Optimizing the Tuning of Fuzzy-PID Controllers for Motion Control of Friction Stir Welding Robots
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M. Soron and I. Kalaykov, “A Robot Prototype for Friction Stir Welding,” 2006 IEEE Conference on Robotics, Automation and Mechatronics, pp. 1-5, 2006, doi: 10.1109/RAMECH.2006.252646.
Z. Lin, C. Cui, and G. Wu, “Dynamic Modeling and Torque Feedforward based Optimal Fuzzy PD control of a High-Speed Parallel Manipulator,” Journal of Robotics and Control, vol. 2, no. 6, pp. 527–538, 2021, doi: 10.18196/jrc.26133.
A. Alipour, M. Mahjoob, and A. Nazarian, “A New 4-DOF Robot for Rehabilitation of Knee and Ankle-Foot Complex: Simulation and Experiment,” Journal of Robotics and Control, vol. 3, no. 4, pp. 483–495, 2022, doi: 10.18196/jrc.v3i4.14759.
H. R. Nohooji, “Constrained neural adaptive PID control for robot manipulators,” Journal of the Franklin Institute, vol. 357, no. 7, pp. 3907–3923, 2020, doi: 10.1016/j.jfranklin.2019.12.042.
C. E. Luis and A. P. Schoellig, “Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control,” in IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 375-382, 2019, doi: 10.1109/LRA.2018.2890572.
M. M. Ghazaei Ardakani, B. Olofsson, A. Robertsson and R. Johansson, “Model Predictive Control for Real-Time Point-to-Point Trajectory Generation,” in IEEE Transactions on Automation Science and Engineering, vol. 16, no. 2, pp. 972-983, 2019, doi: 10.1109/TASE.2018.2882764.
S. Xue, Z. Zhao, and F. Liu, “Latent variable point-to-point iterative learning model predictive control via reference trajectory updating,” European Journal of Control, vol. 65, 2022, doi: 10.1016/j.ejcon.2022.100631.
Y. Wang, K. Zhu, B. Chen, and H. Wu, “A New Model-Free Trajectory Tracking Control for Robot Manipulators,” Mathematical Problems in Engineering, vol. 2018, 2018, doi: 10.1155/2018/1624520.
X. Ma, Y. Zhao, and Y. Di, “Trajectory Tracking Control of Robot Manipulators Based on U-Model,” Mathematical Problems in Engineering, vol. 2020, 2020, doi: 10.1155/2020/8314202.
M. R. Khoshdarregi, S. Tappe and Y. Altintas, “Integrated Five-Axis Trajectory Shaping and Contour Error Compensation for High-Speed CNC Machine Tools,” in IEEE/ASME Transactions on Mechatronics, vol. 19, no. 6, pp. 1859-1871, 2014, doi: 10.1109/TMECH.2014.2307473.
S. Shentu, F. Xie, X.-J. Liu, and Z. Gong, “Motion Control and Trajectory Planning for Obstacle Avoidance of the Mobile Parallel Robot Driven by Three Tracked Vehicles,” Robotica, vol. 39, no. 6, pp. 1037–1050, 2021. doi:10.1017/S0263574720000880.
Y. Li and Q. Xu, “Design and Development of a Medical Parallel Robot for Cardiopulmonary Resuscitation,” in IEEE/ASME Transactions on Mechatronics, vol. 12, no. 3, pp. 265-273, 2007, doi: 10.1109/TMECH.2007.897257.
L. Angel and J. Viola, “Fractional order PID for tracking control of a parallel robotic manipulator type delta,” ISA Transactions, vol. 79, pp. 172–188, 2018, doi: 10.1016/j.isatra.2018.04.010.
A. A. El-samahy and M. A. Shamseldin, “Brushless DC motor tracking control using self-tuning Fuzzy PID control and model reference adaptive control,” Ain Shams Engineering Journal, vol. 9, no. 3, pp. 341–352, 2018, doi: 10.1016/j.asej.2016.02.004.
M. A. Shamseldin and A. A. EL-Samahy, “Speed control of BLDC motor by using PID control and self-tuning fuzzy PID controller,” 15th International Workshop on Research and Education in Mechatronics (REM), pp. 1-9, 2019, doi: 10.1109/REM.2014.6920443.
N. Farouk and T. Bingqi, “Application of self-tuning fuzzy PID controller on the AVR system,” 2012 IEEE International Conference on Mechatronics and Automation, pp. 2510-2514, 2012, doi: 10.1109/ICMA.2012.6285741.
