surplus states to deficit states considering the seasonal procurement, silo capacity, demand satisfaction and vehicle capacity constraints. The objective function of the model seeks to minimize the bulk food grain transportation, inventory holding, and operational cost. Therein, shipment cost contains the fixed and variable cost, inventory holding and operational cost considered at the procurement centers and base silos. The developed mathematical model is computationally complex in nature due to nonlinearity, the presence of numerous binary and integer variables along with a huge number of constraints, thus, it is very difficult to solve it using exact methods. Therefore, recently developed, Hybrid Particle-Chemical Reaction Optimization (HP-CRO) algorithm has been employed to solve the MINLP model. Different problem instances with growing complexities are solved using HP-CRO and the results are compared with basic Chemical Reaction Optimization (CRO) and Particle Swarm Optimization (PSO) algorithms. The results of computational experiments illustrate that the HP-CRO algorithm is competent enough to obtain the better quality solutions within reasonable computational time.

      PubDate: 2017-11-02T15:15:36Z
       
  • Soft-sensing with qualitative trend analysis for wastewater treatment
           plant control
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Christian M. Thürlimann, David J. Dürrenmatt, Kris Villez
      Ammonia control in municipal wastewater treatment plants typically requires maintenance-intensive instrumentation. A low maintenance alternative is sought for small- to medium-scale applications. To this end, a pH-based soft-sensor is proposed to detect ammonia peak load events. This soft-sensor is based on a newly developed technique for qualitative trend analysis and is combined with a rule-based controller. The use of qualitative trend analysis makes this soft-sensor tolerant towards sensor drifts and thereby reduces the maintenance effort. The method allows controlling any process in which relative changes in the measured output are informative about the system output.

      PubDate: 2017-11-02T15:15:36Z
       
  • Model-based control of exhaust heat recovery in a heavy-duty vehicle
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): D. Seitz, O. Gehring, C. Bunz, M. Brunschier, O. Sawodny
      For an exhaust heat recovery (EHR) system, this paper presents a model-based controller that is operated in a common automotive electronic control unit (ECU) and tested in a heavy-duty vehicle on the road. The EHR system is based on an Organic Rankine cycle (ORC) and improves the fuel efficiency. The sensitive vapor quality at the expander inlet is regulated by the pump using a 2-degree-of-freedom design. The feedforward part employs an inverse of a modified Moving-Boundary model, combined with online adaption of parameters. The feedback path utilizes gain-scheduling and LQR based on a Finite-Volume model. The controller performance is compared in simulation and experiment using a real ORC in a heavy-duty vehicle (HDV).

      PubDate: 2017-10-25T15:03:34Z
       
  • A virtual vertical reference concept for aided inertial navigation at the
           sea surface
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Torleiv H. Bryne, Robert H. Rogne, Thor I. Fossen, Tor A. Johansen
      When trying to capture the heaving motion of a marine vessel in waves, conventional position references such as global navigation satellite systems fall short in many cases. Because of design and satellite geometry, the vertical position measurement is typically inferior in both accuracy and precision compared to its horizontal counterparts. For aiding an inertial navigation system (INS), using conventional vertical position references may therefore be suboptimal. This article presents an alternative algorithm utilizing a virtual vertical reference (VVR) concept, based on the mean sea level, to aid the INS. The novelty of the algorithm arises where the VVR concept is augmented with an error model, used to improve the heave estimation performance. The motion of the vessel is estimated using an INS, employing a feedback-interconnected observer framework, based on nonlinear theory. The INS, using low-cost micro-electro-mechanical-system-based inertial measurement units, provides position, velocity and attitude in a dead-reckoning fashion, and is aided by a horizontal position reference, the VVR, and a compass to prevent the estimates from drifting. The estimation performance obtained with the observer structure is evaluated using Monte Carlo simulations and compared to preceding results. The algorithm is also validated experimentally and compared to an industry standard vertical reference unit using data collected on board an offshore vessel.

      PubDate: 2017-10-25T15:03:34Z
       
  • Sensitivity analysis and sensitivity-based design for linear alarm filters
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Ying Xiong, Yindi Jing, Tongwen Chen
      This paper conducts sensitivity analysis and sensitivity-based design for linear filter alarm monitoring systems. Based on a derivative-based local sensitivity measure, models are proposed to assess the sensitivity of the system detection errors to changes in the trip point and to uncertainties in the collected data. Then, analytical expressions are derived to quantitatively evaluate the sensitivity of a general linear alarm filter with unknown data distributions. Subsequently, a new sensitivity-based linear filter design method is formulated to minimize a weighted sum of the detection errors subject to upper bounds on the system sensitivities. Extensive simulations with both Gaussian and industrial data are conducted to verify the analytical results and to show trade-offs between the detection errors and sensitivities of linear filter alarm system.

