Fixed-Time Cooperative Behavioral Control for Networked Autonomous Agents With Second-Order Nonlinear Dynamics.

Area of Science:

• Robotics
• Control Systems Engineering
• Distributed Systems

Background:

• Coordinated control of multi-agent systems is crucial for tasks like formation control and obstacle avoidance.
• Achieving rapid and guaranteed convergence times for control objectives remains a challenge.

Purpose of the Study:

• To investigate the fixed-time behavioral control problem for a team of second-order nonlinear agents.
• To achieve desired formations with collision and obstacle avoidance capabilities.
• To ensure fixed-time convergence for both task and tracking errors.

Main Methods:

• Utilized null-space-based behavioral projection to prioritize and integrate agent behaviors (tasks).
• Designed a fixed-time sliding-mode controller with state-independent adaptive gains for velocity tracking.
• Implemented a distributed control scheme relying on neighbor information exchange.
• Incorporated an online learning algorithm to enhance robustness against uncertainties.

Main Results:

• Demonstrated fixed-time convergence of task errors through prioritized behavior integration.
• Achieved fixed-time convergence of tracking errors using the designed sliding-mode controller.
• Verified the effectiveness of the distributed control and online learning approach via simulations.

Conclusions:

• The proposed fixed-time control strategy effectively enables multi-agent systems to achieve formations while avoiding collisions.
• The integration of null-space-based behavioral projection and fixed-time sliding-mode control ensures robust and efficient performance.
• The distributed and adaptive nature of the controller enhances its applicability in complex, uncertain environments.