Finite-Time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems with Non-Strict Feedback Based on a Neural Network Observer
This paper studies the problem of time-varying formation control with finite-time prescribed performance for non-strict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities. To eliminate nonlinearities, neural networks are applied to approximate the inherent dy...
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| Published in | IEEE/CAA journal of automatica sinica Vol. 11; no. 4; pp. 1039 - 1050 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
Chinese Association of Automation (CAA)
01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2329-9266 2329-9274 |
| DOI | 10.1109/JAS.2023.123615 |
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| Abstract | This paper studies the problem of time-varying formation control with finite-time prescribed performance for non-strict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities. To eliminate nonlinearities, neural networks are applied to approximate the inherent dynamics of the system. In addition, due to the limitations of the actual working conditions, each follower agent can only obtain the locally measurable partial state information of the leader agent. To address this problem, a neural network state observer based on the leader state information is designed. Then, a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region, which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time. Finally, a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm. |
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| AbstractList | This paper studies the problem of time-varying formation control with finite-time prescribed performance for non-strict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities. To eliminate nonlinearities, neural networks are applied to approximate the inherent dynamics of the system. In addition, due to the limitations of the actual working conditions, each follower agent can only obtain the locally measurable partial state information of the leader agent. To address this problem, a neural network state observer based on the leader state information is designed. Then, a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region, which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time. Finally, a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm. This paper studies the problem of time-varying for-mation control with finite-time prescribed performance for non-strict feedback second-order multi-agent systems with unmea-sured states and unknown nonlinearities.To eliminate nonlinear-ities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strat-egy is proposed by restricting the sliding mode surface to a pre-scribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm. |
| Author | Ma, Chi Dong, Dianbiao |
| AuthorAffiliation | School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China |
| AuthorAffiliation_xml | – name: School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China |
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| SubjectTerms | Adaptive control Algorithms Artificial neural networks Closed loops Feedback control Finite-time control Formation control Multi-agent systems Multiagent systems neural network Neural networks Nonlinear systems Nonlinearity Observers Output feedback prescribed performance control State observers time-varying formation control Time-varying systems |
| Title | Finite-Time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems with Non-Strict Feedback Based on a Neural Network Observer |
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