Nonlinear model based predictive control using multiple models approach expanded on Laguerre bases

Abstract:This paper proposes a nonlinear model based predictive control (NMPC) algorithm for nonlinear systems by using multiple models approach. To have a less complexity model we expand each linear sub-model on an orthogonal Laguerre basis, the characteristic pole of which should be optimized. In this paper we propose a pole optimization algorithm based on the Gauss-Newton method and we use the provided Laguerre multiple model (LMM) to synthesize a NMPC algorithm. The proposed pole optimization technique as well as the NMPC using LMM approach are validated on a chemical reactor.

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