Adaptive Predictive Control Based on Local Linear Fuzzy Models

Abstract

Abstract In this paper, local linear fuzzy models are used for adaptive predictive control of nonlinear processes. First, an algorithm (LOLIMOT = local linear model tree algorithm) for off-line identification of Takagi-Sugeno type fuzzy models is briefly reviewed. In order to cope with time-variant nonlinear processes and also with the influence of non-measurable disturbances, an on-line adaptation of the model is performed. A recursive least-squares algorithm (RLS) is applied to update the linear parameters in the rule consequents locally. The local estimation results in low computational effort and avoids forgetting in non-active operating regimes. The effectiveness of the approach is demonstrated by simulations and by controlling the output temperature of a cross-flow heat exchanger.