Comparison of GA tuned fuzzy logic and NARMA-L2 controllers for motion control in 5-DOF robot

Abstract

Robots are useful in industries in many ways. In today’s economy, the manufacturing industry needs to be efficient to cope with the competition. Installing robots in the industry is often a step to be more competitive because robots can do certain tasks more efficiently than humans. Some of the manufacturing tasks in which robots perform better are assembling products, polishing, and cutting. In order to accurately perform these industrial operations, an effective controller needs to be implemented instead of conventional Proportional plus Integral (PI) controller. When a fuzzy controller is implemented directly, there will be a problem of computational complexity. Therefore, soft computing-based approach namely, genetic-fuzzy systems are proposed in this paper. Fuzzy systems have been integrated with Genetic Algorithm (GA) to optimize the scaling factors that define the fuzzy systems. GAs inspired by the process of biological evolution, are adaptive search and optimization algorithms. A system to be optimized is represented by a binary string which encodes the parameters of the system. This methodology is highly robust and imprecision tolerant. If a unique optimum exists, the procedure approaches it through gradual improvement of the fitness and if the optimum is not unique, the method will approach one of the optimum solutions. Hence, GA tuned fuzzy system is proposed and compared with NARMA-L2 controller for controlling the motion of a 5DOF robotic manipulator. This analysis has been performed using SOLIDWORKS and MATLAB/SIMULINK environments.

Keywords:

• 5-DOF robotic manipulator
• proportional plus integral controller
• fuzzy logic controller
• genetic algorithm tuned fuzzy logic controller
• NARMA-L2 controller
• computational intelligence

Acknowledgment

The authors are grateful for the research support rendered by BITS Pilani, Dubai Campus.