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OPTIMAL SELF TUNING NEURAL NETWORK controlLER DESIGN

IFAC Proceedings Volumes 2005年第1期38卷 384-389页

作者： Ladislav Körösi Štefan Kozák Department of Automatic Control Systems Faculty of Electrical Engineering and Information Technology SUT Ilkovičova 3 812 19 Bratislava Slovak Republic

The proposed paper deals with modeling and control of continuous-time processes using artificial neural network with orthogonal activation functions, applicable for real-time control. A genetic algorithm has been used to find the optimal neural structure for on-line identification with the best learning algorithm. A moving prediction horizon in the control algorithm found by genetic algorithm has been compared with a constant prediction horizon. The proposed algorithms were verified on practical control problem and have proved a good performance.

关键词： neural-network models neural control optimal control on-line control genetic algorithms

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Predictive Fuzzy control Application

Predictive Fuzzy Control Application

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Eurocon

作者： J. Paulusova S. Kozak Department of Automatic Control Systems Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia

In this paper a robust predictive fuzzy control (PFC) methods for a nonlinear plant is addressed, proposed and tested. The paper consisting of theoretical and practical parts offers a new approach for intelligent fuzzy robust control design and its successful application. The structure of the fuzzy controller, online model identification and optimal performance index are analyzed, described and verified. The proposed PFC is demonstrated on two examples which one is applied to control of the concentration in the chemical reactor by manipulating its flow rate. The obtained simulation results show that the proposed algorithm is applicable for effective control of highly nonlinear processes that operate over a wide range of working space

关键词： Fuzzy control Predictive control Robust control Three-term control Fuzzy logic Performance analysis Chemical reactors Electrical equipment industry Industrial control Process control

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Robust genetic algorithm approach to modelling and control

Robust genetic algorithm approach to modelling and control

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IEEE International Symposium on Intelligent Signal Processing

作者： S. Kozak R. Memersheimer A. Kozakova Department of Automatic Control Systems Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia

The paper deals with robust intelligent control of linear dynamical systems using algebraic polynomial theory in combination with the genetic algorithms (GA). It shows that the conventional robust polynomial synthesis approach can be successfully modified so as to improve performance by applying genetic algorithms for tuning controller parameters. A general algorithm has been developed for optimal polynomial controller tuning that enables to generate optimal and robust control actions for both SISO and MIMO systems. Moreover, the proposed methodology guarantees finding global optimum and enables achieving a higher performance compared with the conventional approaches. The proposed robust intelligent algorithm involving the intelligent searching procedure has been tested on a case study (control of a servo system with a changing momentum of inertia). Obtained results verify the possibility to improve the performance using combination of a polynomial algebraic controller and a genetic algorithm.

关键词： Robust control Genetic algorithms Polynomials control systems Intelligent control control system synthesis Optimal control MIMO System testing Servomechanisms

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Model-free intelligent control using reinforcement learning and temporal abstraction-applied to pH control

Model-free intelligent control using reinforcement learning ...

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作者： Syafiie, S. Tadeo, F. Martinez, E. Department of Systems Engineering and Automatic Control Science Faculty University of Valladolid Prado de La Magdalena s/n 47011 Valladolid Spain Consejo Nacional de Investigaciones Científicas y Técnicas Avellaneda 3657 3000 Santa Fe Argentina

ISBN: (纸本)008045108X

This article presents a solution to pH control based on model-free intelligent control (MFIC) using reinforcement learning. This control technique is proposed because the algorithm gives a general solution for acid-base system, yet simple enough for its implementation in existing control hardware. in standard reinforcement learning, the interaction between an agent and the environment is based on a fixed time scale: during learning, the agent can select several primitive actions depending on the system state. A novel solution is presented, using multistep actions (MSA): actions on multiple time scales consist of several identical primitive actions. This solves the problem of determining a suitable fixed time scale to select control actions so as to trade off accuracy in control against learning complexity. The application of multi-step actions on a simulated pH process shows that the proposed MFIC learns to control adequately the neutralization process. Copyright © 2005 IFAC.

关键词： Process control

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PREDICTIVE control OF A PROCESS WITH VARIABLE DEAD-TIME

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IFAC Proceedings Volumes 2005年第1期38卷 309-314页

作者： Smaranda Cristea César de Prada Robin de Keyser Department of Systems Engineering and Automatic Control Faculty of Sciences c/ Real de Burgos s/n University of Valladolid Spain fax: +34 983 423161 tel: +34 983 423164 Department of Control Engineering & Automation University of Ghent Technologiepark-Zwijnaarde 9 B-9052 GHENT Belgium

This paper deals with the control of variable-delay processes, where the delay depends on the value of the manipulated variable, which results in a non-linear system difficult to control. As a reference process, the case of a heated tank where the controlled variable is the liquid temperature and the placement of the sensor introduce a transport delay in the control loop, has been considered. This challenging problem is approached from the perspective of predictive control, using the non-linear EPSAC controller.

