Analyzing stability and structural aspects of embedded fuzzy type 2 PID controller for robot manipulators

Author:

Venkataramanan K.1,Arun M.2,Jha Shankaranand3,Sharma Aditi4

Affiliation:

1. Department of Electrical and Electronics Engineering, Prathyusha Engineering College, Chennai, Tamilnadu, India

2. Department of ECE, Panimalar Engineering College, Chennai, Tamilnadu, India

3. Department of Electronics and Communication Engineering, Malla Reddy Engineering College, Secunderabad, India

4. Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune Maharashtra, India

Abstract

This study delves into the development and analysis of a novel Embedded Fuzzy Type 2 PID Controller for Robot Manipulators, motivated by the increasing need for enhanced control systems in robotic applications to improve precision and stability. In the background section, the limitations of conventional PID controllers in addressing uncertainties and disturbances, especially in complex tasks performed by robot manipulators, are presented. The concept of fuzzy logic and the Type 2 fuzzy system is introduced, highlighting their potential to manage imprecise and uncertain information. Through rigorous analysis and simulation, the superior performance of the Embedded Fuzzy Type 2 PID Controller is demonstrated when compared to traditional PID controllers and even Type 1 fuzzy controllers. The results showcase enhanced tracking accuracy, disturbance rejection, and adaptability, making it a promising solution for advanced robotic applications. In conclusion, this research provides a robust solution for improving the control of robot manipulators in uncertain and dynamic environments. The Embedded Fuzzy Type 2 PID Controller offers a new paradigm in control theory, ensuring stability and precision even in the face of unpredictable factors. This innovation holds great promise for advancing the capabilities of robotic systems and underlines the potential for further research in embedded fuzzy control systems.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference24 articles.

1. Real-time path planning for autonomous robots in unknown and dynamic environments;Giusti;Autonomous Robots,2018

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3. Visual SLAM and autonomous navigation for mobile robots: A review;Khusainov;Robotics and Autonomous Systems,2019

4. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates;Wang;IEEE Robotics and Automation Letters,2020

5. An autonomous navigation and obstacle avoidance system for mobile robots using lidar and ultrasonic sensors;Zhou;Journal of Intelligent & Robotic Systems,2020

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