Observer-Based Fault-Tolerant Finite-Time Control of Nonlinear Multiagent Systems

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Multiagent systems require robust control strategies, especially with actuator faults.
• Estimating unmeasured states is crucial for effective control in complex systems.

Purpose of the Study:

• To develop an adaptive neural containment control for nonlinear multiagent systems with actuator faults.
• To enhance system performance and reduce computational load.

Main Methods:

• Utilizing neural networks for state estimation via a neuro-adaptive observer.
• Designing an event-triggered control law to minimize computational burden.
• Employing Lyapunov stability theory for system analysis.

Main Results:

• The closed-loop system achieves cooperative semiglobal uniform ultimate boundedness (CSGUUB).
• Followers' outputs successfully reach the convex hull formed by leaders.
• Containment errors are bounded within a finite time.

Conclusions:

• The proposed neuro-adaptive control scheme effectively addresses actuator faults in nonlinear multiagent systems.
• The event-triggered approach offers computational efficiency.
• Finite-time containment is achieved, validating the control strategy.