A strategy for controlling nonlinear systems using a learning automaton

This paper presents an application of learning automaton (LA) for nonlinear system control. The proposed control strategy utilizes a learning automaton in which the reinforcement scheme is based on the Pursuit Algorithm interacting with a nonstationary environment. Modulated by an adaptive mechanism, the LA selects, at each control period, a local optimal action, which serves as input to the controlled system. During the control procedure, the system output value takes into account the changes occurring inside the system and provides reward/penalty responses to the learning automaton.