System Parameters' Identification and Optimal Tracking Control for Nonlinear Systems

This paper proposes a parameters identification methodology via the continuous-time least-squares algorithm for those nonlinear systems which are linear with respect to their parameters. The parameter identification method can be used for easily determining all the parameters in real plants from only input-output data measurements, such that a posteriori, a model-based control strategy can be synthesized. It is worth mentioning that when a control engineer wants to design a modern and sophisticated controller, usually the system model is used for such purposes, which depends on its parameters, however, in general the system parameters are not easy to determine. Also, this paper uses the parameters identification methodology for the design of an optimal tracking controller for state-dependent coefficient factorized (SDCF) nonlinear systems. Both, the parameters identification scheme and the optimal control strategy are applied via simulations for the control of a Permanent Magnet Synchronous Motor (PMSM), a three-phase nonlinear machine. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.