Adaptive Process Control Using Controlled Finite Markov Chains Based on Multiple Models

Abstract

Controlled finite Markov chain (CFMC) approach can deal with a large variety of signals and systems with multivariable, non-linear and stochastic nature. In this paper, adaptive control based on multiple models is considered. For a set of candidate plant models, CFMC models (and controllers) are constructed off-line. The state transitions predicted by the CFMC models are compared with frequentist information obtained from on-line data. The best model (and controller) is chosen based on the Kullback–Leibler distance. This approach to adaptive control emphasizes the use of physical models as the basis of reliable plant identification. Three series of simulations are conducted: to examine the performance of the developed Matlab-tools; to illustrate the approach in the control of a non-linear non-minimum phase van der Vusse CSTR plant; and to examine the suggested model selection method for the adaptive control.