Adaptive Predictive Control of an Electromagnetic Suspension System with LOLIMOT Identifier

Abstract

Control design using long-range prediction based on a dynamic model of the plant has become an important contender for high performance applications. In case of important parameters variations however, the maintenance of a high level of performances maybe impossible to reach with a fixed controller, even if the predictive laws ensure intrinsic robustness. In this paper locally linear model tree (LOLIMOT) is used as an identifier of an electromagnetic suspension system. Adaptation mechanism based on online estimation of the each local linear model weights in LOLIMOT algorithm is used to make the system adaptive. Also, an evolutionary programming (EP) is used to determine the optimized control variables for a finite future time interval. Simulation results on EMS system conforms the effectiveness of the proposed predictive control strategy in time varying nonlinear systems