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. 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. Notably, this control framework ensures uniform exponential stability (UES). The enhancement of accuracy and tracking time for reference trajectories, along with the validation of theoretical assertions, is demonstrated through the presentation of simulation outcomes.

翻译：本文提出了一种新颖的基于子系统的自整定容错控制（SBFC）系统，专为具有n个自由度（DoF）的机器人机械臂系统设计。该研究首先提出了一种新颖的关节转矩模型，该模型集成了执行器故障校正模型以应对潜在的故障，并引入数学饱和函数以缓解因意外过大转矩引发的问题。该模型旨在防止即使是故障执行器产生过大转矩。随后，提出了一种鲁棒的基于子系统的自适应控制策略，以迫使系统状态紧密跟踪期望轨迹，同时容忍各种执行器故障、过大转矩以及未知的建模误差。此外，通过定制JAYA算法（JA）——一种高性能的群体智能技术——来确定最优的SBFC增益。该算法因其无需精细调整算法特定参数、仅依靠其内在原理即可进行优化的能力而脱颖而出。值得注意的是，该控制框架保证了系统的一致指数稳定性（UES）。通过展示仿真结果，证明了参考轨迹的精度和跟踪时间的改善，并验证了理论主张。