Design and experimental evaluation of an adaptive predictive controller using recurrent neural network

This paper presents a recurrent-neural-network based predictive control for a class of nonlinear discrete-time systems. The neural predictive control law is developed from the minimization of a generalized predictive performance criterion. A real-time adaptive control algorithm, including a neural predictor and a neural predictive controller is proposed, the adaptive learning rates for both the neural predictor and controller are determined based on Lyapunov stability theory. Simulation results reveals that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative nonlinear systems Experimental results for a variable-frequency oil-cooling control process are per which have shown effectiveness of the proposed method under the conditions of setpoint and load changes.