Adaptive Bipartite Tracking Control of Nonlinear Multiagent Systems With Input Quantization.

Area of Science:

• Control Theory
• Nonlinear Systems
• Multiagent Systems

Background:

• Distributed nonlinear multiagent systems present challenges in achieving coordinated control.
• Input quantization, external disturbances, and actuator faults degrade system performance and stability.

Purpose of the Study:

• To develop a robust bipartite tracking control strategy for distributed nonlinear multiagent systems.
• To address the complexities of unknown disturbance bounds and actuator fault numbers.
• To mitigate the impact of input quantization on control performance.

Main Methods:

• Utilized radial basis function (RBF) neural networks to model unknown nonlinearities.
• Employed a backstepping strategy to design an intermediate control law.
• Introduced a compensation term to counteract disturbances and actuator faults.
• Incorporated a novel smooth function to reduce quantization effects.

Main Results:

• Achieved bipartite tracking control for the multiagent system.
• Ensured all signals remain bounded within the closed-loop system.
• Guaranteed follower outputs converge to a neighborhood of the leader's output.
• Demonstrated the effectiveness of the proposed control algorithm through simulations.

Conclusions:

• The proposed distributed controller effectively achieves bipartite tracking control.
• The method provides robustness against external disturbances and actuator faults.
• The approach successfully handles input quantization in nonlinear multiagent systems.