Designing Efficient Model Predictive Control Algorithm Using Easy-to-Obtain Fuzzy Models
Abstract
The paper describes the design process of an efficient model predictive control (MPC) algorithm based on fuzzy models. An interesting feature of the proposed approach is that it uses easy--to--obtain fuzzy Takagi--Sugeno (TS) models composed of a few step responses employed as local models; one of these models is used to derive the dynamic matrix, and the second one, being a skillful modification of the first one, to generate the free response. The designed MPC algorithm uses formulation as an efficient quadratic optimization task. Still, it offers control quality compared with the MPC algorithm formulated as a nonlinear optimization task, thanks to the skillful generation of the free response. The efficiency of the proposed approach is tested and demonstrated in the simulated control system of the nonlinear and non--minimum phase process of the chemical reactor with the van de Vusse reaction.
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