Finite-time consensus control for multi-agent systems with full-state constraints and actuator failures

Area of Science:

• Control Theory
• Artificial Intelligence
• Robotics

Background:

• Uncertain nonlinear multi-agent systems (MASs) often encounter full-state constraints and actuator failures in real-world applications.
• Constraint violations can severely degrade MAS performance and introduce security risks.
• Ensuring MAS performance during actuator failures presents significant control challenges.

Purpose of the Study:

• To develop a finite-time consensus control method for uncertain nonlinear MASs with full-state constraints and actuator failures.
• To ensure system stability and synchronization within a finite time despite uncertainties and failures.
• To reduce communication load using an event-triggered control strategy.

Main Methods:

• Adaptive consensus control is designed using the Backstepping technique.
• Barrier Lyapunov functions (BLFs) are employed to enforce full-state constraints.
• Finite-time neural network (NN) consensus control is utilized for synchronization.
• An event-triggered control strategy is integrated to optimize communication.

Main Results:

• The proposed method guarantees the performance of MASs with full-state constraints, even in the presence of actuator failures.
• System signals are synchronized in finite time for uncertain nonlinear MASs.
• The event-triggered strategy effectively reduces communication load.
• Numerical and practical examples validate the control strategy's effectiveness.

Conclusions:

• The developed finite-time consensus control strategy successfully addresses challenges posed by full-state constraints and actuator failures in MASs.
• The integration of Backstepping, BLFs, NNs, and event-triggering provides a robust and efficient solution for MAS control.
• The approach ensures reliable and synchronized operation of MASs within finite time.