A process-model-free method for model predictive control via a reference model-based proportional-integral-derivative controller with application to a thermal power plant

Abstract

Introduction: Model predictive control (MPC) is an advanced control strategy which can achieve fast reference tracking response and deal with process constraints, time delay and multivariable problems. However, thermal processes in coal-fired power plants are usually difficult to model accurately, which limits the application of MPC to thermal power plants.Methods: To solve the problem, this paper proposes a process-model-free method for MPC via a reference model (RM)-based controller, i.e., a desired dynamic equational (DDE) proportional-integral-derivative (PID) controller (DDE-PID).Results and Discussion: The DDE-PID can provide the design model and enhance the disturbance rejection ability for MPC. Simulations and results of field tests on a coal-fired unit show the superiorities of the proposed controller in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of the field application of the MPC with DDE-PID, or MPC-DDE in short, to thermal power plants.

Reduction of emissions and improvement of dynamic responses of a supercritical clean coal generation unit via neural network inverse control strategy

“…In control theory, the controlled reference correction, by predictive control or intelligent inverse control, is capable of increasing the speed of the response of the various actuators in the regulatory layer of control. 7,13,14,46 The load signal taken from set of data portion representing the once-through operation has been injected as an expected demand signal in order to observe the effect of the upgraded controller on the inputs, especially the coal consumption. As depicted in Figures 24 and 25, which represents the power output and coal flow input, respectively, the load demand is followed smoothly by the inverse controller, and that is also reflected in the coal consumption that has been robustly smoothed and minimized.…”