Robust Adaptive Tracking Control for Uncertain Five-Bar Parallel Robot Using Fuzzy CMAC in Order to Improve Accuracy
Abstract
Parallel robot systems are increasingly important and widely applied due to their superior advantages such as high speed and accuracy. To improve the accuracy of these systems, recent research has focused on developing advanced control methods. However, this remains a significant challenge due to the complex mathematical model of parallel robots. This study introduces a control system based on a fuzzy cerebellar model articulation controller (FCMAC) to control parallel robots. The proposed control system includes FCMAC as the main tracking controller used to estimate the ideal control. A robust controller is employed to compensate for the error between FCMAC and the ideal controller. The parameters of FCMAC are adjusted online based on adaptive laws derived from Lyapunov functions. Finally, a five-bar parallel robot is selected to experiment with the FCMAC algorithm to demonstrate the effectiveness of the proposed controller. The results show that the accuracy of FCMAC is better than that of other algorithms.
Keywords
Full Text:
PDFReferences
Y. Cao, W. Jiang, and J. Wang, “Anti-synchronization of delayed memristive neural networks with leakage term and reaction–diffusion terms,” Knowledge-Based Systems, vol. 233, p. 107539, 2021.
F. Chao, D. Zhou, C. -M. Lin, L. Yang, C. Zhou, and C. Shang, “Type-2 Fuzzy Hybrid Controller Network for Robotic Systems,” in IEEE Transactions on Cybernetics, vol. 50, no. 8, pp. 3778-3792, Aug. 2020, doi: 10.1109/TCYB.2019.2919128.
C.-F. Hsu, B.-R. Chen, and B.-F. Wu, “Broad-learning recurrent Hermite neural control for unknown nonlinear systems,” Knowledge-Based Systems, vol. 242, p. 108263, 2022.
B. Ma, Y. Li, T. An, and B. Dong, “Compensator-critic structure-based neurooptimal control of modular robot manipulators with uncertain environmental contacts using non-zero-sum games,” Knowledge-Based Systems, vol. 224, p. 107100, 2021.
L. Wang, K. Zeng, C. Hu, and Y. Zhou, “Multiple finite-time synchronization of delayed inertial neural networks via a unified control scheme,” Knowledge-Based Systems, vol. 236, p. 107785, 2022.
Y. Wang, J. Sun, H. He, and C. Sun, “Deterministic Policy Gradient With Integral Compensator for Robust Quadrotor Control,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 10, pp. 3713-3725, Oct. 2020, doi: 10.1109/TSMC.2018.2884725.
J. Xiao, Y. Li, and S. Wen, “Mittag-Leffler synchronization and stability analysis for neural networks in the fractional-order multi-dimension field,” Knowledge-Based Systems, vol. 231, p. 107404, 2021.
Y. Yue, H. Yang, F. Liu, and H. Zang, “Cooperative control for multiple quadrotors under position deviations and aerodynamic drag,” Mechanical Systems and Signal Processing, vol. 147, p. 107096, 2021.
J. Y. Lau, W. Liang, and K. K. Tan, “Motion Control for Piezoelectric-Actuator-Based Surgical Device Using Neural Network and Extended State Observer,” in IEEE Transactions on Industrial Electronics, vol. 67, no. 1, pp. 402-412, Jan. 2020, doi: 10.1109/TIE.2019.2897542.
G. Tao, S. Chen, X. Tang, and S. M. Joshi, “Adaptive control of systems with actuator failures,” Springer Science & Business Media, 2013.
X. Zhang, J. Ma, Z. Cheng, S. Huang, S. S. Ge, and T. H. Lee, "Trajectory Generation by Chance-Constrained Nonlinear MPC With Probabilistic Prediction," in IEEE Transactions on Cybernetics, vol. 51, no. 7, pp. 3616-3629, 2021, doi: 10.1109/TCYB.2020.3032711.
L. Kong and J. Yuan, “Disturbance-observer-based fuzzy model predictive control for nonlinear processes with disturbances and input constraints,” ISA Transactions, vol. 90, pp. 74–88, 2019.
W. Gu, J. Yao, Z. Yao, and J. Zheng, “Output feedback model predictive control of hydraulic systems with disturbances compensation,” ISA Transactions, vol. 88, pp. 216–24, 2019.
J. Ma, Z. Cheng, X. Zhang, M. Tomizuka, and T. H. Lee, “Optimal Decentralized Control for Uncertain Systems by Symmetric Gauss–Seidel Semi-Proximal ALM,” in IEEE Transactions on Automatic Control, vol. 66, no. 11, pp. 5554-5560, Nov. 2021, doi: 10.1109/TAC.2021.3052768.
