Developing an Advanced Control System to Enhance Precision in Uncertain Conditions for Five-Bar Parallel Robot Through a Combination of Robust Adaptive Tracking Control Using CMAC

Tong Tan Hoa Le, Thanh Quyen Ngo, Thanh Hai Tran

Abstract


Parallel robot systems have become increasingly applied due to significant advantages such as fast operating speed and high accuracy. Researchers are currently focusing on developing advanced control methods to increase the accuracy of this system. However, these advances face many challenges, including system dynamics and uncertain components in impact factors. Therefore, achieving a high level of accuracy remains a challenging problem and requires continued effort and careful research. This study proposes to use the Cerebellar Model Articulation Controller (CMAC) to estimate the nonlinear components of the system. By applying Lyapunov theory, this method focuses on adapting CMAC's online learning rules while ensuring stability and convergence. Besides using CMAC, the paper proposes a new signed distance method instead of sliding mode control (SMC) to handle input errors. This method aims to increase flexibility and adaptability and overcome the chattering of SMC in nonlinear systems. In particular, the research also adds a robust controller to ensure stability using Lyapunov to improve the system's accuracy. These recommendations increase the flexibility and accuracy of the control system, helping the system respond more quickly to changes and uncertainties in the operating environment. Finally, to demonstrate the effectiveness of the proposed controller, a five-bar parallel robot was chosen to conduct experiments in case situations. The results show that the proposed controller combined with signed distance achieves higher accuracy than other algorithms and is more stable in all cases mentioned in the research.

Keywords


Cerebellar Model Articulation Controller; Adaptive Control; Robust Control; Signed Distance; Five-Bar Parallel Robot.

Full Text:

PDF

References


J. Y. Lau, W. Liang, and K. K. Tan, “Motion control for piezoelectric-actuator-based surgical device using neural network and extended state observer,” IEEE Trans Ind. Electron., vol. 67, no. 1, pp. 402 -412, 2019.

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,” IEEE Trans Cybern., vol. 51, no. 7, pp. 3616-3629, 2020.

L. Kong and J. Yuan, “Disturbance-observer-based fuzzy model predictive control for nonlinear processes with disturbances and input constraints,” ISA Trans., 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 Trans, vol. 88, pp. 216–224, 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,” IEEE Trans Automat. Control, vol. 66, no. 11, pp. 5554-5560, 2021.

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 Trans, vol. 66, pp. 476–84, 2017.

J. Ma, S. -L. Chen, S. 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,” IEEE Trans Ind. Electron., vol. 69, no. 4, pp. 3798-3809, 2021.

X. Zhang, W. Jiang, Z. Li, and S. Song, “A hierarchical Lyapunov-based cascade adaptive control scheme for lower-limb exoskeleton,” Eur. J. Control, vol. 5, 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,” IEEE Trans Ind. Electron., vol. 65, .no7, pp. 5796–5805, 2017.

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. 163–80, 2019.

J. Y. Lau, W. Liang, and K. K. Tan, “Adaptive sliding mode enhanced disturbance observer-based control of surgical device,” ISA Trans, vol. 90, pp. 178–88, 2019.

C. L. P. Chen, Y.-J. Liu, and G.-X. Wen, “Fuzzy neural networkbased adaptive control for a class of uncertain nonlinear stochastic systems,” IEEE Trans. Cybern., vol. 44, no. 5, pp. 583–593, May 2014.

C.-T. Lin, M. Prasad, and A. Saxena, “An improved polynomial neural network classifier using real-coded genetic algorithm,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 45, no. 11, pp. 1389–1401, Nov. 2015.

D. Li, C. L. P. Chen, Y. Liu, and S. Tong, “Neural network controller design for a class of nonlinear delayed systems with time-varying fullstate constraints,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 9, pp. 2625–2636, Sep. 2019.

C. Lin and C. F. Hsu, “Neural-network hybrid control for antilock braking systems,” IEEE Trans. Neural Netw., vol. 14, no. 2, pp. 351–359, Mar. 2003.

Y. Liu, S. Liu, Y. Wang, F. Lombardi, and J. Han, “A survey of stochastic computing neural networks for machine learning applications,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 7, pp. 2809–2824, Jul. 2021.

Y.-J. Liu, L. Tang, S. Tong, and C. L. P. Chen, “Adaptive NN controller design for a class of nonlinear MIMO discrete-time systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 5, pp. 1007–1018, May 2015.

R. Rahimilarki, Z. Gao, A. Zhang, and R. Binns, “Robust neural network fault estimation approach for nonlinear dynamic systems with applications to wind turbine systems,” IEEE Trans. Ind. Informat., vol. 15, no. 12, pp. 6302–6312, Dec. 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.

G. Liu, S. Xu, Y. Wei, Z. Qi, and Z. Zhang, “New insight into reachable set estimation for uncertain singular time-delay systems,” Appl. Math. Comput., vol. 320, pp. 769–780, Mar. 2018.

S. Yin, H. Gao, J. Qiu, and O. Kaynak, “Descriptor reduced-order sliding mode observers design for switched systems with sensor and actuator faults,” Automatica, vol. 76, pp. 282–292, Feb. 2017.

M. Chadli and M. Darouach, “Novel bounded real lemma for discrete-time descriptor systems: Application to H∞ control design,” Automatica, vol. 48, no. 2, pp. 449–453, 2012.

