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作者： Yu, D.L. Yu, D.W. Gomm, J.B. Page, G.F. Control Systems Research Group School of Engineering Liverpool John Moores University Byrom Street Liverpool L3 3AF United Kingdom Department of Automation Northwestern University at Qinhuangdao China

ISBN: (纸本)008045108X

An adaptive radial basis function (RBF) neural network model is developed in this paper for nonlinear systems using the recursive orthogonal least squares (ROLS) algorithm. The model is used in a nonlinear model predictive control (NMPC). The developed adaptive NMPC is applied to a chemical reactor rig. On-line control performance is presented and it demonstrates superiority over the fixed parameter PID control. Copyright © 2005 IFAC.

关键词： Neural network models

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Detecting sensor faults for a chemical reactor rig via adaptive neural network model

Detecting sensor faults for a chemical reactor rig via adapt...

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Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005

作者： Yu, Ding-Li Yu, Dingwen Control Systems Research Group School of Engineering Liverpool John Moores University Byrom Street Liverpool L3 3AF United Kingdom Dept. of Automation Northeast University Qinhuangdao China

An adaptive neural network model based approach to sensor fault detection is proposed for multivariable chemical processes. The neural model is used to predict process output for multi-step ahead with the prediction error used as the residual, while the model is on-line updated to capture dynamics change. The recursive orthogonal least squires algorithm (ROLS) is used to adapt a radial basis function (RBF) model to reduce the effects of data ill conditioning. Two error indices are developed to stop the on-line updating of the neural model and its corresponding threshold is used to distinguish the fault effect from model uncertainty. The proposed approach is evaluated in a three-input three-output chemical reactor rig with three simulated sensor faults. The effectiveness of the method and the applicability of the method to real industrial processes are demonstrated. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Chemical reactors

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A NEW DEVELOPMENT OF ADAPTIVE MODEL PREDICTIVE control

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IFAC Proceedings Volumes 2005年第1期38卷 348-353页

作者： D.L. Yu D.W. Yu J.B. Gomm G.F. Page Control Systems Research Group School of Engineering Liverpool John Moores University Byrom Street Liverpool L3 3AF U.K. Department of Automation Northwestern University at Qinhuangdao China

关键词： Adaptive RBF network NMPC adaptive control ROLS algorithm

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Quadratic stability of process systems in generalized Lotka-Volterra form 6

Quadratic stability of process systems in generalized Lotka-...

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6th IFAC Symposium on Nonlinear control systems, NOLCOS 2004

作者： Magyar, A. Szederkényi, G. Hangos, K.M. Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute HAS P.O. Box 63 BudapestH-1518 Hungary Department of Computer Science University of Veszprém Egyetem u. 10 VeszpremH-8200 Hungary

The global and local stability of process systems in generalized Lotka-Volterra form is studied in this paper using entropy-like and quadratic Lyapunov function candidates. The global stability check for LV models is performed by solving an LMI for a diagonal positive semi-definite matrix using singular perturbation technique. It is shown that a quadratic Lyapunov function can also be determined by solving linear matrix inequalities (LMIs). In addition, the quadratic stability neighborhood is convex in the space of the quasi-monomials and can be estimated by computing its comer points using LMIs. Furthermore, it is proved that quadratic stability with a diagonal weighting matrix enables to construct adissipative-Hamiltonian description of the system. The developed methods are illustrated on the model of a continuously stirred tank reactor with a nonlinear reaction system. Copyright © 2004 IFAC.

关键词： Linear matrix inequalities

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Quadratic stability of process systems in generalized lotka-volterra form

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IFAC Proceedings Volumes 2004年第13期37卷 1217-1222页

作者： A. Magyar G. Szederkényi K.M. Hangos Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute HAS H-1518 P.O. Box 63 Budapest Hungary Tel.: +361 2796000； fax: +361 466 7503 Department of Computer Science University of Veszprém H-8200 Egyetem u. 10 Veszprém Hungary Tel.: +***； fax: +***

The global and local stability of process systems in generalized Lotka-Volterra form is studied in this paper using entropy-like and quadratic Lyapunov function candidates. The global stability check for LV models is performed by solving an LMI for a diagonal positive semi-definite matrix using singular perturbation technique. It is shown that a quadratic Lyapunov function can also be determined by solving linear matrix inequalities (LMIs). In addition, the quadratic stability neighborhood is convex in the space of the quasi-monomials and can be estimated by computing its corner points using LMIs. Furthermore, it is proved that quadratic stability with a diagonal weighting matrix enables to construct a dissipative-Hamiltonian description of the system. The developed methods are illustrated on the model of a continuously stirred tank reactor with a nonlinear reaction system.

