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Journal Cover Control Engineering Practice
  [SJR: 1.354]   [H-I: 84]   [42 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0967-0661
   Published by Elsevier Homepage  [3043 journals]
  • 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
       
  • The Tracking Ratio Station
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): T. Hägglund
      In ratio control, the control objective is to keep the ratio between two signals, normally flow measurements, at a desired value in spite of variations in setpoints and load disturbances, and possible control signal saturations. This paper presents a new ratio control scheme, the Tracking Ratio Station, that manages to handle all these situations. It is also able to keep the ratio when one of the controllers takes a local setpoint or is switched to manual control. The Tracking Ratio Station determines the setpoints of the two flow loops so that the one with the largest control error follows the external setpoint, whereas the one with the smallest control error follows the process output of the opposite control loop. The paper illustrates the properties of the Tracking Ratio Station in terms of both simulations and industrial field tests.

      PubDate: 2017-10-11T22:25:35Z
       
  • Real-time control using Bayesian optimization: A case study in airborne
           wind energy systems
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Ali Baheri, Shamir Bin-Karim, Alireza Bafandeh, Christopher Vermillion
      This paper presents a framework by which a data-driven optimization technique known as Bayesian Optimization can be used for real-time optimal control. In particular, Bayesian Optimization is applied to the real-time altitude optimization of an Airborne Wind Energy (AWE) system, for the purpose of maximizing net energy production. Determining the optimal operating altitude of an AWE system is challenging, as the wind speed constantly varies with both time and altitude. Furthermore, without expensive auxiliary equipment, the wind speed is only measurable at the AWE system’s operating altitude. In this work, Gaussian Process modeling and Bayesian Optimization are used in real-time to optimize the AWE system’s operating altitude efficiently, without the use of auxiliary wind profiling equipment. Specifically, the underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent operating altitude. In the AWE application, context-dependent Bayesian Optimization is used to handle the time-varying nature of the wind shear profile (wind speed vs. altitude). Using real wind data, our method is validated against three baseline approaches. Our simulation results show that the Bayesian Optimization method is successful in significantly increasing power production over these baselines.

      PubDate: 2017-10-11T22:25:35Z
       
  • Optimization-based control of ventilation in a road tunnel complex
    • Abstract: Publication date: December 2017
      Source:Control Engineering Practice, Volume 69
      Author(s): Jan Šulc, Lukáš Ferkl, Jiří Cigler, Jan Pořízek
      This paper introduces a novel approach for controlling the operational ventilation in complex road tunnels. Basically, the control structure is feed-forward with an adaptive logic. The feed-forward part includes the static optimization which uses the mathematical model of airflow dynamics. The adaptive logic is based on the recursive least squares with exponential forgetting, which compensates deviations between the mathematical model and real measured data, and thus provides a feedback. During the standard operation of road tunnels, requirements to maintain indoor air quality (IAQ) and protect the surrounding area of a tunnel from pollution, are necessary. Moreover, reducing electricity costs is desirable, as the ventilation in road tunnels forms a significant part of electricity costs. The control scheme has been validated through the long-term evaluation of operation in the Blanka tunnel complex in Prague, Czech Republic, which is the largest city tunnel in Central Europe. The experimental validation of the proposed control scheme is the main contribution of the paper.

      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
       
  • Design and experimental validation of a physics-based oxygen storage —
           thermal model for three way catalyst including aging
    • Abstract: Publication date: November 2017
      Source:Control Engineering Practice, Volume 68
      Author(s): Stefano Sabatini, Simone Gelmini, Mark A. Hoffman, Simona Onori
      In this paper, a physics-based, oxygen storage-thermal model for a three way catalyst (TWC) is developed and experimentally validated. This model is then extended to account for aging impacts on the TWC. In order to identify the model parameters, a series of ad hoc experiments were designed to test the device over various engine operating conditions. Four TWCs of different ages were tested to investigate the effects of TWC aging on the oxygen storage dynamics. Results show that aging can be lumped within a single model parameter, referred to as oxygen storage capacity. Sensitivity analysis shows only negligible dependence of oxygen storage capacity on catalyst operating temperature. The comprehensive model is validated over real driving conditions for different catalyst ages. The developed model has the potential to enhance the design of optimization-control techniques for fuel consumption benefits and on-board diagnostics health measurement robustness.

