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

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

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.

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

• Journal Article Type: Article
• Acceptance Date: Jul 7, 1999
• Online Publication Date: Feb 14, 2000
• Publication Date: 2000-04
• Journal: Automatica
• Print ISSN: 0005-1098
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 36
• Issue: 4
• Pages: 485-495
• 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
• Public URL: https://hull-repository.worktribe.com/output/409690
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0005109899001739?via%3Dihub