期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 29, 期 12, 页码 6015-6025出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2817880
关键词
Adaptive neural networks (NNs); disturbance; multiple leaders; multiagent system; practical formation tracking; second-order nonlinear dynamics; time-varying formation
类别
资金
- Young Elite Scientists Sponsorship Program by CAST [2017QNRC001]
- Beijing Natural Science Foundation [4182035]
- National Natural Science Foundation of China [61503009, 61333011]
- Aeronautical Science Foundation of China [2016ZA51005]
- Special Research Project of Chinese Civil Aircraft
- Innovation Zone Project [17-163-11-ZT-004-017-01]
- State Key Laboratory of Intelligent Control and Decision of Complex Systems
- Fundamental Research Funds for the Central Universities [YWF-18-BJ-Y-73]
Practical time-varying formation tracking problems for second-order nonlinear multiagent systems with multiple leaders are investigated using adaptive neural networks (NNs), where the time-varying formation tracking error caused by time-varying external disturbances can be arbitrarily small. Different from the previous work, there exists a predefined time-varying formation formed by the states of the followers and the formation tracks the convex combination of the states of the leaders with unknown control inputs. Besides, the dynamics of each agent has both matched/mismatched heterogeneous nonlinearities and disturbances simultaneously. First, a practical time-varying formation tracking protocol using adaptive NNs is proposed, which is constructed using only local neighboring information. The proposed control protocol can process not only the matched/mismatched heterogeneous nonlinearities and disturbances, but also the unknown control inputs of the leaders. Second, an algorithm with three steps is introduced to design the practical formation tracking protocol, where the parameters of the protocol are determined, and the practical time-varying formation tracking feasibility condition is given. Third, the stability of the closed-loop multiagent system is proven by using the Lyapunov theory. Finally, a simulation example is showed to illustrate the effectiveness of the obtained theoretical results.
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