      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
       
  • A local rational model approach for H∞ norm estimation: With application
           to an active vibration isolation system
    • Abstract: Publication date: November 2017
      Source:Control Engineering Practice, Volume 68
      Author(s): Egon Geerardyn, Tom Oomen
      Robust control design hinges on the availability of an accurate uncertainty model. The aim of this paper is to develop an approach for accurate uncertainty modeling. The proposed method is based on H ∞ -norm estimation, or peak amplitude. A new approach is developed that explicitly takes into account inter-grid frequency behavior while only requiring a reduced experiment time, modeling effort, and limited user intervention. In particular, the proposed method relies on local rational models. Experimental results on an active vibration isolation system confirm that the approach is able to handle lightly-damped systems with significantly less data compared to spectral estimation and local polynomial estimation techniques.

      PubDate: 2017-09-07T19:26:40Z
       
  • Variable Structure Feedback Construction Algorithm for controlling
           measurement-imploded leader–follower ground robots
    • Abstract: Publication date: November 2017
      Source:Control Engineering Practice, Volume 68
      Author(s): A. Sahraeekhanghah, M.R. Homaeinezhad
      This paper presents a systematic approach for decreasing the error growth of a formation system consisted of a leader and a number of followers which are open-loop in position measurements (position, linear velocity and relative distance to the leader). Using conventional algorithms to control such a system may lead to an unstable network with increasing consensus error. A 3-phase algorithm is proposed to address open-loop systems of the mentioned form. The algorithm is specifically beneficial to the case where linear velocity and position sensors get damaged. The main idea of the proposed algorithm is to use fundamental relations between feedbacks and construct some of them using the others which in case of car-like ground vehicles, angular sensors’ feedbacks are used to estimate linear feedbacks. The error and input mathematical relations are formulated using kinematic of car-like robots. The stability of the proposed method is proved using a quadratic CLF, and the numerical simulation results are presented to assess the effectiveness of the algorithm compared to a conventional open-loop form. The algorithm is also applied on a pair of ground car-like robots. The experimental results verify the conclusions based on simulation. Various simulation results show that the proposed algorithm has improved the divergence rate of follower’s path from leader’s, from 0.3 m s e c for the unit of velocity’s uncertainty to about zero.

      PubDate: 2017-09-07T19:26:40Z
       
  • Modified predictive control for both normal and LVRT operations of a
           Quasi-Z-Source Matrix Converter based WECS
    • Abstract: Publication date: November 2017
      Source:Control Engineering Practice, Volume 68
      Author(s): Mojtaba Alizadeh, Shokrollah Shokri Kojori
      A new Wind Energy Conversion System (WECS), a Permanent-Magnet Synchronous Generator (PMSG) fed by a Quasi-Z-Source Matrix Converter (qZSMC), is proposed. At the same time, the paper proposes a Modified Predictive Control (MPC) for both normal and Low Voltage Ride-Through (LVRT) operations of the proposed WECS. First, the qZSMC comprised of a Three-Phase Quasi-Z-Source Network (TPQZSN) and a Matrix Converter (MC) is analyzed in both time and frequency domains. The TPQZSN is modeled using state–space averaging method and its transfer functions are derived. The transfer functions are then analyzed using Bode plot, and eventually a guide to select the proper passive components of the qZSMC is rendered. A MPC is proposed to control grid-side quantities while the generator speed is controlled by shoot-through adjustment. Unlike the Traditional Predictive Control (TPC), the proposed MPC can be applied to z-source converters, while considerably reducing output current ripples. The proposed control system is further adapted to enhance LVRT capability of the proposed WECS. The performance of the proposed WECS is evaluated under both normal and abnormal working conditions, comparing it with the Traditional MC-based PMSG-WECS (TMC-WECS).

      PubDate: 2017-09-01T19:14:18Z
       
  • GPU-based optimal control for RWM feedback in tokamaks
    • Abstract: Publication date: November 2017
      Source:Control Engineering Practice, Volume 68
      Author(s): Mitchell Clement, Jeremy Hanson, Jim Bialek, Gerald Navratil
      The design and implementation of a Graphics Processing Unit (GPU) based Resistive Wall Mode (RWM) controller to perform feedback control on the RWM using Linear Quadratic Gaussian (LQG) control is reported herein. The control algorithm is based on a simplified DIII-D VALEN model. By using NVIDIA’s GPUDirect RDMA framework, the digitizer and output module are able to write and read directly to and from GPU memory, eliminating memory transfers between host and GPU. The system and algorithm was able to reduce plasma response excited by externally applied fields by 32% during development experiments.

