This paper presents a novel auto-tuning subsystem-based fault-tolerant control (SBFC) system designed for robotic manipulator systems with n degrees of freedom (DoF). It initially proposes a novel model for joint torques, incorporating an actuator fault correction model to account for potential faults and a mathematical saturation function to mitigate issues related to unforeseen excessive torque. This model is designed to prevent the generation of excessive torques even by faulty actuators. Subsequently, a robust subsystem-based adaptive control strategy is proposed to force system states closely along desired trajectories, while tolerating various actuator faults, excessive torques, and unknown modeling errors. Notably, this control framework ensures uniform exponential stability (UES). Furthermore, optimal SBFC gains are determined by tailoring the JAYA algorithm (JA), a high-performance swarm intelligence technique, standing out for its capacity to optimize without the need for meticulous tuning of algorithm-specific parameters, relying instead on its intrinsic principles. Theoretical assertions are validated through the presentation of simulation outcomes.

翻译：本文提出了一种新颖的基于子系统自调谐的容错控制系统，专门针对具有n自由度的机器人操纵系统设计。首先，该研究提出了一种新的关节扭矩模型，该模型集成了执行器故障校正模型以应对潜在故障，以及数学饱和函数以减轻由意外过度扭矩引发的问题。此模型旨在防止故障执行器产生过大扭矩。随后，提出了一种基于鲁棒子系统自适应控制策略，迫使系统状态紧密跟随期望轨迹，同时容忍多种执行器故障、过大扭矩和未知建模误差。值得注意的是，该控制框架确保了均匀指数稳定性。此外，通过定制高表现群体智能技术JAYA算法确定最优子系统容错控制增益，该算法因无需精细调整算法特定参数，仅依赖其内在原理即可实现优化而脱颖而出。仿真结果验证了理论断言。