Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone

Wu, Wenqiang; Liu, Jiarui; Li, Fangyi; Zhang, Yuanqing; Hu, Zikai

doi:10.3390/math11040988

Open AccessArticle

Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone

by

Wenqiang Wu

¹

,

Jiarui Liu

¹,

Fangyi Li

^1,2,*,

Yuanqing Zhang

¹ and

Zikai Hu

¹

School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

²

Key Laboratory for Safety Control of Bridge Engineering, Ministry of Education and Hunan Province, Changsha University of Science and Technology, Changsha 410114, China

^*

Author to whom correspondence should be addressed.

Mathematics 2023, 11(4), 988; https://doi.org/10.3390/math11040988

Submission received: 11 January 2023 / Revised: 9 February 2023 / Accepted: 10 February 2023 / Published: 15 February 2023

(This article belongs to the Special Issue Mathematic Control and Artificial Intelligence)

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Abstract

For a class of multiagent systems with an unknown time-varying input dead-zone, a prescribed settling time adaptive neural network consensus control method is developed. In practical applications, some control signals are difficult to use effectively due to the extensive existence of an input dead-zone. Moreover, the time-varying input gains further seriously degrade the performance of the systems and even cause system instability. In addition, multiagent systems need frequent communication to ensure a system’s consistency. This may lead to communication congestion. To solve this problem, an event-triggered adaptive neural network control method is proposed. Further, combined with the prescribed settling time transform function, the developed consensus method greatly increases the convergence rate. It is demonstrated that all followers of multiagent systems can track the virtual leader within a prescribed time and not exhibit Zeno behavior. Finally, the theoretical analysis and simulation verify the effectiveness of the designed control method.

Keywords: multiagent systems; input dead-zone; event-triggered control; prescribed settling time; neural network

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MDPI and ACS Style

Wu, W.; Liu, J.; Li, F.; Zhang, Y.; Hu, Z. Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone. Mathematics 2023, 11, 988. https://doi.org/10.3390/math11040988

AMA Style

Wu W, Liu J, Li F, Zhang Y, Hu Z. Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone. Mathematics. 2023; 11(4):988. https://doi.org/10.3390/math11040988

Chicago/Turabian Style

Wu, Wenqiang, Jiarui Liu, Fangyi Li, Yuanqing Zhang, and Zikai Hu. 2023. "Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone" Mathematics 11, no. 4: 988. https://doi.org/10.3390/math11040988

APA Style

Wu, W., Liu, J., Li, F., Zhang, Y., & Hu, Z. (2023). Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone. Mathematics, 11(4), 988. https://doi.org/10.3390/math11040988

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Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone

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