Inverse Dynamic Neuro-Controller for Superheater Steam Temperature Control of a Large-Scale Ultra-Supercritical (USC) Boiler Unit

The main focus of this paper is to develop an Inverse Dynamic Neuro-Controller (IDNC) by utilizing the inverse dynamic relationship of the superheater system for a large-scale ultra-supercritical (USC) boiler unit. After a recurrent neural network-based Inverse Dynamic Process Model (IDPM) has been built and trained, it is then used as a feedforward controller to improve the superheater steam temperature control. In order to eliminate the steady-state control error induced by the model and the IDPM error, a simple feedback PID compensator is added to the inverse controller. Simulation control tests are made on a full-scope simulator of the USC power generating unit to test the validity of the method. It is shown that the convergence speed of the IDNC is faster than the conventional cascaded PID control scheme. Best control result can be acquired by the IDNC together with a simple PID feedback compensator.