Intelligent control of biped robots: optimal fuzzy tracking control via multi-objective particle swarm optimization and genetic algorithms

Document Type : Research Article

Authors

1 Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran.

2 Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA.

10.22060/ajme.2019.16171.5808

Abstract

This paper is concerned with fuzzy tracking control optimized via multi-objective particle swarm optimization for stable walking of biped robots. To present an optimal control approach, multi-objective particle swarm optimization (PSO) is used to design the parameters of the control method in comparison to three effectual multi-objective optimization algorithms in the literature, i.e. MATLAB Toolbox MOGA, Modified NSGA II, and the Sigma method. In PSO, a dynamic elimination technique is utilized as a novel approach to prune the archive effectively. Moreover, a turbulence operator is used to skip the local optima and the personal best position of each particle is determined by making use of the Sigma method. Normalized summation of angles errors and normalized summation of control efforts are two conflicting objective functions addressed by dint of multi-objective optimization algorithms in the present investigation. By contrasting the Pareto front of multi-objective particle swarm optimization with the Pareto fronts of MATLAB Toolbox MOGA, Modified NSGA II, and the Sigma method, it is illustrated that multi-objective PSO performs with high accuracy, convergence and diversity of solutions in the design of fuzzy tracking control for nonlinear dynamics of biped robots. Finally, the proper performance of the proposed controller is demonstrated by the results presenting an appropriate tracking system and optimal control inputs. Indeed, the appropriate tracking system and optimal control inputs prove the efficiency of optimal fuzzy tracking control in dealing with the nonlinear dynamics of biped robots.

Keywords

Main Subjects

AUT Journal of Mechanical Engineering
Volume 4, Issue 2
Spring 2020
Pages 183-192

Files

Share

How to cite

Statistics