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Volume 10 Issue 4
Apr.  2023

IEEE/CAA Journal of Automatica Sinica

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Y. Liu and L. Li, “Adaptive leader-follower consensus control of multiple flexible manipulators with actuator failures and parameter uncertainties,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 1020–1031, Apr. 2023. doi: 10.1109/JAS.2023.123093
Citation: Y. Liu and L. Li, “Adaptive leader-follower consensus control of multiple flexible manipulators with actuator failures and parameter uncertainties,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 1020–1031, Apr. 2023. doi: 10.1109/JAS.2023.123093

Adaptive Leader-Follower Consensus Control of Multiple Flexible Manipulators With Actuator Failures and Parameter Uncertainties

doi: 10.1109/JAS.2023.123093
Funds:  This work was supported in part by the National Key Research and Development Program of China (2021YFB3202200) and Guangdong Basic and Applied Basic Research Foundation (2020B1515120071, 2021B1515120017)
More Information
  • In this paper, the leader-follower consensus problem for a multiple flexible manipulator network with actuator failures, parameter uncertainties, and unknown time-varying boundary disturbances is addressed. The purpose of this study is to develop distributed controllers utilizing local interactive protocols that not only suppress the vibration of each flexible manipulator but also achieve consensus on joint angle position between actual followers and the virtual leader. Following the accomplishment of the reconstruction of the fault terms and parameter uncertainties, the adaptive neural network method and parameter estimation technique are employed to compensate for unknown items and bounded disturbances. Furthermore, the Lyapunov stability theory is used to demonstrate that followers’ angle consensus errors and vibration deflections in closed-loop systems are uniformly ultimately bounded. Finally, the numerical simulation results confirm the efficacy of the proposed controllers.


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    • Suppress the vibrations of multiple flexible manipulators
    • Realize leader-follower angle consensus for multiple flexible manipulators
    • Handle parameter uncertainties and actuator failures with neural networks
    • Estimate the upper bounds of boundary disturbances and approximation errors


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