Adaptive neural network based leader-following consensus control for a class of second-order nonlinear multi-agent...

Area of Science:

• Control Theory
• Robotics
• Artificial Intelligence

Background:

• Multi-agent systems (MASs) are crucial in various fields, but their control is challenged by nonlinear dynamics, input saturation, and component faults.
• Existing control methods often rely on restrictive assumptions like global Lipschitz conditions, limiting their applicability to complex, real-world scenarios.

Purpose of the Study:

• To address the leader-following consensus problem in second-order nonlinear MASs with practical limitations.
• To propose a robust adaptive control strategy that accommodates unknown nonlinear dynamics, input saturation, actuator faults, and sensor faults.

Main Methods:

• Utilized a differential mean value theorem to handle unknown nonlinear factors more effectively than traditional methods.
• Developed a distributed adaptive neural network (NN)-based controller using only relative position and velocity data, avoiding the need for complete state measurements.
• Employed stability theory and Lyapunov functions to rigorously prove the achievement of leader-following consensus.

Main Results:

• The proposed adaptive NN control strategy successfully achieves leader-following consensus in second-order nonlinear MASs.
• The controller demonstrates resilience against unknown nonlinear dynamics, actuator faults, sensor faults, and input saturation.
• Simulation results validate the effectiveness and robustness of the proposed control method in complex MASs.

Conclusions:

• The study presents a novel and effective adaptive control solution for challenging MASs scenarios.
• The approach enhances the practical applicability of consensus control by addressing multiple real-world constraints.
• The findings contribute to the advancement of robust control strategies for distributed autonomous systems.