Exponential Auto-Tuning Fault-Tolerant Control of N Degrees-of-Freedom Manipulators Subject to Torque Constraints

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.