This paper presents a novel robust fault reconfiguration scheme based on optimized Takagi–Sugeno (T–S) fuzzy model and nonsingular terminal sliding mode observer (NTSMO) with adaptive law for micro-gas turbine (MGS). An optimized T–S fuzzy model is introduced because it can approximate any nonlinear model with arbitrary precision, and an improved imperial competition algorithm (ICA) using adaptive reform probability is proposed to improve the accuracy of the model. A linear transformation method is introduced to decouple the fault and disturbance of the system. The nonsingular terminal sliding mode observer is designed to reconstruct actuator fault and disturbance with unknown upper bound of a change rate, in which an adaptive law is introduced to update the sliding mode gain in real-time to eliminate the influence of fault, disturbance and modeling uncertainty. Simulations in Matlab/Simulink show high reconfiguration accuracy and high-speed of the proposed method despite of the presence of fault, disturbance and modeling uncertainty.

本文提出了一种基于优化的Takagi-Sugeno（TS）模糊模型和具有自适应律的微型燃气轮机（MGS）的非奇异终端滑模观测器（NTSMO）的新型鲁棒故障重配置方案。介绍了一种优化的TS模糊模型，因为它可以以任意精度逼近任何非线性模型，并提出了一种采用自适应改革概率的改进的帝国竞争算法（ICA），以提高模型的准确性。引入线性变换方法来解耦系统的故障和干扰。非奇异终端滑模观测器被设计用于以未知的变化率上限来重构执行器故障和干扰，其中引入了自适应定律以实时更新滑模增益以消除故障的影响，干扰和模型不确定性。在Matlab / Simulink中的仿真显示，尽管存在故障，干扰和建模不确定性，但该方法具有很高的重构精度和速度。