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Advances in Model-Based Predictive Control : Oxford Science Publications - David Clarke

Advances in Model-Based Predictive Control

Oxford Science Publications

By: David Clarke (Editor)

Hardcover Published: 5th May 1994
ISBN: 9780198562924
Number Of Pages: 548

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Model based predictive control (MBPC) is arguably the most important approach to the advance control of complex interacting industrial processes. Unique among modern theories, MBPC can handle real-time state and actuator constraints in a natural way, enabling plants to maximize their profits. In addition, the wide range of model structures, prediction horizons, and optimization criteria allows for tailor-made MBPC applications--whether they be for high-speed machine tools or large-scale industrial processes. This timely edited volume, based on a conference held at Oxford University and devoted exclusively to MBPC, brings the field up to date with the latest theoretical and practical advances. Topics include how MBPC is expanding to include variants in the basic themes (such as new prediction and optimization approaches, nonlinear models, and two-dimensional problems), general stabilization ideas for constrained plant, and unsolved problems in MBPC. This excellent volume is the introduction to the theory, current applications, and hot research areas in MBPC that students and professionals in control systems have been waiting for.

`The book will be of interest to both researchers and designers, and control engineers.' Aslib Book Guide, Vol.59, no. 11, Nov 1994

D.W. Clarke: Advances in model-based predictive control J. Richalet, C. de Prada, & M. Sanzo: Matching the uncertainty of the model given by global identification techniques to the robustness of a model-based predictive controller G. de Nicolaom & R. Scattolini: Stability and output terminal constraints in predictive control K.M. Hangos, Zs. Csaki, & E.I. Varga: Use of qualitative models for the choice of design parameters of model-based predictive controllers G. Montague, & M.J. Willis: Artificial neural network model-based control Y. Tan, & R. de Keyser: Neural network based adaptive predictive control D. Matko: Fuzzy generalized predictive controller S. Lall, & K. Glover: A game theoretic approach to moving horizon control M. Karny, & A. Halouskova: Pre-tuning of self-tuners L. Chisei, & E. Mosca: Stabilizing predictive control: the singular transition-matrix case T.-W. Yoon: Robust adaptive predictive control H. Demircioglu: Continuous-time generalised predictive control (CGPC): Implementation issues A. Ordys: Evaluation of stochastic characteristics for a constrained GPC algorithm M.B. Zarrop, & J.J. Troyas: Model-based predictive control for two-dimensional dynamic processes K. Dadd, & P. Krauss: Model-based predictive controller with Kalman filtering for state estimation J. Taylor, P.C. Young, & A. Chotai: On the relationship between GPC and PIP controllers C. de Prada, & J. Serrano: A comparative study of GPC and DMC controllers A.P. de Madrid, M. Santos, S. Dormido, & F. Morilla: Constrained generalized predictive control with dynamic programming J.C. Allwright: Min-max model-based predictive control M. Morari: Stability and robustness of constrained model predictive control M. Alamir, & G. Bornard: New sufficient conditions for global stability of receding horizon control for discrete-time nonlinear systems L. Kershenbaum, D.Q. Mayne, R. Pytlak, & R.B. Vinter: Nonlinear model-based predictive control S. Sommer: Model-based predictive control methods based on non-linear and bilinear parametric system descriptions V. Balakrishnan, Z.Q. Zheng, & M. Morari: Stability results for constrained model predictive control P.O. Scokaert: Stability in constrained predictive control S.A. Heise, & J.M. Maciejowski: Stability of constrained MBPC using an internal model control structure C.M. Chow: Actuator nonlinearities in predictive control J.A. Rossiter, & B. Kouvaritakis: Advances in constrained generalized predictive control with application to a dynamometer model A.G. Kuznetsov: Application of constrained GPC for improving performance of controlled plants D.A. Linkens, & M. Mahfouf: Generalised predictive control in clinical anaesthesia E.P. Evans, & R. Harpin: Modelling control in a large water treatment works F. Berlin, & P.M. Frank: Design and realization of a MIMO predictive controller for a 3-tank system K. Warwick, & E. Kassapakis: Predictive control for target tracking D. Dumur, & P. Boucher: Predictive control application in the machine-tool field E. Camacho: Application of GPC to a solar power plant

ISBN: 9780198562924
ISBN-10: 0198562926
Series: Oxford Science Publications
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 548
Published: 5th May 1994
Publisher: Oxford University Press
Country of Publication: GB
Dimensions (cm): 25.1 x 19.6  x 3.6
Weight (kg): 1.29