Advances in Model-Based Predictive Control

In the classic example of a steam engine governor an increase in speed immediately results in a decrease in steam supplied, so slowing the engine. In many instances there is a considerable lag between the corrective action (decrease in steam) and resumption of the correct state. For example, the output of a base chemical plant may take minutes or even hours to respond to pressure of temperature changes imposed on the plant. In these cases predictive control is required. Without a detailed running history of the chemical plant (in this example) it is then necessary to use model-based predictive control (rather than experience based predictive control). This book is devoted to all aspects of Model-Based Predictive Control, including new developments in the theory of the subect and current applications of MBPC to real processes. Topics included are: algorithm developments, industrial applications, and comparison with other approaches.

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