Analysis And Comparison of Different Tuning Method of PID Control in Robot Manipulator
DOI:
https://doi.org/10.54097/hset.v71i.12373Keywords:
PID control, robot manipulator, Metaheuristic algorithms.Abstract
This paper provides a comprehensive examination of the background and theory of Proportional-Integral-Derivative (PID) control in robot manipulators. It not only compares the different tuning methods, but also offers analysis of the commonly used Metaheuristic algorithms tuning approaches employed by previous researchers. Furthermore, it discusses the challenges encountered and the potentials unlocked when applying PID control to robot manipulators, considering factors such as system complexity, robustness and stability. The insights gained from this research are of utmost significance for the continued development and industrial implementation of PID control in the field of robotics. By improving the understanding of PID control and its performance characteristics, this study contributes to enhancing the precision, stability, and adaptability of robot manipulators in various industries and applications, including manufacturing, automation, and autonomous systems. Overall, this paper sheds light on the future directions and potential advancements in PID control for robot manipulators.
Downloads
References
Yuval Zaidel, Albert Shalumov, Alex Volinski, Lazar Supic and Elishai Ezra Tsur1. Neuromorphic NEF-Based Inverse Kinematics and PID Control. Frontiers in Neurorobotics, 2021, 15.
Zhou Yi. A Summary of PID Control Algorithms Based on AI-Enabled Embedded Systems. Security and Communication Networks, 2022.
Wang Yong. Research on Nonlinear PID Control. Nanjing University of Science and Technology, 2000.
Ziegler and Nichols. Optimum settings for automatic controllers. Transactions of the ASME, 1995, 42(6): 94-100.
Stephen Bassi Joseph,Emmanuel Gbenga Dada Afeez Abidemi, David Opeoluwa Oyewola and Ban Mohammed Khammas. Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems. HELIYON, 2022, 8(5).
James Bennett, Ajay Bhasin, Jamila Grant and Wen Chung Lim. PID Tuning via Classical Methods. university of Michigan.
Marco Dorigo, Vittorio Maniezzo, Alberto Colorni. Ant system: optimization by a colony of cooperating agents. in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996, 26(1): 29-41.
Gaganpreet Kaur and Sankalap Arora. Chaotic whale optimization algorithm. Journal of Computational Design and Engineering, 2018, 5.
James Kennedy and Russell C. Eberhart. Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, 1995, 4: 1942-1948.
Fabian Viola. Simulated annealing simulated. Computers & Mathematics with Applications, 1997, 33(2): 81-94.
Vcerklr Reis, Maris Santos, Mars Carmo and Flastab Ferreira. Control of level systems by arduino via PC platform. 20th International Conference on System Theory, Control and Computing (ICSTCC), 2016, 251-256.
Diego Sandoval and Ismael Soto and Pablo Adasme. Control of direct current motor using Ant Colony optimization. 2015 Chilean Conference on Electrical, Electronics Engineering, Information and Communication Technologies, 2015, 79-82.
Loucif Fatiha, Kechida Sihem and Sebbagh Abdennour. Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator, 2019, 42(1).
Souza Darielson, Batista Josias, Nogueira Dos Reis, Laurinda Lucia, Junior Antonio Beira de Andrade, et al. PID controller with novel PSO applied to a joint of a robotic manipulator. Journal of the Brazilian society of mechanical sciences and engineering, 2021, 43(8).
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
