Robust Adaptive Iterative Learning Control for De-Icing Robot Manipulator

Thanh Quyen Ngo, Thanh Hai Tran

Abstract


This paper introduces a new method of controlling uncertain robot using robust adaptive iterative learning control (RAILC) to track the trajectory in iterative operation mode. This method uses a PD controller combined with gain switching and forward learning techniques to predict the desired torque of the actuator. Using the Lyapunov method, this paper presents an RAILC control scheme for an uncertain robot system with structural and unstructured properties while ensuring the stability of the closed-loop system in the domain repeat. This study believes that this new control method can advance the field of robot control, especially in dealing with structured and unstructured uncertainties. It can help improve the flexibility and performance of robotic systems in real-world applications, such as automated manufacturing, transportation services, or healthcare. At the same time, providing simulation and test results demonstrates the effectiveness of the new control method in deicing high voltage power lines for robots.


Keywords


PD Control; Learning Control; De-Icing Robot Manipulator; Adaptive Iterative Learning Control.

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References


J. Li, Z. Wu, C. Wen, and Y. Liu, “Stabilization involving condition-based adaptive control of an uncertain wave equation for the vibration of a flexible string,” Automatica, vol. 161, p. 111488, March 2024.

Y. Jiang, L. Liu, and G. Feng, “Fully distributed adaptive control for output consensus of uncertain discrete-time linear multi-agent systems,” Automatica, vol. 162, p. 111531, April 2024.

L. H. Wang and Y. Li, “Adaptive control of robot manipulators with closed architecture,” Automatica, vol. 162, p. 111040, April 2024.

Z. Liu, F. Wang, Y. Zhang, X. Chen, and C. L. P. Chen, “Adaptive Tracking Control for A Class of Nonlinear Systems With a Fuzzy Dead–Zone Input,” in IEEE Transactions on Fuzzy Systems, vol. 23, no. 1, pp. 193–204, Feb. 2015.

X. Yu, Z. Hou, M. M. Polycarpou, and L. Duan, “Data–Driven Iterative Learning Control for Nonlinear Discrete–Time MIMO Systems,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 3, pp. 1136–1148, March 2021.

L. Yang, Y. Li, D. Huang, J. Xia, and X. Zhou, “Spatial Iterative Learning Control for Robotic Path Learning,” in IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 5789–5798, July 2022.

D. X. Ba, N. T. Thien, and J. Bae, “A Novel Iterative Second–Order Neural–Network Learning Control Approach for Robotic Manipulators,” in IEEE Access, vol. 11, pp. 58318–58332, 2023.

D. Huang, Y. Chen, D. Meng, and P. Sun, “Adaptive iterative learning control for high–speed train: A multi–agent approach,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 7, pp. 4067-4077, 2021.

Y. Chen, D. Huang, Y. Li, and X. Feng, “A Novel Iterative Learning Approach for Tracking Control of High–Speed Trains Subject to Unknown Time–Varying Delay,” in IEEE Transactions on Automation Science and Engineering, vol. 19, no. 1, pp. 113–121, Jan. 2022.

G. Liu and Z. Hou, “Adaptive Iterative Learning Control for Subway Trains Using Multiple–Point–Mass Dynamic Model Under Speed Constraint,” in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1388–1400, March 2021.

J. Zheng and Z. Hou, “Model Free Adaptive Iterative Learning Control Based Fault–Tolerant Control for Subway Train With Speed Sensor Fault and Over–Speed Protection,” IEEE Transactions on Automation Science and Engineering, vol.21, no.1, pp.168–180, 2024.

S. Airmoto, S. Kawamura, and F. Miyazaki, “Bettering operation of robotics by learning,” J. Robot. Syst., vol. 12, no. 2, pp. 123–140, 1984.

P. Bondi, G. Casalino, and L. Gambardella, “On the iterative learning control theory for robotic manipulators,” IEEE J. Robot. Autom., vol. 4, no. 1, pp. 14–22, Feb. 1998.

P. Jiang, L. C. A. Bamforth, Z. Feng, J. E. Baruch, and Y. Chen, “Indirect iterative learning control for a discrete visual servo without a camera–robot model,” IEEE Trans. Syst. Man Cybern. B Cybern., vol. 37, no. 4, pp. 863–876, Aug. 2007.

Y. Chen, D. Huang, N. Qin, and Y. Zhang, “Adaptive Iterative Learning Control for a Class of Nonlinear Strict–Feedback Systems With Unknown State Delays,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 9, pp. 6416–6427, Sept. 2023.

Z. Chen, Y. Hou, R. Huang, and Q. Cheng, “Neural network compensator-based robust iterative learning control scheme for mobile robots nonlinear systems with disturbances and uncertain parameters,” Applied Mathematics and Computation, vol. 469, p. 128549, 2024.

L. Wu, Y. Yu, Z. Lin, Q. Yan, and X. Liu, “Time–Varying Boundary Layer Based Iterative Learning Control for Nonlinearly Parametric Time–Delay Systems With Arbitrary Initial Errors and Iteration–Varying Reference Trajectories,” in IEEE Access, vol. 9, pp. 90642–90655, 2021.

