Adaptive Single-Input Recurrent WCMAC-Based Supervisory Control for De-icing Robot Manipulator

Thanh Quyen Ngo, Tong Tan Hoa Le, Binh Minh Lam, Trung Kien Pham

Abstract


The control of any robotic system always faces many great challenges in theory and practice. Because between theory and reality, there is always a huge difference in the uncertainty components in the system. That leads to the accuracy and stability of the system not being guaranteed with the set requirements. This paper presents a novel adaptive single-input recurrent wavelet differentiable cerebellar model articulation controller (S-RWCMAC)-based supervisory control system for an m-link robot manipulator to achieve precision trajectory tracking. This adaptive S-RWCMAC-based supervisory control system consists of a main adaptive S-RWCMAC, a supervisory controller, and an adaptive robust controller. The S-RWCMAC incorporates the advantages of the wavelet decomposition property with a CMAC fast learning ability, dynamic response, and input space dimension of RWCMAC can be simplified; and it is used to control the plant. The supervisory controller is appended to the adaptive S-RWCMAC to force the system states within a predefined constraint set and the adaptive robust controller is developed to dispel the effect of the approximate error. In this scheme, if the adaptive S-RWCMAC can not maintain the system states within the constraint set. Then, the supervisory controller will work to pull the states back to the constraint set and otherwise is idle. The online tuning laws of S-RWCMAC and the robust controller parameters are derived from the gradient-descent learning method and Lyapunov function so that the stability of the system can be guaranteed. The simulation and experimental results of the novel three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology. The results indicate that the proposed model has superior accuracy compared to that of the Standalone CMAC Controller. The parameters of the average squared error in the S-RWCMAC -based 3 robot joints are lower than those of the Standalone CMAC Controller by 0.023%, 0.029%, and 0.032%, respectively.

Keywords


Wavelet; Recurrent Wavelet Cerebellar Model Articulation Controller (RWCMAC); De-icing Robot Manipulator; Supervisory Control; Transmission Line; Obstacle Crossing; Path Planning.

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References


B. J. Choi, S. W. Kwak, and B. K. Kim, “Design of single-input fuzzy logic controller and its properties,” Fuzzy Sets and Systems, vol. 106, no. 3, pp. 299-308, 1999.

B. J. Choi, S. W. Kwak, and B. K. Kim, “Design and stability analysis of single-input fuzzy logic controller,” IEEE Syst. Man Cybers. B, vol. 30, no. 2, pp. 303-309, Apr. 2000.

K. Ishaque, S. S. Abdullah, S. M. Ayob, and Z. Salam, “Single input fuzzy logic controller for unmanned underwater vehicle,” J. Intell. Robot. Syst., vol. 59, no. 3, pp. 87-100, Feb. 2010.

T. Q. Ngo, Y. N. Wang, T. L. Mai, M. H. Nguyen, and J. Chen, “Robust adaptive neural-fuzzy network tracking control for robot manipulator,” International Journal of Computers Communications & Control, vol. 7, no. 2, 2012.

T. L. Mai, Y. N. Wang, and T. Q. Ngo, “Adaptive tracking control for robot manipulators using fuzzy wavelet neural networks,” International Journal of Robotics and Automation, vol. 30, no. 1, pp. 26-39, 2015.

J. S. Albus, “A new approach to manipulator control: The cerebellar model articulation controller (CMAC),” J. Dyn. Syst. Meas. Control, vol. 97, no. 3, pp. 220–227, 1975.

H. Shiraishi, S. L. Ipri, and D. D. Cho, “CMAC neural network controller for fuel-injection systems,” IEEE Trans. Control Syst. Technol., vol. 3, no. 1, pp. 32–38, Mar. 1995.

T. Q. Ngo and T. V. Phuong, “Robust adaptive self-organizing wavelet fuzzy CMAC tracking control for de-icing robot manipulator,” International Journal of Computers Communications & Control, vol. 10, no. 4, pp. 567–578, 2015.