      PubDate: 2017-09-01T19:14:18Z
       
  • Sensitivity of predictive controllers to parameter variation in five-phase
           induction motor drives
    • Abstract: Publication date: November 2017
      Source:Control Engineering Practice, Volume 68
      Author(s): Cristina Martín, Mario Bermúdez, Federico Barrero, Manuel R. Arahal, Xavier Kestelyn, Mario J. Durán
      Model predictive control techniques have been recently proposed as a viable control alternative for power converters and electrical drives. The good current tracking, flexible control design or reduced switching losses are some of the benefits that explain the recently increased attention on finite-control-set model predictive control. The performance of the predictive model of the drive, which is the core of the predictive control, highly depends on the parameters of the real system. In this context, most research works assume good agreement between electrical parameters of the predictive model and the real machine, on the basis of nominal values. Nevertheless, this is far from being a real assumption, where non-modeled variables (i.e. the temperature, the magnetic saturation or the deep-bar effect) produce a detuning effect between the real system and its model, which can harm the control performance. The influence of parameter variations on the predictive control has barely been investigated in recent research works, where only conventional three-phase power converter configurations and permanent magnet drives have been taken into account. However, there is a lack of knowledge when different technologies like induction machines or multiphase drives are considered. It is worth highlighting the interest of the industry in induction motors as a mature technology or in multiphase drives as a promising alternative in applications where high overall system reliability and reduction in the total power per phase are required. This paper attempts to fill this gap by examining the impact of parameters mismatch on the finite-control-set predictive control performance of a five-phase induction motor drive, one of the multiphase electromechanical conversion systems with greatest impact in the research community. An exhaustive experimental sensitivity analysis of the close loop system performance based on more than three hundred trials in a test bench is presented.

      PubDate: 2017-09-01T19:14:18Z
       
  • Probabilistic fault diagnosis method based on the combination of nest-loop
           fisher discriminant analysis and analysis of relative changes
    • Abstract: Publication date: November 2017
      Source:Control Engineering Practice, Volume 68
      Author(s): Yue Wang, Chunhui Zhao
      Bias of data location and increase in data variations are two typical disturbances, which in general, simultaneously exist in the fault process. Targeting their different characteristics, a nested-loop fisher discriminant analysis (NeLFDA) algorithm and relative changes (RC) algorithm are effectively combined for analyzing the fault characteristics. First, a prejudgment strategy is developed to evaluate the fault types and determine what changes are covered in the fault process. Two statistical indexes are defined, which conduct Monte Carlo based center fluctuation analysis and dissimilarity analysis respectively. Second, for the fault data containing those two faults simultaneously, a combined NeLFDA-RC algorithm is proposed for fault deviations modeling, which is termed as CNR-FD. Fault directions concerning bias of data location are extracted by the NeLFDA algorithm and then corresponding fault deviations are removed from the fault data. Then RC algorithm is performed on these fault data to extract directions concerning increase of data variations. These fault directions are used as reconstruction models to characterize each fault class. Particularly, the compromise between these two algorithms is determined by the Monte Carlo based center fluctuation analysis. For online applications, a probabilistic fault diagnosis strategy based on Bayes’ rule is performed to identify fault cause by discovering the right reconstruction models that can make the reconstructed monitoring statistics have the largest probabilities of belonging to normal condition. The motivation of the proposed algorithm is illustrated by a numerical case and the performance of the reconstruction models and the probabilistic fault diagnosis strategy are illustrated using pre-programmed faults from the Tennessee Eastman benchmark process and the real industrial process data from the cut-made process of cigarettes in some cigarette factory.

      PubDate: 2017-09-01T19:14:18Z
       
  • Finite-time control of underactuated spacecraft hovering
    • Abstract: Publication date: November 2017
      Source:Control Engineering Practice, Volume 68
      Author(s): Xu Huang, Ye Yan, Zherui Huang
      Finite-time controllers are proposed in this paper for underactuated spacecraft hovering in the absence of the radial or in-track thrust. The indirect method, which is generally adopted to solve the singularity problem in the conventional terminal sliding mode, is modified to ensure the continuity of the high-order time derivative of the sliding surface at the switch points. Rigorous proofs via the Lyapunov-based approaches verify the finite-time stability of the closed-loop system. By comparisons with the asymptotic controllers, the advantages of the finite-time ones in faster convergence rate and enhanced control precision have also been substantiated.

