Fault-Tolerant Consensus Control for T-S Fuzzy Multiagent Systems Under Aperiodic Time-Constrained Sampling Communication and Stochastic Actuator Faults
针对受随机执行器故障和外部干扰的非线性多智能体系统，提出一种容错模糊时间约束记忆采样数据控制机制，通过T-S模糊模型和马尔可夫过程建模，实现均方渐近一致并保证H∞性能。
The main objective of this study is to develop a fault-tolerant fuzzy time-constrained memory-sampled-data control (TCMSDC) mechanism for analyzing the consensus performance of nonlinear multiagent systems (MASs) affected by stochastic actuator faults and external disturbances. To achieve this, the nonlinear MASs are first converted into quasi-linear subsystems using the Takagi–Sugeno (T-S) fuzzy model. In contrast to existing memory-sampled-data consensus methods, the constructed TCMSDC signals vary over time within each sampling period, thereby enhancing consensus performance. Thereafter, a Markov variable process is applied to model multiple stochastic actuator faults in the considered MASs. Furthermore, an aperiodic-sampling-dependent asymmetric dual-looped functional (ASADLF) is constructed, which incorporates different sets of matrices designed for each sampling instance. Subsequently, a new nonorthogonal polynomial integral inequality (NOPII) is introduced to approximate the integral quadratic terms. By leveraging this ASADLF along with the proposed NOPII technique, less conservative consensus criteria are derived in the form of linear matrix inequalities (LMIs), and the TCMSDC gain parameters are obtained to guarantee mean-square asymptotic consensus with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$</tex-math> </inline-formula> performance for the considered MASs. Finally, the effectiveness and advantages of the proposed TCMSDC approach in enhancing fault tolerance and achieving reliable consensus under stochastic actuator faults are validated through numerical simulations on multi-ship steering autonomous surface vehicles (SSASVs) and mass–spring systems.