C. -T. Chao, N. Sutarna, J. -S. Chiou, and C. -J. Wang, “An Optimal Fuzzy PID Controller Design Based on Conventional PID Control and Nonlinear Factors,” Applied Sciences, vol. 9, no. 6, 2019, doi: 10.3390/app9061224.
A. Ramya, A. Imthiaz, and M. Balaji, “Hybrid Self Tuned Fuzzy PID controller for speed control of Brushless DC Motor,” Automatika, vol. 57, no. 3, pp. 672–679, 2017, doi: 10.7305/automatika.2017.02.1769.
J. D. Backer, Feedback Control of Robotic Friction Stir Welding, Doctoral dissertation, University West, 2014.
I. F. Zidane, Y. Khattab, M. El-Habrouk, and S. Rezeka, “Trajectory control of a laparoscopic 3-PUU parallel manipulator based on neural network in SIMSCAPE SIMULINK environment,” Alexandria Engineering Journal, vol. 61, no. 12, pp. 9335–9363, 2022, doi: 10.1016/j.aej.2022.03.024.
X. Liu, J. Yao, Q. Li, and Y. Zhao, “Coordination dynamics and modelbased neural network synchronous controls for redundantly full-actuated parallel manipulator,” Mechanism and manipulator Theory, vol. 160, 2021, doi: 10.1016/j.mechmachtheory.2021.104284.
Z. Chen, Y. Liu, W. He, H. Qiao and H. Ji, “Adaptive-Neural-NetworkBased Trajectory Tracking Control for a Nonholonomic Wheeled Mobile Robot With Velocity Constraints,” in IEEE Transactions on Industrial Electronics, vol. 68, no. 6, pp. 5057-5067, 2021, doi: 10.1109/TIE.2020.2989711.
M. Mukhtar, D. Khudher, and T. Kalganova, “A control structure for ambidextrous robot arm based on Multiple Adaptive Neuro-Fuzzy Inference System,” IET Control Theory & Applications, vol. 15, no. 11, pp. 1518–1532, 2021, doi: 10.1049/cth2.12140.
M. R. A. Refaai, “Using Multiple Adaptive Neuro-Fuzzy Inference System to Solve Inverse Kinematics of SCARA Robot,” 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 154-159, 2021, doi: 10.1109/SSD52085.2021.9429498.
S. Gobinath and M. Madheswaran, “Deep perceptron neural network with Fuzzy PID controller for speed control and stability analysis of BLDC motor,” Soft Computing, vol. 24, no. 13, pp. 10161–10180, 2020, doi: 10.1007/s00500-019-04532-z.
A. Ramya, M. Balaji, and V. Kamaraj, “Adaptive MF tuned Fuzzy logic speed controller for BLDC motor drive using ANN and PSO technique,” The Journal of Engineering, vol. 2019, no. 17, pp. 3947–3950, 2019, doi: 10.1049/joe.2018.8179.
X. Wu, P. Jin, T. Zou, Z. Qi, H. Xiao, and P. Lou, “Backstepping Trajectory Tracking Based on Fuzzy Sliding Mode Control for Differential Mobile Robots,” Journal of Intelligent & Robotic Systems, vol. 96, no. 1, pp. 109–121, 2019, doi: 10.1007/s10846-019-00980-9.
J. Y. Zhai and Z. B. Song, “Adaptive sliding mode trajectory tracking control for wheeled mobile robots,” International Journal of Control, vol. 92, no. 10, pp. 2255–2262, 2019, doi: 10.1080/00207179.2018.1436194.
A. Ma’arif and A. C¸ akan, “Simulation and Arduino Hardware Implementation of DC Motor Control Using Sliding Mode Controller,” Journal of Robotics and Control, vol. 2, no. 6, pp. 582–587, 2021, doi: 10.18196/jrc.26140.
A. T. Azar, F. E. Serrano and A. Koubaa, “Adaptive Fuzzy Type-2 Fractional Order Proportional Integral Derivative Sliding Mode Controller for Trajectory Tracking of Robotic Manipulators,” 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 183-187, 2020, doi: 10.1109/ICARSC49921.2020.9096163.
M. Yang, Z. Du, L. Sun, and W. Dong, “Optimal design, modeling and control of a long stroke 3-PRR compliant parallel manipulator with variable thickness flexure pivots,” Robotics and Computer-Integrated Manufacturing, vol. 60, pp. 23–33, 2019, doi: 10.1016/j.rcim.2019.05.014.
