Skip to main content

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

Kambhampati, C.; Mason, J. D.; Warwick, K.

Authors

J. D. Mason

K. Warwick



Abstract

In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence of plant uncertainties and input–output constraints. There is no requirement that the plant should be open-loop stable and the analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectiveness of controllers designed according to the algorithm analyzed in this paper is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis function neural network is employed as the non-linear system model.

Journal Article Type Article
Publication Date 2000-04
Journal Automatica
Print ISSN 0005-1098
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 36
Issue 4
Pages 485-495
APA6 Citation Kambhampati, C., Mason, J. D., & Warwick, K. (2000). A stable one-step-ahead predictive control of non-linear systems. Automatica : the journal of IFAC, the International Federation of Automatic Control, 36(4), 485-495. https://doi.org/10.1016/s0005-1098%2899%2900173-9
DOI https://doi.org/10.1016/s0005-1098%2899%2900173-9
Keywords Nonlinear systems; Neural networks; RBFN's; Predictive control; Stability; Robust; Input–output constraints
Publisher URL https://www.sciencedirect.com/science/article/pii/S0005109899001739?via%3Dihub
;