      PubDate: 2017-09-01T19:14:18Z
       
  • Improved PI neural network-based tension control for stranded wire helical
           springs manufacturing
    • Abstract: Publication date: October 2017
      Source:Control Engineering Practice, Volume 67
      Author(s): Qi Zhang, Shilong Wang, Anrui Zhang, Jie Zhou, Qing Liu
      During the winding process of stranded wire helical springs (SWHSs), uneven wire tension always results in high rejection rate and non-compliance service life of SWHSs. Combining the proportion integral neural network (PINN) with a simplified actuator model, this paper presents a new control scheme for the SWHS CNC machine to keep the wire tension uniform. The PINN is improved by introducing an error variance ratio, accounting for the interaction between wires, as a modifying factor in the second hidden layer. The actuator model is simplified based on the analysis of the dynamic characteristics of the actuator. The output value of the improved PINN is transferred into control voltage value by the simplified model. The tension of each wire is controlled by an improved PINN. In order to enhance the control performance, the network parameters are updated using the gradient-based back-propagation method. The validity and consistency of the improved PINN are verified by experiments. The results indicate that (1) the computation load is slight; (2) the rising time of the step response is within 1 s; (3) 89%-96% of tension deviation values of the wire 1 and wire 3 under different process parameters are within 10% of the reference tension value; (4) the standard deviation of the wire 2 with large disturbance is 8.24 N. Compared with other algorithms (incremental PI, multiple PIDNN, PI based particle swarm optimization), the control scheme based on the improved PINN has less computation load, faster response speed and better performance in the time-varying and nonlinear system with larger disturbance.

      PubDate: 2017-09-01T19:14:18Z
       
  • A novel data-based quality-related fault diagnosis scheme for fault
           detection and root cause diagnosis with application to hot strip mill
           process
    • Abstract: Publication date: October 2017
      Source:Control Engineering Practice, Volume 67
      Author(s): Liang Ma, Jie Dong, Kaixiang Peng, Kai Zhang
      In this paper, a new technology or solution of quality-related fault diagnosis is provided for hot strip mill process (HSMP). Different from traditional data-based fault diagnosis methods, the alternative approach is focused more on root cause diagnosis. The new scheme addresses the quality-related fault detection with the developed modified canonical variable analysis (MCVA) model, then the advantage of original generalized reconstruction based contribution (GRBC) is followed to identify the faulty variables. Meanwhile, a new transfer entropy (TE)-based causality analysis method is proposed for root cause diagnosis of quality-related faults. Finally, the whole proposed framework is practiced with real HSMP data, and the results demonstrate the usage and effectiveness of these approaches.

      PubDate: 2017-09-01T19:14:18Z
       
  • Cooperative modalities in robotic tele-rehabilitation using nonlinear
           bilateral impedance control
    • Abstract: Publication date: October 2017
      Source:Control Engineering Practice, Volume 67
      Author(s): Mojtaba Sharifi, Saeed Behzadipour, Hassan Salarieh, Mahdi Tavakoli
      A nonlinear model reference adaptive bilateral impedance controller is proposed that can accommodate various cooperative tele-rehabilitation modes for patient–therapist interaction using a multi-DOF tele-robotic system. In this controller, two reference impedance models are implemented for the master and slave robots using new model reference adaptive control laws for the nonlinear bilateral teleoperation system. “Hand-over-hand” and “adjustable-flexibility” are two modes of patient–therapist cooperation that are realized using the proposed strategy. The Lyapunov-based stability proof guarantees the patient’s and the therapist’s safety during the cooperation and interaction with robots, even in the presence of modeling uncertainties of the multi-DOF teleoperation system. The performance of the proposed bilateral impedance controller is experimentally investigated for upper-limb tele-rehabilitation in the two mentioned cooperation modes.

      PubDate: 2017-09-01T19:14:18Z
       
  • Advanced control of a continuous oscillatory flow crystalliser
    • Abstract: Publication date: October 2017
      Source:Control Engineering Practice, Volume 67
      Author(s): F. Tahir, K. Krzemieniewska-Nandwani, J. Mack, D. Lovett, H. Siddique, F. Mabbott, V. Raval, I. Houson, A. Florence
      This paper presents the application and challenges of achieving Model Predictive Control (MPC) on two continuous oscillatory baffled crystallisation reactors, delivering precise product quality control in the face of raw material fluctuations. A key advantage of MPC is that it effectively deals with multivariable interactions and constraints that appear within the continuous crystallisation process. Using a flexible real-time software package, a control scheme is proposed that incorporates three MPC blocks for controlling reactor cooling profile, API (Active Pharmaceutical Ingredient) concentration and crystal size distribution, respectively. Furthermore, the solution is customisable and transferable to different crystallisation reactors as well as various APIs.

