Nonlinear Control Using Human Behavior Learning

Abstract In this paper, we discuss a human behavior learning approach for nonlinear systems control. We use several cognitive models and human skills to model and accelerate the learning process. A neural reinforcement learning algorithm is applied as main cognitive model. A persistent exciting signal and experience replay methods are proposed to improve learning accuracy and overcome the sensitivity problem of the human actions. The stability and convergence of the neural network based reinforcement learning is discussed. Simulations results verify the approach with two benchmark control problems.