To solve the temperature control problem of the catalyst baking furnace

a heuristic dynamical programming (HDP) control method is proposed.The optimal control policy in HDP scheme is approximated gradually by implementing policy evaluation and policy improvement repeatedly.The system dynamics and the critic model are established by artificial neural networks.The learning algorithm of the HDP controller is clarified based on the gradient-decent principle.The proposed controller is tested on a baking furnace in a certain catalyst company.The experimental results indicate that the HDP controller has stronger ability to accommodate different work conditions than PID controller.In comparison with PID controller

the control precision in HDP controller increases about 70%

and the average electric current in HDP controller decreases about 5%.