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.

Table of Contents:
Advances in model-based predictive control ; Matching the uncertainty of the model given by global identification techniques to the robustness of a model-based predictive controller ; Stability and output terminal constraints in predictive control ; Use of qualitative models for the choice of design parameters of model-based predictive controllers ; Artificial neural network model-based control ; Neural network based adaptive predictive control ; Fuzzy generalized predictive controller ; A game theoretic approach to moving horizon control ; Pre-tuning of self-tuners ; Stabilizing predictive control: the singular transition-matrix case ; Robust adaptive predictive control ; Continuous-time generalised predictive control (CGPC): Implementation issues ; Evaluation of stochastic characteristics for a constrained GPC algorithm ; Model-based predictive control for two-dimensional dynamic processes ; Model-based predictive controller with Kalman filtering for state estimation ; On the relationship between GPC and PIP controllers ; A comparative study of GPC and DMC controllers ; Constrained generalized predictive control with dynamic programming ; Min-max model-based predictive control ; Stability and robustness of constrained model predictive control ; New sufficient conditions for global stability of receding horizon control for discrete-time nonlinear systems ; Nonlinear model-based predictive control ; Model-based predictive control methods based on non-linear and bilinear parametric system descriptions ; Stability results for constrained model predictive control ; Stability in constrained predictive control ; Stability of constrained MBPC using an internal model control structure ; Actuator nonlinearities in predictive control ; Advances in constrained generalized predictive control with application to a dynamometer model ; Application of constrained GPC for improving performance of controlled plants ; Generalised predictive control in clinical anaesthesia ; Modelling control in a large water treatment works ; Design and realization of a MIMO predictive controller for a 3-tank system ; Predictive control for target tracking ; Predictive control application in the machine-tool field ; Application of GPC to a solar power plant