Adaptive consensus of uncertain nonlinear systems with event triggered communication and intermittent actuator faults
This paper investigates distributed consensus tracking problem for uncertain nonlinear systems with event-triggered communication. The common desired trajectory information and each subsystem's state will be broadcast to their linked subsystems only when predefined triggering conditions are sat...
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Main Authors: | , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2021
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/152107 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | This paper investigates distributed consensus tracking problem for uncertain nonlinear systems with event-triggered communication. The common desired trajectory information and each subsystem's state will be broadcast to their linked subsystems only when predefined triggering conditions are satisfied. Compared with the existing related literature, the main features of the results presented in this paper include four folds. (i) A totally distributed adaptive control scheme is developed for multiple nonlinear systems without Lipschitz condition, while with parametric uncertainties. (ii) The derivative of desired trajectory function is allowed unknown by all subsystems and directed communication condition is considered. (iii) The designed event triggering conditions do not require either continuous monitoring of neighboring subsystems’ states or global graph information available by all subsystems. (iv) The results are successfully extended to the case with uncertain intermittent actuator faults by modifying both local control laws and adaptive laws. It is shown that for both fault-free and faulty cases, all closed-loop signals are ensured globally uniformly bounded and the tracking errors of all subsystems states will converge to a compact set. Besides, the tracking performance in the mean square error sense can be improved by appropriately adjusting design parameters. |
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