      PubDate: 2017-09-01T19:14:18Z
       
  • Path-following control of small fixed-wing unmanned aircraft systems with
           H∞ type performance
    • Abstract: Publication date: October 2017
      Source:Control Engineering Practice, Volume 67
      Author(s): Devaprakash Muniraj, Mark C. Palframan, Kyle T. Guthrie, Mazen Farhood
      The focus of this paper is on the design of path-following controllers for a small fixed-wing unmanned aircraft system (UAS) using the H ∞ robust control framework. The robust controllers are synthesized based on a lumped path-following and UAS dynamics formulation, effectively combining the six degree-of-freedom aircraft dynamics with the established parallel transport frame virtual vehicle dynamics. Two path-following controllers with a conventional cascaded architecture consisting of an outer guidance loop and an inner stabilization loop are also considered as points of reference. The robustness and performance of these controllers are tested in a rigorous MATLAB simulation environment that includes steady winds, turbulence, measurement noise, and time delays. Finally, flight experiments are conducted on a small fixed-wing UAS platform, and the controllers are compared in terms of tracking performance, control effort, and ease of implementation.

      PubDate: 2017-09-01T19:14:18Z
       
  • Contents list
    • Abstract: Publication date: October 2017
      Source:Control Engineering Practice, Volume 67


      PubDate: 2017-09-01T19:14:18Z
       
  • Vehicle sideslip estimation: A kinematic based approach
    • Abstract: Publication date: October 2017
      Source:Control Engineering Practice, Volume 67
      Author(s): Donald Selmanaj, Matteo Corno, Giulio Panzani, Sergio M. Savaresi
      This paper deals with vehicle sideslip angle estimation. The paper introduces an industrially amenable kinematic-based approach that does not need tire–road friction parameters or other dynamical properties of the vehicle. The convergence of the estimate is improved by the introduction of a heuristic based on readily available inertial measurements. The method is tested on a vast collection of tests performed in different conditions, showing a satisfactory behavior despite not using any information on the road friction. The extensive experimental validation confirms that the estimate is robust to a wide range of driving scenarios.

      PubDate: 2017-08-03T16:58:10Z
       
  • Secure and private control using semi-homomorphic encryption
    • Abstract: Publication date: October 2017
      Source:Control Engineering Practice, Volume 67
      Author(s): Farhad Farokhi, Iman Shames, Nathan Batterham
      Networked control systems with encrypted sensors measurements is considered. Semi-homomorphic encryption, specifically the Paillier encryption, is used so that the controller can perform the required computation on the encrypted data. Conditions on the parameters of the encryption technique are provided that guarantee the stability and the performance of the closed-loop system. The results are subsequently extended Laplacian based distributed systems, such as formation-seeking algorithms. It is shown that the problem of figuring out the state measurements of the neighbouring agents of a compromised agent upon using the proposed algorithm is numerically intractable.

      PubDate: 2017-08-03T16:58:10Z
       
  • Dominant trend based logistic regression for fault diagnosis in
           nonstationary processes
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Jun Shang, Maoyin Chen, Hongquan Ji, Donghua Zhou, Haifeng Zhang, Mingliang Li
      This paper presents a fault diagnosis method called dominant trend based logistic regression (DTLR) for monitoring nonstationary processes. Different from conventional sample-wise diagnosis approaches, it uses sliding windows to collect process data and extract dominant trend features. After data preprocessing via robust sparse representation, the feature vector reflecting variation trend is obtained by solving a convex optimization problem, i.e., dominant trend extraction (DTE). Then the ℓ 2 -norm of the dominant trend vector is used as a detection index to quantify the dissimilarity between normal and abnormal conditions. Once it exceeds the control limit, the feature vector is used to train the weight vector of logistic regression. The fault type can be determined as the class with the maximum conditional probability. With trend information, DTLR can effectively detect and isolate faults in nonstationary processes. Simulations on a synthetic nonstationary dynamic process, a nonstationary continuous stirred tank reactor (CSTR), and the real data of a blast furnace iron-making process illustrate superior monitoring and isolation performance of DTLR, compared with conventional methods.