Q. Huang, Y. H. Chen, and A. Cheng, “Adaptive robust control for fuzzy mechanical systems: constraint-following and redundancy in constraints,” IEEE Trans. Fuzzy Syst, vol. 23, no. 4, pp. 1113–1126, 2015.

S. C. Zhen, X. Peng, X. L. Liu, H. M. Li, and Y. H. Chen, “A new PD based robust control method for the robot joint module,” Mech. Syst. Sig. Process, vol. 161, 2021.

X. Chen, W. Liang, H. Zhao, and A. A. Mamun, “Adaptive robust controller using intelligent uncertainty observer for mechanical systems under non-holonomic reference trajectories,” ISA Trans, vol. 122, pp. 79–87, 2022.

N. Zhou, X. Cheng, Z. Sun, and Y. Xia, “Fixed-time cooperative behavioral control for networked autonomous agents with second-order nonlinear dynamics,” IEEE Trans. Cybern, vol. 52, no. 9, pp. 9504–9518, 2022.

L. Xiao, S. Li, K. Li, L. Jin, and B. Liao, “Co-Design of Finite-Time Convergence and Noise Suppression: A Unified Neural Model for Time Varying Linear Equations With Robotic Applications,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 12, pp. 5233-5243, Dec. 2020, doi: 10.1109/TSMC.2018.2870489.

Z. Lu, Q. Ge, Y. Li, and J. Hu, “Finite-Time Synchronization of Memristor-Based Recurrent Neural Networks With Inertial Items and Mixed Delays,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 5, pp. 2701-2711, May 2021, doi: 10.1109/TSMC.2019.2916073.

C. -L. Hwang, F. C. Weng, W. H. Hung, F. Wu, and C. Jan, “Simultaneous translation and rotation tracking design for sharp corner, obstacle avoidance, and time-varying terrain by hierarchical adaptive fixed-time saturated control,” Mech. Syst. Sig. Process, vol. 16, p. 107969, 2021.

C. -L. Hwang and B. -S. Chen, “Adaptive Finite-Time Saturated Tracking Control for a Class of Partially Known Robots,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 9, pp. 5674-5685, Sept. 2021, doi: 10.1109/TSMC.2019.2957183.

S. Chang, Y. Wang, Z. Zuo, and H. Yang, “Fixed-Time Formation Control for Wheeled Mobile Robots With Prescribed Performance,” in IEEE Transactions on Control Systems Technology, vol. 30, no. 2, pp. 844-851, March 2022, doi: 10.1109/TCST.2021.3069831.

D. Cruz-Ortiz, I. Chairez, and A. Poznyak, “Non-singular terminal sliding-mode control for a manipulator robot using a barrier Lyapunov function,” ISA Trans, vol. 121, pp. 268–283, 2022.

C. -L. Hwang, F. -C. Weng, D. -S. Wang, and F. Wu, “Experimental Validation of Speech Improvement-Based Stratified Adaptive Finite-Time Saturation Control of Omnidirectional Service Robot,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 2, pp. 1317-1330, Feb. 2022, doi: 10.1109/TSMC.2020.3018789.

C. -L. Hwang and H. B. Abebe, “Generalized and Heterogeneous Nonlinear Dynamic Multiagent Systems Using Online RNN-Based Finite-Time Formation Tracking Control and Application to Transportation Systems,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 13708-13720, Aug. 2022, doi: 10.1109/TITS.2021.3126662.

F. Sedghi, M. M. Arefi, and A. Abooee, “Command filtered-based neuro-adaptive robust finite-time trajectory tracking control of autonomous underwater vehicles under stochastic perturbations,” Neurocomputing, vol. 519, pp. 158–172, 2023.

Z. Zhu, Q. Zhu, and Q. Zhu, “Fixed-time adaptive neural self-triggered decentralized control for stochastic nonlinear systems with strong interconnections,” Neurocomputing, vol. 523, pp. 92–102, 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, pp. 109267, 2022.

J. Albus, “A new approach to manipulator control: The cerebellar model articulation controller,” Trans. ASME J. Dyn. Syst., vol. 97, no. 3, pp. 270–277, 1975.

W. Fang et al., “A recurrent emotional CMAC neural network controller for vision-based mobile robots,” Neurocomputing, vol. 334, pp. 227–238, Mar. 2019.

H. Ying, F. Lin, and R. Sherwin, “Fuzzy discrete event systems with gradient-based online learning,” in Proc. FUZZ-IEEE Conf., New Orleans, 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.

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, p. 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.

T. Q. Ngo, T. H. Tran, and T. T. H. Le, “Robust Adaptive Tracking Control for Uncertain Five-Bar Parallel Robot Using Fuzzy CMAC in Order to Improve Accuracy,” Journal of Robotics and Control (JRC), vol. 5, no. 3, pp. 766-774, 2024.

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.

P. R. Ouyang, Q. Li, W. J. Zhang, and L. S. Guo, “Design, modeling and control of a hybrid machine system,” Mechatronics, vol. 14, no. 10, pp. 1197–1217, 2004.

F. X. Wu, W. J. Zhang, Q. Li, P. R. Ouyang, and Z. X. Zhou, “Control of hybrid machines with 2-DOF for trajectory tracking problems,” IEEE Trans. Control Syst. Technol., vol. 13, no. 2, pp. 338–342, Mar. 2005.




DOI: https://doi.org/10.18196/jrc.v5i4.22188

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Tong Tan Hoa Le, Thanh Quyen Ngo, Thanh Hai Tran

Creative Commons License
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

Kuliah Teknik Elektro Terbaik