PSO optimal tracking control for state-dependent coefficient nonlinear systems

Abstract

This contribution presents an infinite-horizon optimal tracking controller for nonlinear systems based on the state-dependent Riccati equation approach. The synthesized control law comes from solving the Hamilton-Jacobi-Bellman equation for state-dependent coefficient factorized (SDCF) nonlinear systems. The proposed controller minimizes a quadratic performance index, whose entries are determined by the particle swarm optimization (PSO) algorithm in order to improve the performance of the control system by fulfilling with design specifications such as bound of the control input expenditure, steady-state tracking error and rise time. The effectiveness of the proposed PSO optimal tracking controller is applied via simulation to the Van der Pol Oscillator. © 2014 Springer International Publishing Switzerland.