关键词： quasi-polynomial systems Lotka-Volterra systems stability analysis linear matrix inequalities Hamiltonian systems

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Long-range optimal model and multi-step-ahead prediction identification for predictive control 13

Long-range optimal model and multi-step-ahead prediction ide...

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13th IFAC Symposium on System Identification, SYSID 2003

作者： Haber, R. Schmitz, U. Bars, R. Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne Betzdorfer Str. 2 KölnD-50679 Germany Department of Automation and Applied Informatics Control Research Group Hungarian Academy of Sciences Budapest University of Technology and Economics Hungary

Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements are noisy or/and model structure differs from real process structure. In this paper two different identification schemes are presented and compared: long-range predictive single-model identification and simultaneous multi-step-ahead prediction identification. It is shown that the first method is easier to realize but the second one leads to more accurate results. Both methods are derived for a first-order model in details. Simulation runs and a level control example illustrate the algorithms presented. © 2003 International Federation of Automatic control.

关键词： Parameter estimation

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Hybrid IMC dead-beat controller design in delta domain 2

Hybrid IMC dead-beat controller design in delta domain

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2nd IFAC Conference on control systems Design, CSD 2003

作者： Preit, Z. Bars, R. Vajk, I. Haber, R. Department of Automation and Applied Informatics Budapest University of Technology and Economics MTA-BME Control Research Group Goldmann Gyter 3 BudapestH-1111 Hungary Department of Plant and Process Engineering University of Applied Science Cologne Germany

The paper presents a control system design technique in delta domain for IMC (Internal Model control) structure. Also a generalised form for delta domain Dead-beat control algorithm is given, a hybrid implementation of the controller (Z-domain combined with delta domain) is presented and its architecture is compared with the pure deltadomain implementation. The effect of placing the limitations in the IMC structure is also studied. The hybrid control structure is compared with a similar Dead-beat controller in Z-domain for second and third order plants (benchmarks). An illustrative sensitivity analysis between the hybrid control system and the Z-domain system has been performed. © 2003 IFAC.

关键词： controllers

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Reduction of the Optimization Efforts with Predictive control by Using Non-Equidistant Prediction Points

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IFAC Proceedings Volumes 2003年第8期36卷 57-62页

作者： U. Schmitz R. Haber R. Bars Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne D-50619 Köln Betzdorf er Str. 2 Germany fax: +49-221-8275-2836 Department of Automation and Applied Informatics MTA-BME Control Research Group Budapest University of Technology and Economics Hungary

With predictive control most of the computation time is spent for the simulation of the predicted variables and for the optimization if constraints or nonlinear processes are assumed. In addition to the known blocking technique for the manipulated variable another possibility is calculating the control error not in each sampling point of the prediction horizon but only in some coincidence points. It will be shown that the best choice is to allocate the coincidence points exponentially thus that with small prediction steps more and with increasing prediction steps less coincidence points are considered. As a practical example the multivariable control of a distillation column model illustrates the benefits of the method presented.

关键词： Predictive control optimal control control applications chemical industry execution time

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Long-range optimal model and multi-step-ahead prediction identification for predictive control

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IFAC Proceedings Volumes 2003年第16期36卷 477-482页

作者： R. Haber U. Schmitz R. Bars Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne D-50679 Köln Betzdorfer Str. 2 Germany fax: +49-221-8275-2836 Department of Automation and Applied Informatics Control Research Group of the Hungarian Academy of Sciences Budapest University of Technology and Economics Hungary

关键词： process identification prediction parameter estimation predictive control

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Hybrid IMC Dead-Beat controller Design in Delta Domain

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IFAC Proceedings Volumes 2003年第18期36卷 381-386页

作者： Zs. Preitl R. Bars I. Vajk R. Haber Department of Automation and Applied Informatics Budapest University of Technology and Economics. MTA-BME Control Research Group H-1111 Budapest Goldmann Gy. tér 3. Hungary: fax : + 36-1-463-2871 Department of Plant and Process Engineering University of Applied Science Cologne Germany

关键词： controller design delta domain generalised hybrid Dead-beat controller sensitivity analysis

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