Robust regulation for superheated steam temperature control based on data-driven feedback compensation

Control of the superheated steam temperature is a significant technical challenge to coal-fired power plants due to the strong nonlinearity, large inertia and parameter uncertainties with external disturbances. This paper proposes a robust proportion-integration-differentiation (PID) control of the superheated steam temperature with proven stability and the external disturbance rejection. The design is based on the proposed data-driven feedback compensator (DFC) which is a neural network (NN) trained to isolate the nonlinearity and inertia from the PID controller, allowing the system for tuning the PID controller can be simplified as an approximate linear system with the modeling error based-feedback and feed-forward compensations. Then, the PID controller is tuned by the Nyquist stability criterion to place every closed-loop pole to a specific region in the left-half s-plane to guarantee the stability with considering the modeling error. Besides, a NN based feed-forward compensator is trained as the inverse model of the feedback compensator to further smooth the temperature fluctuation caused by the disturbance. The simulations and engineering implementation of the superheated steam temperature controls for a coal-fired power plant in Australia show the effectiveness.

Automated summary

This paper proposes a robust PID control method based on neural networks for controlling the superheated steam temperature in coal-fired power plants. By using a data-driven feedback compensator, the nonlinearity and inertia are isolated, simplifying the tuning process. The stability of the control system is ensured by the Nyquist stability criterion and a feed-forward compensator.