S. L. Chen, S. M. Hsieh, and T. Q. Ta, “Iterative learning contouring control for five-axis machine tools and industrial robots,” Mechatronics, vol. 94, p. 103030, 2023.

Y. Chen and C. T. Freeman, “Iterative learning control for piecewise arc path tracking with validation on a gantry robot manufacturing platform,” ISA Transactions, vol. 139, pp. 650-659, 2023.

Y. H. Lee, S. C. Hsu, T. Y. Chi, Y. Y. Du, J. S. Hu, and T. C. Tsao, “Industrial robot accurate trajectory generation by nested loop iterative learning control,” Mechatronics, vol. 74, p. 102487, 2021.

A. Tayebi, “Adaptive iterative learning control for robot manipulators,” Automatica, vol. 40, no. 7, pp. 1195–1203, Jul. 2004.

C.–J. Chien and A. Tayebi, “Further results on adaptive iterative learning control of robot manipulators,” Automatica, vol. 44, no. 3, pp. 830–837, Mar. 2008.

X. X. He, H. Zhuang, and D. Zhang, “Pulse neural network–based adaptive iterative learning control for uncertain robots,” Neural Comput. Appl., vol. 23, no. 7, pp. 1885–1890, Dec. 2013.

F. Cao and J. Liu, “An adaptive iterative learning algorithm for boundary control of a coupled ODE–PDE two–link rigid–flexible manipulator,” J. Franklin Inst., vol. 354, no. 1, pp. 277–297, 2017.

X. Li, Y.–H. Liu, and H. Yu, “Iterative learning impedance control for rehabilitation robots driven by series elastic actuators,” Automatica, vol. 90, pp. 1–7, Apr. 2018.

J.–X. Xu and R. Yan, “On initial conditions in iterative learning control,” IEEE Trans. Autom. Control, vol. 50, no. 9, pp. 1349–1354, Sep. 2005.

Y. Wang, W. Wu, X. Lou, and D. Görges, “Iterative learning control of an Euler-Bernoulli beam with time-varying boundary disturbance,” Computers & Mathematics with Applications, vol. 162, pp. 145-154, 2024.

L. Wang, Z. Huangfu, R. Li, X. Wen, Y. Sun, and Y. Chen, “Iterative learning control with parameter estimation for non-repetitive time-varying systems,” Journal of the Franklin Institute, vol. 361, no. 3, pp. 1455-1466, 2024.

Y. Kong, X.-D. Li, and X. Li, “Iterative learning control approach for linear discrete time-varying systems with different initial time points,” Journal of the Franklin Institute, vol. 361, no. 6, p. 106702, 2024.

X. Jin, “Iterative learning control for non–repetitive trajectory tracking of robot manipulators with joint position constraints and actuator faults,” Int. J. Adapt. Control Signal Process., vol. 31, no. 6, pp. 859–875, Jun. 2017.

O. Zhang, W. Yao, D. Du, C. Wu, J. Liu, L. Wu, and Y. Sun, “Trajectory optimization and tracking control of free-flying space robots for capturing non-cooperative tumbling objects,” Aerospace Science and Technology, vol. 143, p. 108718, 2023.

Q. Yang, Q. Yan, J. Cai, J. Tian, and X. Guan, “Neural Network–Based Error–Tracking Iterative Learning Control for Tank Gun Control Systems With Arbitrary Initial States,” in IEEE Access, vol. 8, pp. 72179–72187, 2020.

G. Liu and Z. Hou, “RBFNN–Based Adaptive Iterative Learning Fault–Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 9, pp. 5785–5799, Sept. 2021.

D. Li, C. L. P. Chen, Y.–J. 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.

L. Liu, Y.–J. Liu, and S. Tong, “Fuzzy–based multierror constraint control for switched nonlinear systems and its applications,” IEEE Trans. Fuzzy Syst., vol. 27, no. 8, pp. 1519–1531, Aug. 2018.

T. Gao, Y.–J. Liu, L. Liu, and D. Li, “Adaptive neural network–based control for a class of nonlinear pure–feedback systems with time–varying full state constraints,” IEEE/CAA J. Automatica Sinica, vol. 5, no. 5, pp. 923–933, Sep. 2018.

L. Wu, Q. Yan, and J. Cai, “Neural Network–Based Adaptive Learning Control for Robot Manipulators With Arbitrary Initial Errors,” in IEEE Access, vol. 7, pp. 180194–180204, 2019.

X. Zhou, Y. Tian, and H. Wang, “Neural network state observer-based robust adaptive fault-tolerant quantized iterative learning control for the rigid-flexible coupled robotic systems with unknown time delays,” Applied Mathematics and Computation, vol. 430, p. 127286, 2022.

K. Xu, B. Meng, and Z. Wang, “Generalized regression neural networks-based data-driven iterative learning control for nonlinear non-affine discrete-time systems,” Expert Systems with Applications, vol. 248, p. 123339, 2024.