C. Lauretti et al., “Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons,” Frontiers in Neurorobotics, vol. 12, 2018, doi: 10.3389/fnbot.2018.00005.
K. S. Devi, R. Dhanasekaran and S. Muthulakshmi, “Improvement of speed control performance in BLDC motor using fuzzy PID controller,” 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 380-384, 2016, doi: 10.1109/ICACCCT.2016.7831666.
J. Xu, L. Xiao, M. Lin, and X. Tan, “Application of Fuzzy PID Position Control Algorithm in Motion Control System Design of Palletizing Robot,” Security and Communication Networks, vol. 2022, pp. 1–11, 2022, doi: 10.1155/2022/8720960.
N. Y. Allagui, F. A. Salem, and A. M. Aljuaid, “Artificial Fuzzy-PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms,” Computational Intelligence and Neuroscience, vol. 2021, 2021, doi: 10.1155/2021/5542888.
S. Krishna and S. Vasu, “Fuzzy PID based adaptive control on industrial robot system,” Materials Today: Proceedings, vol. 5, no. 5, pp. 13055–13060, 2018, doi: 10.1016/j.matpr.2018.02.292.
M. Rabah, A. Rohan, Y. J. Han, and S. H. Kim, “Design of FuzzyPID Controller for Quadcopter Trajectory-Tracking,” The International Journal of Fuzzy Logic and Intelligent Systems, vol. 18, no. 3, pp. 204–213, 2018, doi: 10.5391/IJFIS.2018.18.3.204.
G. S. Maraslidis, T. L. Kottas, M. G. Tsipouras and G. F. Fragulis, “A Fuzzy Logic Controller for Double Inverted Pendulum on a Cart,” 2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), pp. 1-8, 2021, doi: 10.1109/SEEDA-CECNSM53056.2021.9566228.
A. M. S¸erifoglu and C. Kasnako ˘ glu, “Fuzzy Logic Controlled Active ˘ Hydro-Pneumatic Suspension Design Simulation and Comparison for Performance Analysis,” 2021 3rd International Congress on HumanComputer Interaction, Optimization and Robotic Applications (HORA), pp. 1-5, 2021, doi: 10.1109/HORA52670.2021.9461373.
N. H. Singh and K. Thongam, “Mobile Robot Navigation Using Fuzzy Logic in Static Environments,” Procedia Computer Science, vol. 125, pp. 11–17, 2018, doi: 10.1016/j.procs.2017.12.004.
R. Arulmozhiyal, “Design and Implementation of Fuzzy PID controller for BLDC motor using FPGA,” 2012 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), pp. 1-6, 2022, doi: 10.1109/PEDES.2012.6484251.
H. Maghfiroh, M. Ahmad, A. Ramelan, and F. Adriyanto, “FuzzyPID in BLDC Motor Speed Control Using MATLAB/Simulink,” Journal of Robotics and Control, vol. 3, no. 1, pp. 8–13, 2022, doi: 10.18196/jrc.v3i1.10964.
P. Chotikunnan, R. Chotikunnan, A. Nirapai, A. Wongkamhang, P. Imura, and M. Sangworasil, “Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques,” Journal of Robotics and Control, vol. 4, no. 2, pp. 128–140, 2023, doi: 10.18196/jrc.v4i2.18108.
R. Kristiyono and W. Wiyono, “Autotuning Fuzzy PID Controller for Speed Control of BLDC Motor,” Journal of Robotics and Control, vol. 2, no. 5, pp. 400–407, 2021, doi: 10.18196/jrc.25114.
D. Somwanshi, M. Bundele, G. Kumar, and G. Parashar, “Comparison of Fuzzy-PID and PID Controller for Speed Control of DC Motor using LabVIEW,” Procedia Computer Science, vol. 152, pp. 252–260, 2019, doi: 10.1016/j.procs.2019.05.019.
A. Dhyani, M. K. Panda, and B. Jha, “Moth-Flame Optimization-Based Fuzzy-PID Controller for Optimal Control of Active Magnetic Bearing System,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 42, no. 4, pp. 451–463, 2018, doi: 10.1007/s40998-018-0077-1.
A. Shuraiji and S. Shneen, “Fuzzy Logic Control and PID Controller for Brushless Permanent Magnetic Direct Current Motor: A Comparative Study,” Journal of Robotics and Control, vol. 3, no. 6, pp. 762–768, 2022, doi: 10.18196/jrc.v3i6.15974.
