Fuzzy Model-Based Control: A Practical Approach

This paper addresses the main steps in model-based control: the identification of the model and the design of the controller. A series of recent research results is aggregated to present a complete approach. A fuzzy model of the system is identified from sampled data using supervised fuzzy clustering for rule extraction. This model is applied in model predictive control (MPC) of the process. The non-convex optimization problem introduced by a nonlinear plant model is solved by discretizing the control space and apply discrete search techniques. The trade-off between computational time and performance that follows from the discretization is addressed by using fuzzy rule-based optimization to adapt the discrete control actions. The global fuzzy model-based control approach is applied to pressure control of a fermentation tank. Key-Words: fuzzy model-based control, fuzzy modeling, discrete model predictive control, discrete optimization.