A BIOLOGICALLY INSPIRED CONTROLLER FOR TRAJECTORY TRACKING OF FLEXIBLE-JOINT MANIPULATORS

Hamid Salmasi, Reza Fotouhi, and Peter N. Nikiforuk

Keywords

Flexiblejoint manipulators, biologically inspired swarm control, trajectory tracking

Abstract

This paper presents a new biologically inspired control strategy for trajectory tracking of flexible-joint manipulators. The social behavioural pattern of swarms such as bird flocks and fish schools is the inspiration to develop a new controller for flexible-joint manipulators in this study. This controller, referred to as swarm control, is a self-organized robust technique which is able to control simultaneously the rotations of the joints and the position of the robot end-effector (tip of the robot which interacts with environment). Therefore, in contrast to other studies in which the joints are individually controlled, and the performance of the controller is independent of the position of the end-effector, the swarm control takes into account the errors in the positions of the end-effector and the joints at the same time. In this control scheme, the parameters of the controller are updated every time-step based on their values at the previous time-step and also the feedbacks of the positions of the links and the end-effector. Along with the swarm control, a friction compensating torque which is based on the LuGre method is employed to counterbalance the effect of the friction in the joints. Verification of the proposed controller is performed using the experimental setup of a two-rigid-link flexible-joint. The experimental results show that the controller is successful in tip trajectory tracking at several different desired trajectories and at several different speeds, such that the tracking and the steady-state errors are almost zero during the manoeuvre of the manipulator and after reaching to the final desired position. Furthermore, comparison of swarm control with other controllers (e.g., the computed torque scheme) demonstrates the superiority of the swarm control strategy in reducing the trajectory tracking error during robot manoeuver.

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