Stable Economic Model-Predictive Control for T-S Fuzzy Systems with Persistent Disturbances

The economic performance of a process is generally managed in a hierarchical model-predictive control structure, wherein the low layer fulfills the dynamic tracking performance while the upper layer fulfills the economic performance. Nevertheless, this architecture achieves steady-state optimization, but it may ignore the dynamic optimization of this process. In contrast, the input and soft-state constraints are important to be satisfied whole the time with guaranteeing a feasible solution. From that perspective, economic model-predictive control is proposed to demonstrate the performance and the stability of T-S fuzzy systems with persistent disturbance. Based on fuzzy modeling, online optimal feedback control is designed to guarantee the proposed controller's stability and feasibility through a constrained-MPC problem in linear matrix inequalities form. The proposed approach combines economic optimization and the dynamic tracking of the fuzzy control system into a single online framework. A nonlinear numerical example and the boiler-turbine application are utilized to demonstrate the proposed scheme's effectiveness under system load variations and persistent disturbances.