PUMA 560 Trajectory Control Using NSGA-II Technique With Real Valued Operators

TL;DR

This paper applies the NSGA-II multi-objective genetic algorithm with real-valued operators to optimize the control gains of a PUMA 560 robotic arm, demonstrating improved performance in minimizing trajectory errors.

Contribution

It introduces the use of real-valued recombination and mutation operators in NSGA-II for tuning robot controller gains, enhancing optimization effectiveness.

Findings

Real-valued operators improve NSGA-II performance

Optimized controller gains reduce trajectory errors

Simultaneous minimization of multiple cost functions achieved

Abstract

In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems.The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.