关键词： Variable dead-time Predictive control Nonlinear control Delay systems

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IMPROVEMENT OF FAULT DETECTION METHOD FOR NONLINEAR BLACK-BOX systems BASED ON MULTI-FORM QUASI-ARMAX MODELING

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IFAC Proceedings Volumes 2005年第1期38卷 101-106页

作者： K. Kumamaru K. Inoue F. Tsubouchi T. Söderström Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Kawazu 680-4 Iizuka 820-8502 Fukuoka Japan Department of Automatic Control and Systems Analysis Institute of Technology Uppsala University P.O.Box 27 Uppsala Sweden

This paper is concerned with improvement of the KDI-based fault detection method so far developed by authors for nonlinear black-box systems. When modeling the system, Quasi-ARMAX model with multi-model structure is used. A fault due to unexpected change in system parameters will appear as the change of identified model. Kullback discrimination Information (KDI) can then be used as the fault detection index to evaluate the distortion in identified model. Several schemes to improve the fault detection performance are proposed, as well as the realization of a kind of fault isolation function based on a recognition approach in the model parameter space. The effectiveness of the method is verified through simulation studies on the ship propulsion system constructed for benchmark test.

关键词： Fault detection Kullback discrimination information non-linear system parameter estimation quasi-ARMAX model ship propulsion system

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Non-linear Predictive control for a Distillation Column

Non-linear Predictive Control for a Distillation Column

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IEEE Conference on Decision and control

作者： A. Rueda S. Cristea C. de Prada R. De Keyser Department of Systems Engineering and Automatic Control Faculty of Sciences c/Real de Burgos sln University of Valladolid Spain EeSA-Department of Electrical Energy Systems and Automation Ghent University Gent Belgium

ISBN: (纸本)0780395670

In this paper an alternative approach to non-linear predictive control is presented. It is based on iterative linearisation of the model response so that the same closed loop responses as in the pure non-linear approach are obtained but with reduced computation times and more efficient optimisation tools. The method is applied to a high purity distillation column and some results are presented showing the behaviour of the proposed algorithm.

关键词： Predictive control Distillation equipment Iterative methods Industrial control Constraint optimization Iterative algorithms Electrical equipment industry Sampling methods Quadratic programming Pressure control

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NEURO-FUZZY control OF A PH PLANT

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IFAC Proceedings Volumes 2005年第1期38卷 195-200页

作者： M.J. Fuente G.I. Sainz M. Alonso A. Aguado Department of Systems Engineering and Control. Faculty of Science University of Valladolid. Prado de la Magdalena s/n 47011 Valladolid Spain ETSII. University of Valladolid. Paseo del Cauce s/n 47011 Valladolid Spain Department of Automatic Control ICIMAF Cuba

This paper studies the control of a pH process by using a neuro fuzzy controller with gain scheduling. As the process to be controlled is highly non-linear the PI-type fuzzy controller that will be used generally is not able to control the system adequately. For this, a very simple feedforward neural network trained on-line, is put at the output of the PI-type fuzzy controller in order to calculate the gain of the controller. This neuro-fuzzy regulator has been tested in real-time on a bench plant. On-line results show that the designed control system allows the plant to operate in a range of pH values, despite perturbations and variations of the plant parameters, obtaining good performance at the desired workings points.

关键词： Fuzzy controller neural networks Nonlinear process control real pH plant gain scheduling

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Mixed H2/H∞ and robust control of differential linear repetitive processes

Mixed H2/H∞ and robust control of differential linear repet...

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IEEE Conference on Decision and control

作者： W. Paszke K. Gakowski E. Rogers A. Kummert D.H. Owens Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland School of Electronics and Computer Science University of Southampton Southampton UK Faculty of Electrical University of Wuppertal Wuppertal Germany Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

Repetitive processes are a distinct class of 2D systems (*** propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direct extension of existing techniques from either standard (termed 1D here) or 2D systems theory. Here we give new results on the design of physically based control laws and, in particular, the first results on a mixed H 2 /H ∞ approach and on H 2 control in the presence of uncertainty in the process model.

关键词： Robust control control systems Linear systems Uncertainty Writing Disk drives Process control Iterative algorithms control engineering computing Computer science

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MODEL-FREE INTELLIGENT control USING REINFORCEMENT LEARNING AND TEMPORAL ABSTRACTION-APPLIED TO pH control

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IFAC Proceedings Volumes 2005年第1期38卷 127-132页

作者： S. Syafiie F. Tadeo E. Martinez Department of Systems Engineering and Automatic Control Science Faculty University of Valladolid Prado de la Magdalena s/n. 47011 Valladolid. Spain Consejo Nacional de Investigaciones Científicas Técnicas Avellaneda 3657 3000 Santa Fe Argentina

This article presents a solution to pH control based on model-free intelligent control (MFIC) using reinforcement learning. This control technique is proposed because the algorithm gives a general solution for acid-base system, yet simple enough for its implementation in existing control hardware. In standard reinforcement learning, the interaction between an agent and the environment is based on a fixed time scale: during learning, the agent can select several primitive actions depending on the system state. A novel solution is presented, using multi-step actions (MSA): actions on multiple time scales consist of several identical primitive actions. This solves the problem of determining a suitable fixed time scale to select control actions so as to trade off accuracy in control against learning complexity. The application of multi-step actions on a simulated pH process shows that the proposed MFIC learns to control adequately the neutralization process.

关键词： learning control intelligent control online learning agents process control neutralization process pH control temporal abstraction model free

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