Approximate neural optimal control with reinforcement learning for a torsional pendulum device

Abstract

A torsional pendulum device containing hyperbolic tangent input nonlinearities can be formulated as a nonaffine system. Unlike basic affine systems, the optimal feedback control of complex nonaffine plants is difficult but quite important. In this paper, the approximate optimal control design of continuous-time nonaffine nonlinear systems is investigated with the help of reinforcement learning. For addressing the learning algorithm conveniently, an effective pre-compensation technique is adopted to perform proper system transformation. Then, the integral policy iteration strategy is incorporated to relieve the demand of system dynamics. Moreover, the actor–critic structure is implemented by virtue of neural network approximators. Finally, the experimental verification for the proposed torsional pendulum plant is conducted after a learning process of 20 iterations and the stability performance with basic robustness guarantee can be observed during two case studies.