Adaptive Fuzzy Control of Robot Manipulators with Asymptotic Tracking Performance

Adaptive fuzzy controllers are powerful intelligent control tools applied to handle the complexity of robot manipulators; however, their tracking performances are considerably affected by the fuzzy approximation error. This paper introduces a decentralized adaptive fuzzy control (AFC) design based on voltage control strategy, equipped with a novel fuzzy approximation error compensator. Asymptotic tracking performance is achieved by means of compensating the fuzzy approximation error adaptively. The proposed design simplifies the complex control procedure of electrically driven robot manipulators, and it is independent from the mathematical model. The asymptotic stability of the proposed controller has been guaranteed and proven via Barbalat’s lemma. The control approach is simulated on a four-link SCARA robot driven by permanent magnet DC motors, and the simulation results reveal the improvement in tracking the desired trajectory, compared to a previous AFC based on voltage control strategy without fuzzy approximation error compensation.