A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler-Turbine Systems

Optimal control theory and reinforcement learning are gradually being used in the field of industrial control. In this article, a new optimal tracking control scheme for 160 MW boiler-turbine systems is proposed based on an online policy iteration integral reinforcement learning (IRL) method. Firstly, a self-learning state tracking control with a cost function is developed to deal with the optimal tracking control problems for the boiler-turbine nonlinear system. Then with a modified cost function, a policy iteration-based IRL method is introduced to obtain the optimal control law. Finally, the monotonicity and the convergence of the cost function is analyzed and the detailed implementation of the policy iteration-based IRL method is provided via neural networks. The simulation results show that the control of the boiler-turbine system can converge in a short time by using this online iterative method. Through a theoretical simulation case, it can be concluded that the proposed method is more advanced compared with the MPC method.

Automated summary

This article proposes a new optimal tracking control scheme for 160 MW boiler-turbine systems based on an online policy iteration integral reinforcement learning (IRL) method. The implementation and simulation results using neural networks demonstrate that the proposed method can achieve control convergence in a short time for boiler-turbine systems, and it is more advanced compared to the MPC method.