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

TL;DR

This paper presents a distributed fixed-time control method for networked second-order nonlinear agents to achieve formation and obstacle avoidance, using behavioral projection, sliding mode control, and online learning for robustness.

Contribution

It introduces a novel fixed-time control framework combining behavioral projection, sliding mode control with adaptive gains, and online learning for robustness in multi-agent systems.

Findings

Achieves fixed-time convergence of formation errors

Ensures collision and obstacle avoidance

Demonstrates robustness through online learning

Abstract

In this paper, we investigate the fixed-time behavioral control problem for a team of second-order nonlinear agents, aiming to achieve a desired formation with collision/obstacle~avoidance. In the proposed approach, the two behaviors(tasks) for each agent are prioritized and integrated via the framework of the null-space-based behavioral projection, leading to a desired merged velocity that guarantees the fixed-time convergence of task errors. To track this desired velocity, we design a fixed-time sliding mode controller for each agent with state-independent adaptive gains, which provides a fixed-time convergence of the tracking error. The control scheme is implemented in a distributed manner, where each agent only acquires information from its neighbors in the network. Moreover, we adopt an online learning algorithm to improve the robustness of the closed system with respect to uncertainties/disturbances. Finally, simulation results are provided to show the effectiveness of the proposed approach.