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This paper proposed a feedback feedforward Iterative Learning Control (ILC) law for nonlinear system with iteratively variable trial lengths under a networked systems structure, where the both sensor and actuator occurs random data lost separately. The feedforward ILC part includes the calculated input signal, actual input signal, and the modified tracking error of last iteration. Some tracking signal would be lost at last iteration because of the iterative varying trial lengths. In order to offset the missing signal of last trial, the tracking error of present trial is adopted by feedback control part. It is established that the convergence relied on the feedforward control gain merely, while the rate of convergence is also expedited by the feedback control component. When the initial state expectation equals to the reference one, it is established that the tracking error expectation can be controlled to zero. With an illustrative simulation, the effectiveness of the developed algorithm can be demonstrated.
L. Jin, S. Liang, X. Luo, and M. Zhou, Distributed and time-delayed-winner-take-all network for competitive coordination of multiple robots, IEEE Trans. Cybern., vol. 53, no. 1, pp. 641–652, 2023.
K. Zhu and T. Zhang, Deep reinforcement learning based mobile robot navigation: A review, Tsinghua Science and Technology, vol. 26, no. 5, pp. 674–691, 2021.
Z. Zhao, Z. Tan, Z. Liu, M. O. Efe, and C. K. Ahn, Adaptive inverse compensation fault-tolerant control for a flexible manipulator with unknown dead-zone and actuator faults, IEEE Trans. Ind. Electron., vol. 70, no. 12, pp. 12698–12707, 2023.
L. Jin, F. Zhang, M. Liu, and S. S. D. Xu, Finite-time model predictive tracking control of position and orientation for redundant manipulators, IEEE Trans. Ind. Electron., vol. 70, no. 6, pp. 6017–6026, 2023.
J. Zhang, L. Jin, and Y. Wang, Collaborative control for multimanipulator systems with fuzzy neural networks, IEEE Trans. Fuzzy Syst., vol. 31, no. 4, pp. 1305–1314, 2023.
L. Jin, J. Zhao, and S. Li, A data-driven sparse motion planning scheme for redundant manipulators, IEEE Trans. Circuits Syst. II: Express Briefs, vol. 70, no. 7, pp. 2600–2604, 2023.
Z. Zhuang, H. Tao, Y. Chen, V. Stojanovic, and W. Paszke, An optimal iterative learning control approach for linear systems with nonuniform trial lengths under input constraints, IEEE Trans. Syst., Man, Cybern.: Syst., vol. 53, no. 6, pp. 3461–3473, 2023.
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, IEEE Trans. Neural Network. Learn. Syst., vol. 34, no. 9, pp. 6416–6427, 2023.
H. Li, R. Chi, Z. Hou, and B. Huang, Double dynamic linearization-based higher order indirect adaptive iterative learning control, IEEE Trans. Cybern., vol. 53, no. 6, pp. 3506–3517, 2023.
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 Trans. Autom. Sci. Eng., vol. 21, no. 1, pp. 168–180, 2024.
T. Zhang, X. Jiao, and Y. Zhang, Internal-model-principle-based fast adaptive iterative learning trajectory tracking control for autonomous farming vehicle under alignment condition and input constraint, IEEE Trans. Syst., Man, Cybern.: Syst., vol. 53, no. 6, pp. 3588–3599, 2023.
J. Zheng and Z. Hou, Data-driven spatial adaptive terminal iterative learning predictive control for automatic stop control of subway train with actuator saturation, IEEE Trans. Intell. Transport. Syst., vol. 24, no. 10, pp. 11453–11465, 2023.
Y. Wang, T. Wang, X. Yang, and J. Yang, Gradient descent-Barzilai Borwein-based neural network tracking control for nonlinear systems with unknown dynamics, IEEE Trans. Neural Network. Learn. Syst., vol. 34, no. 1, pp. 305–315, 2023.
R. Yang, G. Li, Y. Zhu, and G. P. Liu, Event-triggered control for networked predictive control systems with time delay and external disturbance, IEEE Trans. Control Netw. Syst., vol. 10, no. 4, pp. 2120–2129, 2023.
