Knowledge-based reinforcement learning controller with fuzzy-rule network: experimental validation

Abstract

A model-free controller for a general class of output feedback nonlinear discrete-time systems is established by action-critic networks and reinforcement learning with human knowledge based on IF–THEN rules. The action network is designed by a single input fuzzy-rules emulated network with the set of IF–THEN rules utilized by the relation between control effort and plant’s output such as IF the output is high THEN the control effort should be reduced. The critic network is constructed by a multi-input FREN (MiFREN) for estimating an unknown long-term cost function. The set of IF–THEN rules for MiFREN is defined by the general knowledge of optimization such that IF the quadratic values of control effort and tracking error are high THEN the cost function should be high. The convergence of tracking error and bounded external signals can be guaranteed by Lyapunov direct method under general assumptions which are reasonable for practical plants. A computer simulation system is firstly provided to demonstrate the design method and the performance of the proposed controller. Furthermore, an experimental system with the prototype of DC-motor current control is conducted to show the effectiveness of the control scheme.