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An auto-tuning method of PID controllers for robot manipulators using multi-objective optimization technique is proposed. Two approaches are introduced based on the multi-objective particle swarm optimization (MOPSO) and multi-objective cuckoo search (MOCS), respectively. The main goal of this work is to introduce a comparative study on the performance of both algorithms with respects to their applicability to the auto-tuning process. For this sake, necessary metrics are considered such as the hyperarea difference and the overall Pareto spread, among others. In order to generate a sufficient amount of statistical data, a simulation of the robot Puma 560 is implemented. Using a relatively accurate model of the robot dynamics, a PID controller is applied and an optimization problem is configured. Two objective functions are defined, namely the integral of absolute error and the variance of control action. In addition, two constraints are considered regarding the maximal position error and maximal motor torque. After defining the optimization problem, the two algorithms are implemented as auto-tuning methods of the controller gains. Execution of the tuning process is repeated 30 times to test the statistical power of the obtained results. After that, an experiment on a real robot is performed to gain an overview on the practical application of the proposed method. Finally, the performance of both algorithms are compared and conclusions about the efficiency of each one are made.

A Comparative Study on the Performance of MOPSO and MOCS as Auto-tuning Methods of PID Controllers for Robot Manipulators