Direct adaptive control: an echo state network and genetic algorithm approach

Abstract

This paper presents a direct adaptive approach to design controllers for nonlinear dynamical systems, where system identification of the unknown dynamical system is not required. The solution is powered by both echo state network (ESN) and genetic algorithm (GA). ESN enables a simple modeling of the controller, with which only a linear readout needs to be trained. GA is used to optimize ESN's linear readout directly so that system identification is not required. Simulation results reveal that the algorithm is capable of achieving very good control performance with computational efficiency.