Neural Network-Based Fixed-Time Tracking and Containment Control of Second-Order Heterogeneous Nonlinear Multiagent...

Area of Science:

• Control Theory
• Artificial Intelligence
• Networked Systems

Background:

• Multiagent systems (MASs) require robust control strategies for coordinated tasks.
• Fixed-time control offers finite-time convergence with improved performance over traditional methods.
• Heterogeneous nonlinear systems present significant challenges in control design.

Purpose of the Study:

• To develop fixed-time tracking consensus and containment control for second-order nonlinear MASs.
• To address scenarios with both measurable and immeasurable velocity states.
• To handle systems with unknown nonlinear dynamics under directed communication topologies.

Main Methods:

• Utilizing radial basis function neural networks (RBFNNs) to approximate unknown nonlinear dynamics.
• Designing a novel distributed protocol based on nonsingular sliding-mode control.
• Developing a fixed-time convergent state observer for immeasurable velocity scenarios.
• Employing a time-varying scaling function and prescribed-time convergent sliding surfaces.

Main Results:

• A novel distributed protocol for fixed-time tracking consensus and containment control is proposed for MASs with full state availability.
• A fixed-time observer successfully estimates immeasurable velocity, enabling control in such scenarios.
• The proposed methods ensure fixed-time convergence for both consensus and containment control objectives.
• Simulation examples validate the effectiveness and robustness of the developed control strategies.

Conclusions:

• The study successfully extends fixed-time control techniques to complex second-order nonlinear heterogeneous MASs.
• The proposed observer-based control strategy effectively handles systems with immeasurable velocities.
• The research contributes advanced solutions for distributed coordination and control in networked systems.