Real-Time Experiments for Decentralized Adaptive Synchronized Motion Control of a Closed-Kinematic Chain Mechanism Robot Manipulator

This paper presents the results of real-time experiments conducted to evaluate the performance of a developed adaptive control scheme applied to control the motion of a real closed-kinematic chain mechanism (CKCM) robot manipulator with two degrees of freedom (DOFs). The developed control scheme, referred to as the decentralized adaptive synchronized control scheme (DASCS), was the result of the combination of model reference adaptive control (MRAC) based on the Lyapunov direct method and the synchronization technique. CKCM manipulators were considered in the experimental study due to their advantages over their open-kinematic chain mechanism (OKCM) manipulator counterparts, such as higher stiffness, better stability, and greater payload. The conducted computer simulation study showed that the DASCS was able to asymptotically converge tracking errors to zero, with all the active joints moving synchronously in a prescribed way. One of the important properties of the DASCS is the independence of robot manipulator dynamics, making it computationally efficient and therefore suitable for real-time applications. The present paper reports findings from experiments in which the DASCS was applied to control the above manipulator and carry out various paths. The DASCS’s performance was compared with that of a traditional adaptive control scheme, namely the SMRACS, when both schemes were applied to track the same paths.