New Hybrid Approach to Control the Arm of Flexible Robots by using Neural Networks, Fuzzy Algorithms and Particle Swarm Optimization Algorithm

Background/Objectives: Mechanizing the instruments is one of the most important and widespread fields which is used in the processes of production and control. Methods/Statistical Analysis: Given the complexity and distrust of mechanizing processes, soft computing techniques which are based on physical models have been preferred to common methods in order to predict the performance of processes and optimize them. Results: The combination of fuzzy logic and neural networks enables the system to have the capability of learning and adapting to the environment, as well as tolerating the imprecise circumstances which is an advantage of fuzzy logic methods. In this paper, a new hybrid approach is proposed to control the arm of flexible robots by using neural networks, fuzzy algorithms and particle swarm optimization algorithm. The objective is to control robot’s claw with two movable arms.