Predictive control of a Nonlinear process using multiple models optimization based on fast evolutionary programming

This paper presents a predictive control method based on switching among a set of mathematical models for a nonlinear experimental process, An adaptive predictive control configuration with multi-model identification scheme is appropriate to deal with systems subjected to sudden parameter changes or running at several operating points with different characteristics, The methodology uses a set of discrete-time mathematical models (multiple models) of the process obtained from an off-line technique by fast evolutionary programming with mutation operator based on Cauchy distribution. Experimental tests, identification and control tasks are carried out in a laboratory scale fan-and-plate plant. The good performance shown by the predictive controller based on multiple models confirms the usefulness and robustness of the proposed control method.