G. L. Demidova, D. V. Lukichev, and A. Y. Kuzin, “A Genetic Approach for Auto-Tuning of Adaptive Fuzzy PID Control of a Telescope’s Tracking System,” Procedia Computer Science, vol. 150, pp. 495–502, 2019, doi: 10.1016/j.procs.2019.02.084.
P. Duraisamy, M. N. Santhanakrishnan, and R. Amirtharajan, “Genetic Algorithm Optimized Grey-Box Modelling and Fuzzy Logic Controller for Tail-Actuated Robotic Fish,” Neural Processing Letters, vol. 55, no. 8, pp. 11577–11594, 2023, doi: 10.1007/s11063-023-11391-1.
C. Ntakolia, K. S. Platanitis, G. P. Kladis, C. Skliros and A. D. Zagorianos, “A Genetic Algorithm enhanced with Fuzzy-Logic for multi-objective Unmanned Aircraft Vehicle path planning missions,” 2022 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 114-123, 2022, doi: 10.1109/ICUAS54217.2022.9836068.
H. Hu, T. Wang, S. Zhao, and C. Wang, “Speed control of brushless direct current motor using a genetic algorithm–optimized Fuzzy proportional integral differential controller,” Advances in Mechanical Engineering, vol. 11, no. 11, 2019, doi: 10.1177/1687814019890199.
E. Pourjavad and R. V. Mayorga, “A comparative study and measuring performance of manufacturing systems with Mamdani Fuzzy inference system,” Journal of Intelligent Manufacturing, vol. 30, no. 3, pp. 1085–1097, 2019, doi: 10.1007/s10845-017-1307-5.
N. H. Singh and K. Thongam, “Mobile Robot Navigation Using Fuzzy Logic in Static Environments,” Procedia Computer Science, vol. 125, pp. 11–17, 2018, doi: 10.1016/j.procs.2017.12.004.
Z. Hou, Z. Li, C. Hsu, K. Zhang and J. Xu, “Fuzzy Logic-Driven Variable Time-Scale Prediction-Based Reinforcement Learning for Robotic Multiple Peg-in-Hole Assembly,” in IEEE Transactions on Automation Science and Engineering, vol. 19, no. 1, pp. 218-229, 2022, doi: 10.1109/TASE.2020.3024725.
R. D. Puriyanto and A. K. Mustofa, “Design and Implementation of Fuzzy Logic for Obstacle Avoidance in Differential Drive Mobile Robot,” Journal of Robotics and Control, vol. 5, no. 1, pp. 132–141, 2024, doi: 10.18196/jrc.v5i1.20524.
R. Hassan, B. Cohanim, and O. de Weck, “A Comparison of Particle Swarm Optimization and the Genetic Algorithm,” 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, vol. 2, 2005, doi: 10.2514/6.2005-1897.
Z. Wang, Q. Wang, D. He, Q. Liu, X. Zhu and J. Guo, “An Improved Particle Swarm Optimization Algorithm Based on Fuzzy PID Control,” 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 835-839, 2017, doi: 10.1109/ICISCE.2017.178.
Y. Liu et al., “Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm,” Frontiers in Bioengineering and Biotechnology, vol. 9, 2022, doi: 10.3389/fbioe.2021.817723.
J. Mendel, Uncertain Rule-Based Fuzzy Systems, Springer Cham, 2017, doi: 10.1007/978-3-319-51370-6.
C. Muresan and C. Ionescu, “Generalization of the FOPDT Model for Identification and Control Purposes,” Processes, vol. 8, no. 6, 2020, doi: 10.3390/pr8060682.
Q. Bi, W. J. Cai, E. L. Lee, Q. G. Wang, C. C. Hang, and Y. Zhang, “Robust identification of first-order plus dead-time model from step response,” Control Engineering Practice, vol. 7, no. 1, pp. 71–77, 1999, doi: 10.1016/S0967-0661(98)00166-X.
Z. Zhu et al., “Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm,” Applied Sciences, vol. 12, no. 21, 2022, doi: 10.3390/app122111128.
A. Singh and V. K. Giri, “Design and Analysis of DC Motor Speed Control by GA Based Tuning of Fuzzy Logic Controller,” International journal of engineering research and technology, vol. 1, 2012.
DOI: https://doi.org/10.18196/jrc.v5i4.21697
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