In this paper, a fuzzy reinforcement learning (RL)-based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi-Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the RL technique and fuzzy-augmented model, the new fuzzy integral RL-based fault-tolerant control algorithm is designed, and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proven by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method.

中文翻译：

---

通过积分强化学习方法对执行器故障的部分未知系统进行自适应模糊容错跟踪控制


本文首先针对具有执行器故障的部分未知系统提出了一种基于模糊强化学习（RL）的跟踪控制算法。基于Takagi-Sugeno模糊模型，开发了一种新颖的模糊增强跟踪动力学，并设计了具有相应性能指标的总体模糊控制策略，其中考虑了执行器失效和偏差故障等四种执行器故障。结合强化学习技术和模糊增强模型，设计了基于模糊积分强化学习的新型容错控制算法，并针对执行器故障的系统实时运行。动态矩阵可以是部分未知的，并且在线算法在学习过程中需要较少的信息传输或计算负载。在整体模糊容错策略下，达到了跟踪目标，并通过Lyapunov理论证明了稳定性。最后，在单连杆机械臂系统和F-16战斗机复杂的俯仰速率控制问题中的应用证明了该方法的有效性。