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.

本文提出了一种适用于受输入约束的一般多目标 T-S 模糊系统的自适应帕累托最优控制方案。首先，采用模糊状态反馈控制器来关闭增强系统。然后，基于线性矩阵不等式 (LMI)，提出了一种新的多目标优化器，用于预调节控制增益以同时最小化H ₂ / H _∞表现。利用多面体表示，在所提出的优化器中推导出确保输入约束系统稳定性的充分条件。此外，通过利用隶属函数相关分析和参数化 LMI 技术，可以放宽最终的设计标准。此外，在不确定的工况下，设计了具有减少计算负担的交互式模糊决策器来在线调度帕累托最优控制增益。最后，在永磁同步电机系统上实施的仿真和实验验证了所提出方案的有效性和适用性。