V. -P. Ta, X. -K. Dang, and T. -Q. Ngo, “Adaptive tracking control based on CMAC for nonlinear systems,” 2017 International Conference on System Science and Engineering (ICSSE), pp. 494-498, 2017, doi: 10.1109/ICSSE.2017.8030923.

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.

S. Jagannathan, S. Commuri, and F. L. Lewis, “Feedback linearization using CMAC neural networks,” Automatica, vol. 34, no. 3, pp. 547–557, 1998.

Y. H. Kim and F. L. Lewis, “Optimal design of CMAC neural-network controller for robot manipulators,” IEEE Trans. Syst. Man Cybern. C, Appl. Rev., vol. 30, no. 1, pp. 22–31, Feb. 2000.

C. T. Chiang and C. S. Lin, “CMAC with general basis functions,” J. Neural Netw., vol. 9, no. 7, pp. 1199–1211, 1996.

Y. N. Wang, T. Q. Ngo, T. L. Mai, and C. Z. Wu, “Adaptive recurrent wavelet fuzzy CMAC tracking control for de-icing robot manipulator,” in Proceedings of the World Congress on Engineering and Computer Science, vol. 1, pp. 372–379, 2012.

T. Q. Ngo and Y. N. Wang, “Self-Structured Organizing Single-Input CMAC Control for Robot Manipulator,” International Journal of Advanced Robotic Systems, vol. 8, pp. 110-119, Sep 2011.

H. C. Lu, C. Y. Chuang, and M. F. Yeh, “Design of hybrid adaptive CMAC with supervisory controller for a class of nonlinear system,” Neurocomputing, vol. 72, no. 7-9, pp. 1920-1933, Aug. 2009.

C. -M. Lin and T. -Y. Chen, “Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems,” in IEEE Transactions on Neural Networks, vol. 20, no. 9, pp. 1377-1384, Sept. 2009, doi: 10.1109/TNN.2009.2013852.

M. Hwang, Y. J. Chen, M. Y. Ju, and W. C. Jiang, “A fuzzy CMAC learning approach to image based visual servoing system,” Information Sciences, vol. 576, pp. 187-203, 2021.

C. -M. Lin and Y. -F. Peng, “Adaptive CMAC-based supervisory control for uncertain nonlinear systems,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, no. 2, pp. 1248-1260, April 2004, doi: 10.1109/TSMCB.2003.822281.

S. H. Lane, D. A. Handelman, and J. J. Gelfand, “Theory and development of higher-order CMAC neural networks,” in IEEE Control Systems Magazine, vol. 12, no. 2, pp. 23-30, April 1992, doi: 10.1109/37.126849.

S. -Y. Wang, C. -L. Tseng, and C. -C Yeh, “Adaptive supervisory Gaussian-cerebellar model articulation controllers for direct torque control induction motor drive,” IET Electr. Power Appl., vol. 5, no. 3, pp. 295-306, June 2011.

Y. H. Kim and F. L. Lewis, “Optimal design of CMAC neural-network controller for robot manipulators,” in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 30, no. 1, pp. 22-31, Feb. 2000, doi: 10.1109/5326.827451.

H. -J. Uang and C. -C. Lien, “Mixed H2/H∞ PID tracking control design for uncertain spacecraft systems using a cerebellar model articulation controller,” IEE Proc.-Control Theory Appl., vol. 153, no. 1, pp. 1-13, Jan. 2006.

C. -T. Chiang and C. -S. Lin, “CMAC with general basis functions,” Neural Networks, vol. 9, no. 7, pp. 1199–1211, 1996.

F. L. Lewis, A. Yesildirek, and K. Liu, “Multilayer neural-net robot controller with guaranteed tracking performance,” in IEEE Transactions on Neural Networks, vol. 7, no. 2, pp. 388-399, March 1996, doi: 10.1109/72.485674.

C. -M. Lin, L. -Y. Chen, and C. -H. Chen, “RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology,” in IEEE Transactions on Neural Networks, vol. 18, no. 3, pp. 708-720, May 2007, doi: 10.1109/TNN.2007.891198.

