Shortest-prediction-horizon non-linear model-predictive control

This article concerns non-linear control of single-input-single-output processes with input constraints and deadtimes. The problem of input-output linearization in continuous time is formulated as a model-predictive control problem, for processes with full-state measurements and for processes with incomplete state measurements and deadtimes. This model-predictive control formulation allows one (i) to establish the connections between model-predictive and input-output linearizing control methods; and (ii) to solve directly the problems of constraint handling and windup in input-output linearizing control. The derived model-predictive control laws have the shortest possible prediction horizon and explicit analytical form, and thus their implementation does not require on-line optimization. Necessary conditions for stability of the closed-loop system under the constrained dynamic control laws are given. The connections between (a) the developed control laws and (b) the model state feedback control;and the modified internal model control-are established. The application and performance of the derived controllers are demonstrated by numerical simulations of chemical and biochemical reactor examples. (C) 1997 Elsevier Science Ltd.