W. Liang, S. Huang, S. Chen, and K. K. Tan, “Force estimation and failure detection based on disturbance observer for an ear surgical device,” ISA Transactions, vol. 66, pp. 476–84, 2017.
J. Ma, S. L. Chen, C. S. Teo, A. Tay, A. Al Mamun, and K. K. Tan, “Parameter space optimization towards integrated mechatronic design for uncertain systems with generalized feedback constraints,” Automatica, vol. 105, pp. 149–58, 2019.
J. Ma, Z. Cheng, H. Zhu, X. Li, M. Tomizuka, and T. H. Lee, “Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System,” in IEEE Transactions on Industrial Electronics, vol. 69, no. 4, pp. 3798-3809, April 2022, doi: 10.1109/TIE.2021.3070518.
X. Zhang, W. Jiang, Z. Li, and S. Song, “A hierarchical Lyapunov-based cascade adaptive control scheme for lower-limb exoskeleton,” European Journal of Control, vol. 50, pp.198–208, 2019.
C. Shen, Y. Shi, and B. Buckham, “Trajectory Tracking Control of an Autonomous Underwater Vehicle Using Lyapunov-Based Model Predictive Control,” in IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5796-5805, July 2018, doi: 10.1109/TIE.2017.2779442.
Y. Wang, J. Chen, F. Yan, K. Zhu, and B. Chen, “Adaptive super-twisting fractional-order nonsingular terminal sliding mode control of cable-driven manipulators,” ISA Trans., vol. 86, pp. 163–180, 2019.
J. Y. Lau, W. Liang, and K. K. Tan, “Adaptive sliding mode enhanced disturbance observer-based control of surgical device,” ISA Transactions, vol. 90, pp. 178–188, 2019.
W. Fang et al., “Visual-Guided Robotic Object Grasping Using Dual Neural Network Controllers,” in IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 2282-2291, March 2021, doi: 10.1109/TII.2020.2995142.
X. Guan, J. Hu, J. Qi, D. Chen, F. Zhang, and G. Yang, “Observer-based hsliding mode control for networked systems subject to communication channel fading and randomly varying nonlinearities,” Neurocomputing, vol. 437, pp. 312–324, 2021.
S. He, Y. Xu, Y. Wu, Y. Li, and W. Zhong, “Adaptive consensus tracking of multirobotic systems via using integral sliding mode control,” Neurocomputing, vol. 455, pp. 154-162, 2021.
Y. Ren, W. Zhou, Z. Li, L. Liu, and Y. Sun, “Prescribed-time leader-following consensus for stochastic second-order multi-agent systems subject to actuator failures via sliding mode control strategy,” Neurocomputing, vol. 425, pp. 82–95, 2021.
W. Wang, X. Jia, Z. Wang, X. Luo, L. Li, J. Kurths, and M. Yuan, “Fixed-time synchronization of fractional order memristive MAM neural networks by sliding mode control,” Neurocomputing, vol. 401, pp. 364–376, 2020.
W. Ji, J. Qiu, and H. R. Karimi, “Fuzzy-Model-Based Output Feedback Sliding-Mode Control for Discrete-Time Uncertain Nonlinear Systems,” in IEEE Transactions on Fuzzy Systems, vol. 28, no. 8, pp. 1519-1530, Aug. 2020, doi: 10.1109/TFUZZ.2019.2917127.
T. Q. Ngo, T. H. Tran, T. T. H. Le, and B. M. Lam, “An Application of Modified T2FHC Algorithm in Two-Link Robot Controller,” Journal of Robotics and Control (JRC), vol. 4, no. 4, Aug. 2023
X. Li, Z. Zhu, N. Shen, W. Dai, and Y. Hu, “Deeply feature learning by CMAC network for manipulating rehabilitation robots,” Future Generation Computer Systems, vol. 121, pp. 19-24, 2021.
N. Tan, C. Li, P. Yu, and F. Ni, “Two model-free schemes for solving kinematic tracking control of redundant manipulators using CMAC networks,” Applied Soft Computing, vol. 126, p. 109267, 2022.
E. Stingu and F. L. Lewis, “An approximate Dynamic Programming based controller for an underactuated 6DoF quadrotor,” 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pp. 271-278, 2011, doi: 10.1109/ADPRL.2011.5967394.