H. -M. Lee, C. -M. Chen, and Y. -F. Lu, “A self-organizing HCMAC neural-network classifier,” in IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 15-27, Jan. 2003, doi: 10.1109/TNN.2002.806607.

W. Yu, F. O. RodrÍguez, and M. A. Moreno-Armendariz, “Hierarchical Fuzzy CMAC for Nonlinear Systems Modeling,” in IEEE Transactions on Fuzzy Systems, vol. 16, no. 5, pp. 1302-1314, Oct. 2008, doi: 10.1109/TFUZZ.2008.926579.

M. N. Nguyen, D. Shi, and C. Quek, “FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 36, no. 5, pp. 1180-1190, Oct. 2006, doi: 10.1109/TSMCB.2006.874691.

J. Sim, W. L. Tung, and C. Quek, “FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC,” in IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1394-1410, Nov. 2006, doi: 10.1109/TNN.2006.880362.

M. -F. Yeh, “Single-input CMAC control system,” Neurocomputing, vol. 70, no. 16-18, pp. 2638-2644, Apr. 2007.

M. -F. Yeh, H. -C. Lu, and J. -C. Chang, “Single-Input CMAC Control System with Direct Control Ability,” 2006 IEEE International Conference on Systems, Man and Cybernetics, pp. 2602-2607, 2006, doi: 10.1109/ICSMC.2006.385256.

T. Tao, H. C. Lu, C. Y. Hsu, and T. H. Hung, “The one-time learning hierarchical CMAC and the memory limited CA-CMAC for image data compression,” J. Chin. Inst. Eng., vol. 26. no.2, pp. 133-145, 2003.

F. Xu, J. Xu, J. Zhang, C. Zhang, and Z. Wang, “Research on parallel control of CMAC and PD based on U model,” Automatika, vol. 62, pp. 331-338, 2021.

M. -F. Yeh and C. -H. Tsai, “Standalone CMAC Control System With Online Learning Ability,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 1, pp. 43-53, Feb. 2010, doi: 10.1109/TSMCB.2009.2030334.

M. Agarwal, “A systematic classification of neural-network-based control,” in IEEE Control Systems Magazine, vol. 17, no. 2, pp. 75-93, April 1997, doi: 10.1109/37.581297.

J. G. Kuschewski, S. Hui, and S. H. Zak, “Application of feedforward neural networks to dynamical system identification and control,” in IEEE Transactions on Control Systems Technology, vol. 1, no. 1, pp. 37-49, March 1993, doi: 10.1109/87.221350.

C. -M. Lin and C. -F. Hsu, “Neural-network-based adaptive control for induction servomotor drive system,” in IEEE Transactions on Industrial Electronics, vol. 49, no. 1, pp. 115-123, Feb. 2002, doi: 10.1109/41.982255.

C. -C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic systems control,” in IEEE Transactions on Neural Networks, vol. 6, no. 1, pp. 144-156, Jan. 1995, doi: 10.1109/72.363441.

T. W. S. Chow and Yong Fang, “A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics,” in IEEE Transactions on Industrial Electronics, vol. 45, no. 1, pp. 151-161, Feb. 1998, doi: 10.1109/41.661316.

Z. Xu, C. Sun, T. Ji, J. H. Manton, and W. Shieh, “Feedforward and Recurrent Neural Network-Based Transfer Learning for Nonlinear Equalization in Short-Reach Optical Links,” in Journal of Lightwave Technology, vol. 39, no. 2, pp. 475-480, 2021, doi: 10.1109/JLT.2020.3031363.

C. Fu, Q. -G. Wang, J. Yu, and C. Lin, “Neural Network-Based Finite-Time Command Filtering Control for Switched Nonlinear Systems With Backlash-Like Hysteresis,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 3268-3273, July 2021, doi: 10.1109/TNNLS.2020.3009871.