      PubDate: 2017-10-25T15:03:34Z
       
  • Automated weighted outlier detection technique for multivariate data
    • Abstract: Publication date: January 2018
      Source:Control Engineering Practice, Volume 70
      Author(s): Suresh N. Thennadil, Mark Dewar, Craig Herdsman, Alison Nordon, Edo Becker
      In the chemical and petrochemical industries, spectroscopy-based online analysers are becoming common for process monitoring and control applications. A significant challenge in using these analysers as part of process monitoring and control loops is the large amount of personnel time required for calibration and maintenance of models which involve decision inputs such as whether an observation is an outlier, the number of latent variables in a model, type of pre-processing and when a calibration model has to be updated. Since no one measure works well for all applications, supervision by the process data analyst is required which invariably involves some level of subjectivity. In this paper, we focus on the detection of multivariate outliers in a calibration set. We propose a method which combines multiple outlier detection techniques to identify a set of outlying observations without operator input. Apart from the overall methodology, this work introduces several novelties. The system uses partial least squares (PLS) instead of principal component analysis (PCA) which is normally used for detecting multivariate outliers. A simple modification to the Mahalanobis distance was also proposed which appears to be more sensitive to outliers than the conventional Mahalanobis distance. The methodology also introduces the concept of a desirability function to enable automatic decision making based on multiple statistical measures for outlier detection. The methodology is demonstrated using Raman spectroscopy data collected from an industrial distillation process.

      PubDate: 2017-10-25T15:03:34Z
       
  • Model predictive control to two-stage stochastic dynamic economic dispatch
           problem
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Amru Alqurashi, Amir H. Etemadi, Amin Khodaei
      A multi-stage model predictive control approach is proposed to compensate the forecast error in a scenario-based two-stage stochastic dynamic economic dispatch problem through a feedback mechanism. Reformulating the problem as a finite moving-horizon optimal control problem, the proposed approach decelerates the growth of the number of scenarios by updating the system as uncertainties are gradually realized. Consequently, the computation time is reduced, and the problem is solved without the need for using scenario reduction techniques that compromise the accuracy of the solution. To exhibit the computational efficiency of the proposed approach, numerical experiments are conducted on the IEEE 118-bus system.

      PubDate: 2017-10-25T15:03:34Z
       
  • Optimal traction control for heavy-duty vehicles
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Pavel Osinenko, Stefan Streif
      Heavy-duty vehicles such as tractors, bulldozers, certain construction and municipal vehicles, soil millers, forestry machinery etc. have a high demand for propulsion force and consequently a high fuel consumption. The current work presents a traction control approach based on motion dynamics estimation for optimizing propulsion force and energy efficiency according to a user-defined strategy. Unscented Kalman filter augmented with a fuzzy-logic system for adaptive estimation is used as the state observer. Simulation case study with an electrically driven tractor is presented. The new method of traction control showed considerable improvement of balancing energy efficiency and propulsion force.

      PubDate: 2017-10-11T22:25:35Z
       
  • Self-tuning PID controllers in pursuit of plug and play capacity
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Jérôme Mendes, Luís Osório, Rui Araújo
      This work addresses the problem of controlling unknown and time varying plants for industrial applications. The concept of “plug-and-play” was pursued using control algorithms that auto-adapt their control parameters in order to control unknown and time-varying plants. Self Tuning Controllers (STC) with PID form were studied and tested on a real process setup. The setup is composed of two coupled DC motors and a variable load. Controllers’ performances were compared in order to distinguish which controllers perform better, which are easier to set up, which have a better initial response, and which enable faster reaction to plant variations and load disturbances.

      PubDate: 2017-10-03T21:02:56Z
       
  • Robust adaptive controller based on evolving linear model applied to a
           Ball-Handling mechanism
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Farzad F. Bigelow, Ahmad Kalhor
      The increasing complexity and permanent growth of real-world robotics formidable challenges demand that most control systems be intelligently adaptive to the parameters and structures of dynamics. This paper, therefore, discusses an extended sliding mode controller that is based on an evolving linear model (ELM) designed and implemented as a systematic approach to tackling the arms target angle tracking problem in the ball-handling system of a robot. Without any prior knowledge about the dynamics of the system other than its highest possible order, the dynamic orders and relative degrees of the system are practically derived. A novel online linearization technique based on the recursive least squares (RLS) method which keeps the output error of estimation in a relatively small bound is applied to identify the plant and to derive an adaptive-linear-regression (ALR) model of the system. Subsequently, having a model in which the number of constructing independent regressors varies over time, an extended sliding mode control strategy, established upon Lyapunov theory, is applied to the online-identifying ELM of the ball-handling system. In order to quantify the effectiveness of the proposed methodology, comparative analysis of the proposed strategy with well-established linear quadratic regulator (LQR) design and other suggested work on this topic, on the robustness of controllers, are performed in simulations. Ultimately, multifarious practical scenarios were designed, performed, and validated for the handling mechanism. The results clearly demonstrate the benefits and effectiveness of the design approaches in practice.

      PubDate: 2017-10-03T21:02:56Z
       
  • Robust position control design for a cylinder in mobile hydraulics
           applications
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): I Yung, Carlos Vázquez, Leonid B. Freidovich
      Automation of various agricultural tasks, which are nowadays routinely executed by operators of hydraulically actuated tractors equipped with front-end loaders, is an important open problem. The so-called self-leveling task is considered here, where the lifting and lowering motions of the loader are performed manually while the orientation of the tool must be adjusted automatically. The proposed controller is constituted by a proportional feedback, a disturbance compensator based on an observer and a relay controller. A model-based tuning procedure for the controller parameters is discussed and an implementation is validated experimentally on an industry-standard commercial set-up.

