A universal error transformation strategy for distributed event-triggered formation tracking of pure-feedback nonlinear multiagent systems with communication and avoidance ranges

Sung Jin Yoo and Bong Seok Park

Applied Mathematics and Computation, 2022, vol. 433, issue C

Abstract: A universal error transformation method is proposed for designing a distributed event-triggered formation tracker with preserved network connectivity and collision avoidance for multiple pure-feedback nonlinear multiagent systems connected in a directed network. It is assumed that the communication and avoidance ranges of agents are heterogeneous and that all nonaffine nonlinear functions are unknown. The primary contribution of this study is to establish a universal nonlinear transformation of relative output errors among agents for dealing with the initial network interaction, collision prevention, and predefined-time tracking performance problems in a fully distributed manner. To this end, nonlinear relative output errors among agents are designed using performance functions dependent on communication and avoidance ranges. Local event-triggered adaptive control laws are recursively designed using nonlinear relative output errors and neural networks. It is proved that the boundedness of the presented nonlinear errors ensures the achievement of network interaction preservation, collision prevention, and formation tracking with predefined-time convergence. Furthermore, the existence of a minimum inter-event time is established for local event-triggered control laws. Finally, the effectiveness of the proposed theoretical strategy is validated by simulation examples.

Keywords: Relative output error transformation; Preserved network connectivity; Collision prevention; Predefined-time tracking performance; Pure-feedback nonlinear systems (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:433:y:2022:i:c:s0096300322004866

DOI: 10.1016/j.amc.2022.127412

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