Minimum energy trajectory planning for vibration control of a flexible manipulator using a multi-objective optimisation approach

Abstract

In this study, we deal with a point-to-point motion task for a flexible manipulator and develop a minimum energy trajectory planning method for residual vibration suppression, in which soft computing techniques are used. An artificial neural network (ANN) is employed to generate the joint angle of the manipulator. For the ANN, we use a vector evaluated particle swarm optimisation (VEPSO) algorithm as the learning algorithm. The maximum residual vibration amplitude and the operating energy are adopted as multi-objective functions of the VEPSO algorithm. By operating the manipulator along the trajectory thus generated, the suppression of the residual vibration can be realised with minimum consumption of driving energy. In other words, the proposed method is an open-loop control that does not require sensors to measure unwanted vibrations. The performance of the proposed control scheme is confirmed by numerical simulation. In addition, the effectiveness of the proposed approach is experimentally verified.