This paper presents a novel auto-tuning subsystem-based fault-tolerant control (SBFC) system designed for robot manipulator systems with n degrees of freedom. It first employs an actuator fault model to account for various faults that may occur, and second, a mathematical saturation function is incorporated to address torque constraints. Subsequently, a novel robust subsystem-based adaptive control method is proposed to direct system states to follow desired trajectories closely in the presence of input constraints, unknown modeling errors, and actuator faults, which are primary considerations of the proposed system. This ensures uniform exponential stability and sustained performance. In addition, optimal values are identified by tuning the SBFC gains and customizing the JAYA algorithm (JA), a high-performance swarm intelligence technique. Theoretical assertions are validated through the presentation of simulation outcomes.

翻译：本文提出一种新型的基于子系统的自整定容错控制系统（SBFC），专为具有n自由度的机器人机械臂系统设计。首先，采用执行器故障模型来考虑可能发生的各类故障；其次，引入数学饱和函数以处理扭矩约束。在此基础上，提出一种新颖的鲁棒自适应控制方法，旨在当存在输入约束、未知建模误差和执行器故障（这些是所提系统的核心考量因素）时，引导系统状态紧密跟踪期望轨迹，从而确保均匀指数稳定性与持续性能。此外，通过调节SBFC增益并定制高性能群体智能技术——JAYA算法（JA），识别出最优参数值。仿真结果验证了理论论断。