      PubDate: 2017-08-03T16:58:10Z
       
  • FIR filter-based online jerk-constrained trajectory generation
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Pierre Besset, Richard Béarée
      In the context of human–robot manipulation interaction for service or industrial robotics, the robot controller must be able to quickly react to unpredictable events in dynamic environments. In this paper, a FIR filter-based trajectory generation methodology is presented, combining the simplicity of the analytic second-order trajectory generation, i.e. acceleration-limited trajectory, with the flexibility and computational efficiency of FIR filtering, to generate on the fly smooth jerk-constrained trajectories. The proposed methodology can generate synchronized (fixed-time) and time-optimal jerk-limited trajectories from arbitrary initial velocity and acceleration conditions within 20 microsecond. Other jerk-constrained trajectories such as jerk-time fixed trajectories, which are particularly suitable for vibration reduction, can be easily generated. Experimental validations carried out on a seven axis Kuka LBR iiwa are presented.

      PubDate: 2017-08-03T16:58:10Z
       
  • A quadratic boundedness approach to robust DC motor fault estimation
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Mariusz Buciakowski, Marcin Witczak, Marcin Mrugalski, Didier Theilliol
      In the paper, a novel methodology of actuator fault estimation for linear discrete-time systems is proposed. To solve such a challenging problem, a quadratic boundedness approach is used to guarantee the convergence of the proposed state and actuator fault estimation method. In the proposed methodology, the robustness is achieved through the unknown input decoupling while an unappealing effect of the undecoupled disturbances is minimized. Moreover, the developed approach enables to obtain a feasible set of joint system state and fault estimation error. Based on this knowledge, a novel methodology of calculating the uncertainty intervals of the system state and actuator fault is proposed. The illustrative part of the paper presents results obtained for the laboratory DC servo-motor and compares the proposed approach with two alternative methods. Based on this real-data example, the efficiency of the developed methodology is clearly exposed.

      PubDate: 2017-08-03T16:58:10Z
       
  • UAS based Li-ion battery model parameters estimation
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): D. Ali, S. Mukhopadhyay, H. Rehman, A. Khurram
      Estimation of Lithium-ion (Li-ion) battery model parameters is key for accurately determining battery state of charge (SOC). Estimating these parameters requires substantial experimental effort. This work reduces the experimentation required, by using universal adaptive stabilization (UAS) for estimating parameters appearing in battery model state equations. Accuracy of estimated model parameters is verified by comparing the estimated and measured battery terminal voltage. SOC obtained using the estimated model parameters, and open circuit EMF vs SOC curve, captures effects due to discharge currents of small magnitude, which Coulomb counting, well known filtering methods ignore. Rigorous mathematical analysis supports the experimental results presented.

      PubDate: 2017-07-23T14:15:10Z
       
  • Inverse dynamics based robust control method for position commanded servo
           actuators in robot manipulators
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Nabanita Adhikary, Chitralekha Mahanta
      In this paper, a simple torque to position conversion method is proposed for position commanded servo actuators used in robot manipulators. The torque to position conversion is based on the low level controller of the servomotor. The proposed conversion law is combined with a backstepping sliding mode control method to realize a robust dynamic controller. The proposed torque based method can control a servomotor which can otherwise be operated only through position inputs. This method facilitates dynamic control for position controlled servomotors and it can be extended to position commanded robotic manipulators also. Simulation and experimental studies are conducted to validate the proposed torque to position conversion based robust control method.

      PubDate: 2017-07-23T14:15:10Z
       
  • Complete dynamic model of the Twin Rotor MIMO System (TRMS) with
           experimental validation
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Azamat Tastemirov, Andrea Lecchini-Visintini, Rafael M. Morales-Viviescas
      In this paper we develop a complete dynamic model of the Twin Rotor MIMO System (TRMS) using the Euler–Lagrange method. Our model improves upon the model provided by the manufacturer in the user manual and upon previous models of the TRMS which can be found in the literature. The complete procedure for the model parameters’ estimation and validation is illustrated.