Z. Zhong, H. -K. Lam, H. Ying, and G. Xu, “CMAC-Based SMC for Uncertain Descriptor Systems Using Reachable Set Learning,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 2, pp. 693-703, Feb. 2024.
T. Q. Ngo, M. K. Duong, D. C. Pham, and D.-N. Nguyen, “Adaptive Wavelet CMAC Tracking Control for Induction Servomotor Drive System,” Journal of Electrical Engineering & Technology, vol. 14, no. 1, pp. 209-218, 2019.
T. -T. Huynh et al., “A New Self-Organizing Fuzzy Cerebellar Model Articulation Controller for Uncertain Nonlinear Systems Using Overlapped Gaussian Membership Functions,” in IEEE Transactions on Industrial Electronics, vol. 67, no. 11, pp. 9671-9682, Nov. 2020.
T. Q. Ngo, D. K. Hoang, T. T. Tran, T. T. Nguyen, V. T. Nguyen, and L. H. Le, “A Novel Self-Organizing Fuzzy Cerebellar Model Articulation Controller Based Overlapping Gaussian Membership Function for Controlling Robotic System,” International Journal Of Computers Communications & Control, vol. 17, no. 4, 4606, 2022.
T. Q. Ngo, T. T. H. Le, B. M. Lam, and T. K. Pham, “Adaptive Single-Input Recurrent WCMAC-Based Supervisory Control for De-icing Robot Manipulator,” Journal of Robotics and Control (JRC), vol. 4, no. 4, July 2023.
Z. Piao, C. Guo, and S. Sun, “Adaptive Backstepping Sliding Mode Dynamic Positioning System for Pod Driven Unmanned Surface Vessel Based on Cerebellar Model Articulation Controller,” in IEEE Access, vol. 8, pp. 48314-48324, 2020.
H. Wu, H. An, Q. Wei, and H. Ma, “Fuzzy CMAC-Based Adaptive Scale Force Control of Body Weight Support Exoskeletons,” in IEEE Access, vol. 8, pp. 147286-147294, 2020.
C. Dong, Z. Yu, X. Chen, H. Chen, Y. Huang, and Q. Huang, “Adaptability Control Towards Complex Ground Based on Fuzzy Logic for Humanoid Robots,” in IEEE Transactions on Fuzzy Systems, vol. 30, no. 6, pp. 1574-1584, June 2022.
C. -F. Juang, C. -H. Lu, and C. -A. Huang, “Navigation of Three Cooperative Object-Transportation Robots Using a Multistage Evolutionary Fuzzy Control Approach,” in IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3606-3619, May 2022.
N. Tan, Z. Ye, P. Yu, and F. Ni, “A Dual Fuzzy-Enhanced Neurodynamic Scheme for Model-Less Kinematic Control of Redundant and Hyperredundant Robots,” in IEEE Transactions on Fuzzy Systems, vol. 30, no. 10, pp. 4409-4422, Oct. 2022.
S. Xu and B. He, “Robust Adaptive Fuzzy Fault Tolerant Control of Robot Manipulators With Unknown Parameters,” in IEEE Transactions on Fuzzy Systems, vol. 31, no. 9, pp. 3081-3092, Sept. 2023.
Q. Lin, X. Chen, C. Chen, and J. M. Garibaldi, “A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty,” in IEEE Transactions on Fuzzy Systems, vol. 31, no. 8, pp. 2532-2544, Aug. 2023, doi: 10.1109/TFUZZ.2022.3228332.
Y. Ren, Z. Zhao, C. K. Ahn, and H. -X. Li, “Adaptive Fuzzy Control for an Uncertain Axially Moving Slung-Load Cable System of a Hovering Helicopter With Actuator Fault,” in IEEE Transactions on Fuzzy Systems, vol. 30, no. 11, pp. 4915-4925, Nov. 2022, doi: 10.1109/TFUZZ.2022.3164512.
B. Zhu, Y. Wang, H. Zhang, and X. Xie, “Fuzzy Functional Observer-Based Finite-Time Adaptive Sliding-Mode Control for Nonlinear Systems With Matched Uncertainties,” in IEEE Transactions on Fuzzy Systems, vol. 30, no. 4, pp. 918-932, April 2022, doi: 10.1109/TFUZZ.2021.3050846.
W. Zhao, Y. Liu, and X. Yao, “Adaptive Fuzzy Containment and Vibration Control for Multiple Flexible Manipulators With Model Uncertainties,” in IEEE Transactions on Fuzzy Systems, vol. 31, no. 4, pp. 1315-1326, April 2023, doi: 10.1109/TFUZZ.2022.3199573.
