  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.
