Adaptive Pareto Optimal Control of T–S Fuzzy System with Input Constraints and Its Application

This paper proposes an adaptive Pareto optimal control scheme for a general class of multi-objective T–S fuzzy systems subject to input constraints. Firstly, the fuzzy state feedback controller is employed to close the augmented system. Then, based on linear matrix inequality (LMI), a novel multi-objective optimizer is proposed for pre-regulation of the control gains to simultaneously minimize H2/H∞ performance. Utilizing a polytopic representation, sufficient conditions to ensure the stability of the input constrained system are derived in the proposed optimizer. Furthermore, the resultant design criteria are relaxed by utilizing the membership-function-dependent analysis and the parameterized LMI technologies. Besides, under uncertain working conditions, the interactive fuzzy decision-maker with the reduced computation burden is designed to online schedule Pareto optimal control gains. Finally, simulations and experiments implemented on a permanent magnet synchronous motor system validate the effectiveness and applicability of the proposed scheme.