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

Wenqiang Wu, Jiarui Liu, Fangyi Li, Yuanqing Zhang and Zikai Hu
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Wenqiang Wu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Jiarui Liu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Fangyi Li: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Yuanqing Zhang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Zikai Hu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Mathematics, 2023, vol. 11, issue 4, 1-21

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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