Design of an Adaptive Receding Horizon Controller Based on TSK Fuzzy Inference System for a Nonlinear Dynamic System

In this paper, the design steps of a multi-model adaptive receding horizon controller for a nonlinear dynamic system are investigated. To implement this control structure, the Takagi-Sugno-Kong (TSK) fuzzy inference system (TSK) has been used to predict the behavior of the dynamic system on a receding horizon. In the proposed controller, the linear part of the TSK fuzzy model is used as a linear model to implement a multi-stage receding horizon controller to calculate the optimal control input sequence. A standard least square algorithm is used to identify the rules consequent parameters of the TSK model. A clustering method is used for partitioning the input-output space in order to generate TSK fuzzy model. Each cluster represents a functional area of the complex dynamic system in the input-output space. In the proposed control strategy, it is assumed that the variables which are used in the premise of the rules are also those which are used in linear models that describe the consequents of the rules. For proper control of the nonlinear system, multiple models are used on the receding horizon. In order to evaluate the proposed control strategy, the proposed control structure has been used to control the power of a nuclear reactor in the charge pursuit problem. The simulation results show the good performance of the proposed control structure.