Identification of complex nonlinear processes based on fuzzy decomposition of the steady state space

Abstract

Abstract A methodology for identification and control of complex nonlinear plants using multi-model approach is presented in this paper. The proposed methodology is based on fuzzy decomposition of the steady state map. It is shown that such a decomposition strategy facilitates the design of input perturbation signals and helps in identifying linear or simple nonlinear models for each local region. A composition strategy to aggregate the local model predictions is proposed and shown to give excellent cross validation as well as to facilitate smooth switching between the local models. A novel control scheme that is based on the multi model strategy is proposed. The practicality of the identification and control scheme presented here is demonstrated by application to the continuous fermenter of Henson and Seborg (M.A. Henson, D.E. Seborg, Nonlinear control strategies for continuous fermenter, in: Proceedings of 1990 American Control Conference, San Diego, 1990), which exhibits severe nonlinearities and gain directionality changes.