C. Yang, C. Chen, W. He, R. Cui, and Z. Li, “Robot learning system based on adaptive neural control and dynamic movement primitives,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 3, pp. 777–787, Mar. 2019.

Y. Lv, X. Ren, J. Tian, and X. Zhao, “Inverse-model-based iterative learning control for unknown MIMO nonlinear system with neural network,” Neurocomputing, vol. 519, pp. 187-193, 2023.

M. Sun, T. Wu, L. Chen, and G. Zhang, “Neural AILC for error tracking against arbitrary initial shifts,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 7, pp. 2705–2716, Jul. 2018.

Y. Chai, K. Liu, X. Duan, J. Yi, R. Sun, and J. Li, “Human–machine interaction controller of upper limb based on iterative learning method with zeroing neural algorithm and disturbance observe,” Engineering Applications of Artificial Intelligence, vol. 122, p. 106108, 2023.

Q. Yan, J. Cai, Y. Zhang, and Z. Yang, “Adaptive Iterative Learning Control for Robot Manipulators With Time–Varying Parameters and Arbitrary Initial Errors,” in IEEE Access, vol. 9, pp. 166920–166931, 2021.

X. Jin, “Iterative learning control for non–repetitive trajectory tracking of robot manipulators with joint position constraints and actuator faults,” Int. J. Adapt. Control Signal Process., vol. 31, no. 6, pp. 859–875, Jun. 2017.

Z. Chaochen, Y. Wang, H. Tan, J. Zhou, and Q. Lin, “Posture–based Leader–follower Formation for Multiple Mobile Robot Systems Using Data–based Adaptive Iterative Learning Control,” 2022 China Automation Congress (CAC), pp. 6463–6468, 2022.

J. Xu, D. Li, X. Ma, B. Zhang, and Q. Jin, “Study of an Adaptive Iterative Learning Control Based on Extended State Observer in Path Tracking of Double Joint Robot,” 2020 39th Chinese Control Conference (CCC), pp. 2037–2042, 2020.

Z. Cheng and Z. Zhuo, “Adaptive Iterative Learning Trajectory Tracking Control of SCARA Robot,” 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 910–914, 2021.

J. Li, R. Chi, and D. Yang, “High–order Model–free Adaptive Iterative Learning Control for Velocity Tracking of Wheeled Mobile Robots,” 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), pp. 388–392, 2020.

L. Sun and W. Duan, “Decentralized Robust Adaptive Iterative Learning Control of Robot Manipulators,” 2018 37th Chinese Control Conference (CCC), pp. 2685–2689, 2018.

H. Zhang, Q. Yan, J. Cai, S. Gao, and Y. Zhang, “Initial–Rectification Neuro–Adaptive Iterative Learning Control for Robot Manipulators With Input Deadzone and Nonzero Initial Errors,” in IEEE Access, vol. 11, pp. 22441–22449, 2023.

Z. Zhu, X. Xu, Y. Zhu, A. Rodic, and P. B. Petrovic, “Research on Fuzzy Adaptive and PD–Type Iterative Learning Control for Robot Manipulator,” 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 487–491, 2021.

D. X. Ba, N. T. Thien, and J. Bae, “A Novel Iterative Second–Order Neural–Network Learning Control Approach for Robotic Manipulators,” in IEEE Access, vol. 11, pp. 58318–58332, 2023.

D. X. Ba and T. T. Nguyen, “A new neural iterative learning control approach for position tracking control of robotic manipulators: Theory simulation and experiment,” Meas. Control Autom., vol. 3, no. 2, pp. 66–74, 2022.

C. Zhang and X. Tian, “Non–uniform trajectory tracking adaptive iterative learning control for nonlinear pure–feedback systems with initial state error based on RBF–neural network,” 2021 40th Chinese Control Conference (CCC), pp. 532–539, 2021.

G. Zhu and Q. Yan, “Error Tracking–Based Neuro–Adaptive Learning Control for Pneumatic Artificial Muscle Systems With Output Constraint,” in IEEE Access, vol. 11, pp. 127479–127491, 2023.

C. Zhang and J. Li, “Adaptive iterative learning control for nonlinear pure–feedback systems with initial state error based on fuzzy approximation,” Journal of the Franklin Institute, vol. 351, no. 3, pp. 1483–1500, 2014.

P. R. Ouyang, W. J. Zhang, and M. M. Gupta, “An adaptive switching learning control method for trajectory tracking of robot manipulators,” Mechatronics, vol. 16, pp. 51–61, Apr. 2006.

Q. Chen, H. Shi, and M. Sun, “Echo State Network-Based Backstepping Adaptive Iterative Learning Control for Strict-Feedback Systems: An Error-Tracking Approach,” in IEEE Transactions on Cybernetics, vol. 50, no. 7, pp. 3009-3022, July 2020.

Y. Guo, J. Ding, X. Feng, P. Sun, Q. Fang, and M. Wei, “Robust adaptive iterative learning control for high-speed trains under non-strictly repeated conditions,” Control Engineering Practice, vol. 14, p. 105865, 2024.




DOI: https://doi.org/10.18196/jrc.v5i3.21791

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