Finite-Time Neuroadaptive Cooperative Control for Nonlinear Multiagent Systems Under Nonaffine Faults and Partially...

Area of Science:

• Control Systems Engineering
• Robotics
• Nonlinear Systems Theory

Background:

• Complex nonlinear multiagent systems (CNMASs) present significant control challenges.
• Existing control methods struggle with nonaffine faults and unknown control directions in CNMASs.
• Finite-time control offers improved performance over traditional asymptotic control.

Purpose of the Study:

• To develop a finite-time adaptive control scheme for CNMASs.
• To address challenges posed by nonaffine faults and partially unknown control directions.
• To ensure finite-time boundedness of all signals and convergence of cooperative control errors.

Main Methods:

• A finite-time command filter is employed to mitigate complexity and chattering issues.
• An improved error compensation mechanism is utilized to alleviate filter errors.
• Piecewise Nussbaum functions are incorporated to handle partially unknown control directions.

Main Results:

• The proposed cooperative control strategy ensures finite-time boundedness of all closed-loop system signals.
• Cooperative control errors converge to a predefined upper bound within a finite time.
• The method demonstrates rapidity and robustness, validated through simulations and a real-world experiment.

Conclusions:

• The developed finite-time adaptive control strategy effectively manages CNMASs with nonaffine faults and unknown control directions.
• The approach guarantees finite-time stability and bounded errors, outperforming existing methods.
• Experimental validation confirms the practical applicability and effectiveness of the proposed control scheme.