Fuzzy predictive control applied to an air-conditioning system

• Published in: Control engineering practice 1997-10, Vol.5 (10), p.1395-1406
• Main Authors: Sousa, J.M., Babuška, R., Verbruggen, H.B.
• Format: Article
• Language: English
• Subjects: fuzzy control; fuzzy modeling; model-based control; optimization problems; Predictive control
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/S0967-0661(97)00136-6

A method of designing a nonlinear predictive controller based on a fuzzy model of the process is presented. The Takagi-Sugeno fuzzy model is used as a powerful structure for representing nonlinear dynamic systems. An identification technique which enables the acquisition of the fuzzy model from process measurements is described. The fuzzy model is incorporated as a predictor in a nonlinear model-based predictive controller, using the internal model control scheme to compensate for disturbances and modeling errors. Since the model is nonlinear, a non-convex optimization problem must be solved at each sampling period. An optimization approach is proposed, that alleviates the computational burden of iterative optimization techniques, by using a combination of a branch-and-bound search technique, applied in a discretized space of the control variable, with an inverted fuzzy model of the process. The algorithm is applied to temperature control in air-conditioning system. Comparisons with a nonlinear predictive control scheme based on iterative numerical optimization show that the proposed method requires fewer computations and achieves better performance. Real-time control results are presented.