      PubDate: 2017-09-19T19:41:56Z
       
  • A unified anti-windup strategy for SISO discrete dead-time compensators
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Rodolfo C.C. Flesch, Julio E. Normey-Rico, Carlos A. Flesch
      This paper presents the development of a unified anti-windup strategy for dead-time compensators that copes with stable, integrating, and unstable single-input and single-output linear plants subject to sector-bounded and memoryless input constraints (such as magnitude or rate saturation, the most common cases in practical applications). A discrete-time Filtered Smith Predictor is used as the base control structure and in this specific case it is shown that stability conditions are related to the primary controller and fast model of the plant instead of the whole control structure. As the design is driven directly in the z -domain, implementation of the strategy using a digital controller is immediate. In addition, simulations and a practical experiment using a vapor-compression refrigeration system with time delay are provided and the results show that the strategy has good performance, close to the one presented by optimal model predictive control structures, with the great advantage of requiring no optimization problem to be numerically solved.

      PubDate: 2017-09-19T19:41:56Z
       
  • A systematic approach for airflow velocity control design in road tunnels
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Jan Šulc, Sigurd Skogestad
      This paper introduces a systematic approach to design and tune the airflow velocity control system for use during fire situations in road tunnels. The proposed approach is focused on road tunnels with a complex structure; long tunnels with connected ramps (entrances and exits), where the controller design can be challenging and time consuming. Such tunnels usually have many sections where a fire can be localized, and this makes the control task difficult. Our approach is based on a simplified one-dimensional simulation model of a tunnel, which includes all the important factors influencing the airflow dynamics of a tunnel. The proportional–integral (PI) controllers are tuned based on the Skogestad Internal Model Control (SIMC) method, which requires a simple model for the process dynamics. The case study is the airflow velocity control in the Blanka tunnel complex in Prague, Czech Republic, which is the largest city tunnel in Central Europe. The results of the paper show how to improve the control algorithm in real operation and how to use the proposed systematic approach for future tunnels.

      PubDate: 2017-09-19T19:41:56Z
       
  • Modeling and robust control of a twin wind turbines structure
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): I. Guenoune, F. Plestan, A. Chermitti, C. Evangelista
      The control of a new structure of twin wind turbines (TWT) is presented in this paper. This new concept includes two identical wind turbines ridden on the same tower, which can pivot face the wind with no additional actuator. The motion of the arms carrying the TWT is free. The control law based on sliding mode controller is designed to track the maximum power, by controlling the rotor speed of the TWT and the yaw rotation but without yaw actuator. Finally, performances of the proposed control strategy are compared to standard proportional integral controller, for several scenarios (time varying direction or magnitude of the wind, error on the inertia of the system, …).

      PubDate: 2017-09-13T19:35:53Z
       
  • Real-time feasible multi-objective optimization based nonlinear model
           predictive control of particle size and shape in a batch crystallization
           process
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Yankai Cao, David Acevedo, Zoltan K. Nagy, Carl D. Laird
      This paper presents nonlinear model predictive control (NMPC) and nonlinear moving horizon estimation (MHE) formulations for controlling the crystal size and shape distribution in a batch crystallization process. MHE is used to estimate unknown states and parameters prior to solving the NMPC problem. Combining these two formulations for a batch process, we obtain an expanding horizon estimation problem and a shrinking horizon model predictive control problem. The batch process has been modeled as a system of differential algebraic equations (DAEs) derived using the population balance model (PBM) and the method of moments. Therefore, the MHE and NMPC formulations lead to DAE-constrained optimization problems that are solved by discretizing the system using Radau collocation on finite elements and optimizing the resulting algebraic nonlinear problem using Ipopt. The performance of the NMPC–MHE approach is analyzed in terms of setpoint change, system noise, and model/plant mismatch, and it is shown to provide better setpoint tracking than an open-loop optimal control strategy. Furthermore, the combined solution time for the MHE and the NMPC formulations is well within the sampling interval, allowing for real world application of the control strategy.

      PubDate: 2017-09-07T19:26:40Z
       
  • Intermodal terminal planning by Petri Nets and Data Envelopment Analysis
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Graziana Cavone, Mariagrazia Dotoli, Nicola Epicoco, Carla Seatzu
      A procedure for planning and resources’ management in intermodal terminals is presented. It integrates Timed Petri Nets (TPNs) and Data Envelopment Analysis (DEA) and consists of three steps: the terminal modeling via TPNs to model the regular behavior; the evaluation of whether the current configuration may cope with increased freight flows; if not, the analysis by cross-efficiency DEA of alternative planning solutions. The procedure provides the decision maker with number, capacity, and schedule of resources to tackle the flows increase. The method is evaluated by a real case study, showing that integrating TPNs and DEA allows taking planning decisions under conflicting requirements.

      PubDate: 2017-09-07T19:26:40Z
       
 
 
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