      PubDate: 2017-07-12T12:42:45Z
       
  • Fault-tolerant model predictive control of a direct methanol-fuel cell
           system with actuator faults
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): R. Keller, S.X. Ding, M. Müller, D. Stolten
      This paper investigates fault tolerant model predictive control (MPC) of a direct methanol fuel cell (DMFC) system with several faults in the methanol feeding pump. An active FTMPC strategy with a hierarchal structural design is developed. The focus here is on fault detection and isolation (FDI) and the implementation of fault-tolerant strategies within the control algorithm. To this end, a model-based FDI scheme with virtual sensors is first developed by means of the real-time diagnosis of fault occurrence during operation. Thereby, several faults in the methanol pump are characterized and the information integrated into the MPC algorithm in each fault case. Strategies are presented to reconfigure the active fault-tolerant MPC to keep the DMFC system stable in case of a feeding failure. Moreover, economic, stability and lifetime characteristics are also integrated into the active fault-tolerant MPC. The proposed FDI and FTMPC scheme is tested experimentally in a DMFC test rig with a 5-cell DMFC stack to demonstrate the effectiveness and robustness of the designed approach. Several fault scenarios with the FTMPC are shown. Particularly in the case of fuel cells, fault tolerance is necessary to meet the goals of long-lasting system stability and efficiency.

      PubDate: 2017-07-12T12:42:45Z
       
  • Further results on nonlinear tracking control and parameter estimation for
           induction motors
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): C.M. Verrelli, P. Tomei, E. Lorenzani, R. Fornari, F. Immovilli
      The original contribution of this paper, which concerns induction motors with uncertain constant load torque and rotor/stator resistances, is twofold. The first innovative contribution relies on the experimental analysis of the latest theoretically-based sensorless/output feedback solutions to the problem of tracking rotor speed and flux modulus reference signals with the simultaneous estimation of the uncertain parameters. The second novel contribution is constituted by the proof of existence for a new adaptive local flux observer from rotor speed and stator currents/voltages, which, in its full-order or reduced-order-like versions, involves neither over-parameterizations nor non-a priori verifiable first order stator resistance identifiability conditions at steady-state.

      PubDate: 2017-07-12T12:42:45Z
       
  • Experimental validation of an active heave compensation system:
           Estimation, prediction and control
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): M. Richter, S. Schaut, D. Walser, K. Schneider, O. Sawodny
      This work presents a comprehensive active heave compensation (AHC) approach proposing estimation, prediction and control methods. The estimation concept covers the estimation of the attitude using sensor fusion as well as the estimation of heave which is obtained by applying adaptive filtering methods. Moreover, a prediction approach based on a Levinson recursive least squares (RLS) algorithm is proposed. The actuation concept consists of a model predictive trajectory planner and a model-based Two-Degree-of-Freedom (2-DOF) controller. It is based on a model of the hydraulically driven compensation winch. The overall compensation performance as well as the estimation and prediction accuracy are evaluated using a full-scale AHC test bench.

      PubDate: 2017-07-03T08:31:21Z
       
  • Robust control for disturbed buck converters based on two GPI observers
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Zuo Wang, Shihua Li, Junxiao Wang, Qi Li
      The output voltage tracking problem of a disturbed buck converter is investigated in this paper via a GPI observer-based approach. Control performances of the converters are always influenced by undesirable effects of disturbances. These disturbances usually are of time-varying forms. Conventional backstepping schemes cannot achieve satisfactory performances in handling time-varying disturbances. By adding integral actions into the controller, only the slow-varying disturbances are asymptotically compensated in this way. In this paper, two GPI observers are constructed to estimate the lumped time-varying disturbances. By introducing the disturbance estimations into the design process, a composite GPI observer-based robust control scheme is developed. The proposed algorithm performs a promising disturbance rejection ability. Rigorous stability is guaranteed. Experimental results in the presence of time-varying disturbances are illustrated to show the feasibility of the proposed composite method.

      PubDate: 2017-07-03T08:31:21Z
       
  • Control of technological and production processes as distributed parameter
           systems based on advanced numerical modeling
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): G. Hulkó, C. Belavý, K. Ondrejkovič, L. Bartalský, M. Bartko
      The paper describes some practical control problems of technological and production processes as nonlinear distributed parameter systems. These are solved based on advanced numerical modeling in virtual software environments offered for the numerical dynamic analysis of technological and production processes with co-simulations. The controlled systems are interpreted as nonlinear lumped input and distributed parameter output systems. Synthesis of control in space relation is solved by approximation methods in temporal relation by methods of control of lumped parameter systems. Some results are demonstrated by the control of the secondary cooling in the continuous casting of steel, based on a software sensor. Furthermore, the control of a casting die preheating process is introduced in this framework using a programmable logic controller (PLC).

