Optimal design of a five-bar planar manipulator and its controller by using different algorithms for minimum shaking forces and moments for the largest trajectory in a usable workspace

Abstract

In this paper, a structural design and controller optimization process for a five-bar planar manipulator are studied using three different population-based optimization techniques: particle swarm optimization, genetic algorithm, and differential evolution. First, the desired kinematic properties of the manipulator, such as the position, velocity, and acceleration of the endpoint, are determined using inverse kinematics. Then, an optimization problem is created to minimize the shaking force and moments, and the desired kinematic quantities are implemented as constraints. All the link properties of the manipulator are defined as design variables, and the optimization results are obtained. The results show that it is possible to significantly reduce the shaking force and moment significantly thanks to the optimal design parameters. Finally, the controller is optimized to find the best PID gains considering the forward kinematics of the manipulator. It is observed that the shaking force and shaking moment can be reduced by 99% and 54%, respectively, which has a very positive effect on the accuracy of the trajectory tracking. Moreover, the performances of the optimization methods are compared by using the same number of iterations in the calculations, and thus, it can be seen that the GA method achieves the best results compared to the others. Therefore, the results of this study are of utmost importance for a manufacturer, who wants to design a five-bar planar manipulator and its controller.