J. Liu, N. Zhang, Y. Li, X. Xie, E. Tian, and J. Cao, Learning-based event-triggered tracking control for nonlinear networked control systems with unmatched disturbance, IEEE Trans. Syst., Man, Cybern.: Syst., vol. 53, no. 5, pp. 3230–3240, 2023.
X. Zhou, X. Yu, Y. Zhang, Y. Luo, and X. Peng, Trajectory planning and tracking strategy applied to an unmanned ground vehicle in the presence of obstacles, IEEE Trans. Autom. Sci. Eng., vol. 18, no. 4, pp. 1575–1589, 2021.
H. Ren, S. Chen, L. Yang, and Y. Zhao, Optimal path planning and speed control integration strategy for UGVs in static and dynamic environments, IEEE Trans. Veh. Technol., vol. 69, no. 10, pp. 10619–10629, 2020.
J. Liang and X. Bu, Iterative consensus control for a class of nonlinear MIMO multi-agent systems with data dropout, IFAC-PapersOnLine, vol. 52, no. 24, pp. 111–116, 2019.
D. Shen, C. Zhang, and Y. Xu, Intermittent and successive ILC for stochastic nonlinear systems with random data dropouts, Asian J. Control, vol. 20, no. 3, pp. 1102–1114, 2018.
H. Chen, R. Liu, P. He, and Z. Li, Asynchronous dissipative control for networked time-delay Markov jump systems with event-triggered scheme and packet dropouts, EURASIP J. Wireless Commun. Network., vol. 2022, no. 1, p. 82, 2022.
X. Bu, Z. Hou, Z. Hou, and J. Yang, Robust iterative learning control design for linear systems with time-varying delays and packet dropouts, Adv. Differ. Equ., vol. 2017, no. 1, p. 84, 2017.
J. Liu and X. Ruan, Synchronous-substitution-type iterative learning control for discrete-time networked control systems with Bernoulli-type stochastic packet dropouts, IMA J. Math. Control Inf., vol. 35, no. 3, pp. 939–962, 2018.
L. Huang, H. Ding, Z. Zhang, Q. Zhang, and L. Sun, A comparison of compensation methods for random input data dropouts in networked iterative learning control system, Adv. Differ. Equ., vol. 2019, no. 1, p. 68, 2019.
J. Liu and X. Ruan, Networked iterative learning control for discrete-time systems with stochastic packet dropouts in input and output channels, Adv. Differ. Equ., vol. 2017, no. 1, p. 53, 2017.
Y. Jin and D. Shen, Iterative learning control for nonlinear systems with data dropouts at both measurement and actuator sides, Asian J. Control, vol. 20, no. 4, pp. 1624–1636, 2018.
D. Shen, Iterative learning control with incomplete information: A survey, IEEE/CAA J. Autom. Sin., vol. 5, no. 5, pp. 885–901, 2018.
Y. Chen, B. Chu, and C. T. Freeman, Iterative learning control for path-following tasks with performance optimization, IEEE Trans. Control Syst. Technol., vol. 30, no. 1, pp. 234–246, 2022.
M. Pierallini, F. Angelini, R. Mengacci, A. Palleschi, A. Bicchi and M. Garabini, Iterative learning control for compliant underactuated arms, IEEE Trans. Syst., Man, Cybern.: Syst., vol. 53, no. 6, pp. 3810–3822, 2023.
Y. Liu, Y. Fan, and Y. Jia, Iterative learning formation control for continuous-time multi-agent systems with randomly varying trial lengths, J. Franklin Inst., vol. 357, no. 14, pp. 9268–9287, 2020.
Y. S. Wei and X. D. Li, Robust higher-order ILC for non-linear discrete-time systems with varying trail lengths and random initial state shifts, IET Control Theory Appl., vol. 11, no. 15, pp. 2440–2447, 2017.
M. Shen, X. Wu, J. H. Park, Y. Yi, and Y. Sun, Iterative learning control of constrained systems with varying trial lengths under alignment condition, IEEE Trans. Neural Network. Learn. Syst., vol. 34, no. 9, pp. 6670–6676, 2023.
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