      PubDate: 2017-07-03T08:31:21Z
       
  • Application of Valuation-Based Systems for the availability assessment of
           systems under uncertainty
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Siqi Qiu, Mohamed Sallak, Walter Schön, Zohra Cherfi-Boulanger
      The aim of the paper is twofold. First, it proposes an original application of the Valuation-Based System (VBS) for the availability assessment of systems under uncertainty in a time-varying fashion. Uncertainties related to failure data of components (data uncertainty) and the system structure (model uncertainty) are analysed in the proposed model. Second, it proposes the application of the VBS for the availability assessment of the European Rail Traffic Management System (ERTMS) Level 2 under uncertainty according to the railway dependability standards. The originality of this work lies in the application of the VBS for the availability assessment of systems under data and model uncertainties, and the proposition of a temporal VBS to evaluate the instantaneous system availability.

      PubDate: 2017-07-03T08:31:21Z
       
  • Fault detection and diagnosis in a cement rotary kiln using PCA with
           EWMA-based adaptive threshold monitoring scheme
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Azzeddine Bakdi, Abdelmalek Kouadri, Abderazak Bensmail
      This paper presents main results of fault detection and diagnosis in a cement manufacturing plant using a new monitoring scheme. The scheme is based on multivariate statistical analysis and an adaptive threshold strategy. The process is statistically modeled using Principle Component Analysis (PCA). Instead of the conventional fixed control limits, adaptive thresholds are used to evaluate the common T 2 and Q statistics as faults indicators. The adaptive thresholds are computed and updated using a modified Exponentially Weighted Moving Average (EWMA) chart. These techniques are merged together to construct a novel monitoring scheme whose effectiveness is demonstrated using involuntary real fault of a cement plant process and some simulated faulty cases.

      PubDate: 2017-07-03T08:31:21Z
       
  • Battery state of health monitoring by estimation of the number of cyclable
           Li-ions
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Xin Zhou, Jeffrey L. Stein, Tulga Ersal
      This paper introduces a method to monitor battery state of health (SOH) by estimating the number of cyclable Li-ions, a health-relevant electrochemical variable. SOH monitoring is critical to battery management in balancing the trade-off between maximizing system performance and minimizing battery degradation. The decrease of cyclable Li-ions indicates the effect on the SOH of degradation mechanisms that consume cyclable Li-ions. The unavailability of the number of cyclable Li-ions through non-invasive measurements makes its estimation necessary for in-situ SOH monitoring. In this paper, the extended Kalman filter (EKF) is used to estimate the number of cyclable Li-ions as an unknown battery parameter. The single particle model (SPM), a simplified battery electrochemical model, is used as the model in the EKF to achieve a computational complexity suitable for on-line estimation. Simulations are performed under typical electric vehicle current trajectories using an example parameter set for a hybrid-electric-vehicle battery. In the simulations, the battery is represented by the Doyle–Fuller–Newman (DFN) model, an electrochemical model with higher fidelity than the SPM. To comply with the practice, instead of using the same parameters as the DFN model in the SPM, parameterization of the SPM is performed before estimation of the number of cyclable Li-ions. The simulations show high estimation accuracy of the number of cyclable Li-ions using the EKF, even with the structural and parametric differences between the DFN model and the SPM, under both the ideal conditions and various non-ideal conditions (i.e., SOC estimation error, additional modeling error, and measurement noise).

      PubDate: 2017-07-03T08:31:21Z
       
  • Variable frequency resonant controller for load reduction in wind turbines
    • Abstract: Publication date: September 2017
      Source:Control Engineering Practice, Volume 66
      Author(s): Rafael S. Castro, Aurélio T. Salton, Jeferson V. Flores, Michel Kinnaert, Daniel F. Coutinho
      While most loads on wind turbines are originated from wind speed fluctuations, they show a periodic nature with a time-varying frequency proportional to the turbine rotation. This paper exploits this relation and proposes a modified Resonant Controller able to attenuate these frequency-varying periodic disturbances. The resulting controller is designed for both partial and full load wind speed conditions, therefore, it is able to reject periodic loads even when the wind turbine system is subject to changes in the operating rotation speed. Furthermore, a novel piecewise linear representation of the system is presented allowing a systematic design procedure, based on Linear Matrix Inequalities, in order to compute the control parameters. Simulation results in a 2 . 5 MW large scale three-bladed wind turbine illustrate the proposed method, which is able to reduce the root mean value of blade load up to 12 times when compared to a traditional LPV controller.

      PubDate: 2017-07-03T08:31:21Z
       
 
 
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