Adaptive Prescribed-Time Dynamic Self-Triggered Time-Varying Bipartite Formation Control for Uncertain Nonlinear...

Area of Science:

• Control Theory
• Artificial Intelligence
• Networked Systems

Background:

• Multiagent systems (MASs) face challenges in formation tracking due to nonlinear dynamics, disturbances, and actuator faults.
• Existing distributed control protocols often lack adaptability to system uncertainties and communication constraints.
• Prescribed-time control offers faster convergence but requires careful design for practical applications.

Purpose of the Study:

• To develop a self-triggered prescribed-time (PT) smooth bipartite formation tracking control (BFTC) strategy for uncertain nonlinear multiagent systems (NMASs).
• To address unknown nonlinear dynamics, external disturbances, and actuator faults in cooperative-competitive MASs.
• To achieve user-defined tracking performance and enhance communication efficiency under bandwidth limitations.

Main Methods:

• Adaptive backstepping framework for formation control design.
• Radial basis function neural networks (RBFNNs) for approximating system uncertainties.
• Distributed dynamic self-triggered control (DSTC) mechanism adjusting triggering intervals based on bipartite formation tracking errors (BFTEs).

Main Results:

• The proposed BFTC strategy guarantees user-specified settling time, independent of initial conditions.
• RBFNNs effectively handle unknown system dynamics and disturbances.
• The DSTC mechanism dynamically balances communication load and system performance, enhancing transmission efficiency.

Conclusions:

• The developed self-triggered PT-BFTC strategy is effective for uncertain NMASs.
• The approach offers practical advantages in terms of performance, robustness, and communication efficiency.
• This work advances the state-of-the-art in distributed control for complex multiagent systems.