A Sustainable Power Plant Control Strategy Based on Fuzzy Extended State Observer and Predictive Control

Abstract

The control of an ultra-supercritical (USC) boiler–turbine power plant is critical in maintaining the safety of the sustainable power grid. However, it is challenging due to the internal nonlinearity, hard manipulation constraints, and widespread uncertainties. To this end, a fuzzy extended state observer (FESO)-based stable fuzzy predictive control (SFPC) approach is developed in this paper. First, the control difficulties of the USC boiler–turbine unit are analyzed. Then, based on a Takagi–Sugeno (T–S) fuzzy model, a new FESO is developed for nonlinear systems to achieve a more precise observation performance. The gain of FESO is determined by solving a series of linear matrix inequalities, while guaranteeing the stability of FESO. Then, by combining the proposed FESO with the SFPC, an integrated FESO–SFPC algorithm is devised. The disturbance rejection ability of the FESO–SFPC algorithm is analyzed theoretically. Simulation results on a 1000 MW USC boiler–turbine power plant model further validate the effectiveness of the proposed method.

Model predictive control for gas turbine shaft speed based on model compensation using extended state observer

“…Since the predictive model is of high importance, the improvement on the robustness of the predictive model is helpful for wide-range operation. The predictive model is the plant given by Equation ( 6) modified by ADRC, in the form as G MP (s) given by Equation (16). It can be found that even at the medium/low-load conditions, for the proposed model compensation-based MPC, the settling time and overshooting are as good as them at high-load conditions.…”

“…The first category of such approaches is based on feedforward compensation. The disturbance observer (DOB) 13,14 or extended state observer (ESO) 15,16 is used to estimate the lumped disturbance and the feedforward control law is designed separately to compensate the effects of disturbance. The second category integrates the disturbance estimation into predictive model.…”

Research of NSMDOB-Based Compound Control for Photoelectric Tracking Platform

“…The method needs to get specific structural forms of disturbance, which may be applied to the compensation for a certain kind of disturbance [9]. Moreover, some scholars also proposed the design methods of fuzzy disturbance observer and neural network disturbance observer, which are difficult to realize in engineering practice [14], [15].…”