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

Abstract

Faulty joints in a robot manipulator adversely affect the tracking control performance and compromise the system’s stability; therefore, it is necessary to design a control system capable of compensating for the effects of actuator faults to maintain control efficacy. To this end, this paper presents new amendments to the dynamical formulation of robot manipulators to account for latent actuator faults and over-generated torques mathematically. Subsequently, a novel auto-tuning subsystem-based fault-tolerant control (SBFC) mechanism is designed to force joints’ states closely along desired trajectories, while tolerating actuator faults, excessive torques, and unknown modeling errors. Suboptimal SBFC gains are determined by employing the JAYA algorithm (JA), a high-performance swarm intelligence technique, standing out for its capacity to continuously approach optimal control levels without requiring 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 simulation outcomes.