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自整定的容错控制（SBFC）系统，专为具有n自由度的机械臂系统设计。首先，提出了一种新的关节力矩模型，其中包含执行器故障校正模型以应对潜在故障，并引入数学饱和函数以减轻意外过大力矩问题。该模型旨在防止故障执行器产生过大力矩。随后，提出了一种鲁棒的基于子系统的自适应控制策略，强制系统状态紧密跟踪期望轨迹，同时容忍各种执行器故障、过大力矩和未知建模误差。此外，通过定制化JAYA算法（JA）——一种高性能的群体智能技术，凭借其无需精细调整算法特定参数而依赖内在原理进行优化的能力——确定了最优SBFC增益。值得注意的是，该控制框架确保了均匀指数稳定性（UES）。通过仿真结果的呈现，验证了参考轨迹精度和跟踪时间的提升，以及理论论断的有效性。