A stable one-step-ahead predictive control of non-linear systems

In this paper stability of one-step ahead predictive controllers based on n on-linear models is established. It is shown that, under conditions which c an be fulfilled by most industrial plants, the closed-loop system is robust ly stable in the presence of plant uncertainties and input-output constrain ts. There is no requirement that the plant should be open-loop stable and t he analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectivene ss of controllers designed according to the algorithm analyzed in this pape r is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis f unction neural network is employed as the non-linear system model. (C) 2000 Elsevier Science Ltd. All rights reserved.