A. Abe and K. Komuro, “Trajectory Planning for Saving Energy of a Flexible Manipulator Using Soft Computing Methods,” Proceedings of International Conference on Control, Automation and Systems, Gyeonggi-Do, 27-30 October 2010, pp. 1462-1467.

has been cited by the following article:

• TITLE: Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot

  AUTHORS: Rega Rajendra, Dilip K. Pratihar

  KEYWORDS: Manipulator, Optimal Structure, Adaptive Controller, Genetic Algorithm, Neural Networks, Particle Swarm Optimization

  JOURNAL NAME: Intelligent Control and Automation, Vol.2 No.4, October 31, 2011

  ABSTRACT: The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.