An Alternative Control Methodology to Complex System: Fuzzy Supervisory Indirect Learning Predictive Controller

In this paper, a fuzzy supervisory indirect learning predictive controller (FsiLPC) is proposed. The controller integrates the concepts of the conventional model based predictive control (MBPC), the controller output error method and the fuzzy rule based system. In contrast to the conventional MBPC, the design of FsiLPC is more generic in terms of model compatibility; the predictive model to be adopted in the scheme can be of any structure. The FsiLPC demonstrates a number of attractive characteristics for industrial implementation. These include flexibility of employing a predictive model, adaptive capability to the variation in the operating conditions, and simplicity of the operating mechanisms. The methodology is thus considered as a potential solution to the control of complex multi-input-multi-output (MIMO) systems. The strategy of the integration is first illustrated through an overview of the general architecture of the controller, followed by descriptions of the individual operating mechanism. Two case studies are investigated to evaluate the performances of the controller. The first case study describes the control of a simple system with non-minimum phase dynamics while the control of a multivariable industrial process of a single screw extruder is detailed in the second case study. The encouraging results stem the interest to further explore the applicability of the proposed controller for industrial uses.