K. -S. Hwang and C. -S. Lin, “Smooth trajectory tracking of three-link robot: a self-organizing CMAC approach,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 28, no. 5, pp. 680-692, Oct. 1998, doi: 10.1109/3477.718518.

F. J. G-. Serrano, A. R. F-. Vidal, and A. A-. Rodriguez, “Generalizing CMAC architecture and training,” in IEEE Transactions on Neural Networks, vol. 9, no. 6, pp. 1509-1514, Nov. 1998, doi: 10.1109/72.728400.

J. C. Jan and S. -L. Hung, “High-order MS CMAC neural network,” in IEEE Transactions on Neural Networks, vol. 12, no. 3, pp. 598-603, May 2001, doi: 10.1109/72.925562.

Y. C. Pati and P. S. Krishnaprasad, “Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations,” in IEEE Transactions on Neural Networks, vol. 4, no. 1, pp. 73-85, Jan. 1993, doi: 10.1109/72.182697.

B. Delyon, A. Juditsky, and A. Benveniste, “Accuracy analysis for wavelet approximations,” in IEEE Transactions on Neural Networks, vol. 6, no. 2, pp. 332-348, March 1995, doi: 10.1109/72.363469.

T. Lindblad and J. M. Kinser, “Inherent features of wavelets and pulse coupled networks,” in IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 607-614, May 1999, doi: 10.1109/72.761719.

Q. Zhang and A. Benveniste, “Wavelet networks,” in IEEE Transactions on Neural Networks, vol. 3, no. 6, pp. 889-898, Nov. 1992, doi: 10.1109/72.165591.

J. Zhang, G. G. Walter, Y. Miao, and W. N. W. Lee, “Wavelet neural networks for function learning,” in IEEE Transactions on Signal Processing, vol. 43, no. 6, pp. 1485-1497, June 1995, doi: 10.1109/78.388860.

R. -J. Wai, “Development of new training algorithms for neuro-wavelet systems on the robust control of induction servo motor drive,” in IEEE Transactions on Industrial Electronics, vol. 49, no. 6, pp. 1323-1341, Dec. 2002, doi: 10.1109/TIE.2002.804986.

F. F. M. EI-Sousy, “Robust wavelet-neural network sliding-mode control system for permanent magnet synchronous motor drives,” IET Electr. Power Appl., vol. 5, no. 1, pp. 113-132, 2011.

C. -H. Lu, “Design and Application of Stable Predictive Controller Using Recurrent Wavelet Neural Networks,” in IEEE Transactions on Industrial Electronics, vol. 56, no. 9, pp. 3733-3742, Sept. 2009, doi: 10.1109/TIE.2009.2025714.

F. -J. Lin, S. -Y. Chen, and Y. -C. Hung “Field-programmable gate array-based recurrent wavelet neural network control system for linear ultrasonic motor,” IET Electr. Power Appl., vol. 3, no. 4, pp. 289-312, 2009.

Q. Zhang, “Using wavelet network in nonparametric estimation,” in IEEE Transactions on Neural Networks, vol. 8, no. 2, pp. 227-236, March 1997, doi: 10.1109/72.557660.

Y. Qussar, I. Rivals, L. Personnaz, and G. Dreyfus, “Training wavelet networks for nonlinear dynamic input-output modeling,” Neurocomputing, vol. 20, pp. 173-188, 1998.

L. M. Reyneri, “Unification of neural and wavelet networks and fuzzy systems,” IEEE Trans. Neural Networks, vol. 10, pp. 801-814, 1999.

H. Y. Dalkiliç and S. A. Hashimi, “Prediction of daily streamflow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models,” Water Supply, vol. 20, no. 4, pp. 1396-1408, 2020.

C. -J. Lin and C. -C. Chin, “Prediction and identification using wavelet-based recurrent fuzzy neural networks,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, no. 5, pp. 2144-2154, Oct. 2004, doi: 10.1109/TSMCB.2004.833330.

L. X. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1994.




DOI: https://doi.org/10.18196/jrc.v4i4.18464

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