F. Ardilla, M. I. Mas’udi, and I. K. Wibowo, “Fusion of Feedforward and Feedback Control Using Fuzzy for Active Handling and Dribbling System in MSL Robot Soccer,” International Journal on Advance Science, Engineering and Information Technology, vol. 9, no. 6, pp. 1950-1958, 2019.
S. Wang, “Mobile Robot Path Planning based on Fuzzy Logic Algorithm in Dynamic Environment,” 2022 International Conference on Artificial Intelligence in Everything (AIE), pp. 106-110, 2022, doi: 10.1109/AIE57029.2022.00027.
I. Reguii, I. Hassani, and C. Rekik, “Neuro-fuzzy Control of a Mobile Robot,” 2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 45-50, 2022, doi: 10.1109/STA56120.2022.10018999.
S. Hanana, N. Krichen, and M. S. Masmoudi, “Dual Fuzzy Logic Controllers for Omnidirectional Robot Navigation and Obstacle Avoidance,” 2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 100-105, 2022, doi: 10.1109/STA56120.2022.10019080.
R. K. Ikbar, E. Mulyana, R. Mardiati, and R. R. Nurmalasari, “Fire Fighting Robot Using Flame Detector and Ultrasonic Based on Fuzzy Logic Control,” 2022 16th International Conference on Telecommunication Systems, Services, and Applications (TSSA), pp. 1- 6, 2022, doi: 10.1109/TSSA56819.2022.10063922.
C. -M. Lin, M. -S. Yang, F. Chao, X. -M. Hu, and J. Zhang, “Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 10, pp. 2084-2094, Oct. 2016, doi: 10.1109/TNNLS.2015.2491305.
W. Fang et al., “A recurrent emotional CMAC neural network controller for vision-based mobile robots,” Neurocomputing, vol. 334, pp. 227–238, Mar. 2019.
C. Lin, Y. Liu, and H. Li, “SoPC-based function-link cerebellar model articulation control system design for magnetic ball levitation systems,” IEEE Trans. Ind. Electron., vol. 61, no. 8, pp. 4265–4273, Aug. 2014.
H. Ying, F. Lin, and R. Sherwin, “Fuzzy discrete event systems with gradient-based online learning,” in Proc. FUZZ-IEEE Conf., pp. 1–6, 2019.
C. Lin, R. Ramarao, and S. Gopalai, “Self-organizing adaptive fuzzy brain emotional learning control for nonlinear systems,” Int. J. Fuzzy Syst., vol. 21, no. 7, pp. 1989–2007, 2019.
X. Jing and L. Cheng, “An optimal PID control algorithm for training feedforward neural networks,” IEEE Trans. Ind. Electron., vol. 60, no. 6, pp. 2273–2283, Jun. 2013.
C. C. Jou, “A fuzzy cerebellar model articulation controller,” 1992 Proceedings, IEEE International Conference on Fuzzy Systems, pp. 1171-1178, 1992, doi: 10.1109/FUZZY.1992.258722.
J. Guan, C. -M. Lin, G. -L. Ji, L. -W. Qian, and Y. -M. Zheng, “Robust Adaptive Tracking Control for Manipulators Based on a TSK Fuzzy Cerebellar Model Articulation Controller,” in IEEE Access, vol. 6, pp. 1670-1679, 2018, doi: 10.1109/ACCESS.2017.2779940.
A. Mohammadzadeh and S. Ghaemi, “Optimal synchronization of fractional-order chaotic systems subject to unknown fractional order, input nonlinearities and uncertain dynamic using type-2 fuzzy CMAC,” Nonlinear Dynam., vol. 88, pp. 2993–3002, 2017.
C.-M. Lin, T.-T. Huynh, and T.-L. Le, “Adaptive TOPSIS fuzzy CMAC backstepping control system design for nonlinear systems,” Soft Computing, vol. 23, pp. 6947–66, 2019.
L. Cheng, Y. Lin, Z. -G. Hou, M. Tan, J. Huang, and W. J. Zhang, "Adaptive Tracking Control of Hybrid Machines: A Closed-Chain Five-Bar Mechanism Case," in IEEE/ASME Transactions on Mechatronics, vol. 16, no. 6, pp. 1155-1163, Dec. 2011.
DOI: https://doi.org/10.18196/jrc.v5i3.21742
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Thanh Quyen Ngo, Thanh Hai Tran, Tong Tan Hoa Le
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Journal of Robotics and Control (JRC)
P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com