Distributed Adaptive-Neural Finite-Time Consensus Control for Stochastic Nonlinear Multiagent Systems Subject to...

Area of Science:

• Control Theory
• Robotics
• Artificial Intelligence

Background:

• Addressing distributed finite-time consensus control in stochastic nonlinear multiagent systems (MASs) is challenging due to unknown dynamics, stochastic perturbations, external disturbances, and input saturation.
• Traditional backstepping methods face complexity explosion issues when dealing with such systems.

Purpose of the Study:

• To design innovative control inputs for stochastic nonlinear MASs to achieve finite-time consensus.
• To ensure follower agents' outputs converge to the leader agent's output within a finite time.
• To overcome limitations of existing control methods, including complexity explosion and input saturation.

Main Methods:

• Combined backstepping control, finite-time command filter technique, a finite-time auxiliary system, and artificial neural networks (ANNs).
• Employed Radial-Basis Function Neural Networks (RBFNNs) to approximate unknown system dynamics, stochastic perturbations, and external disturbances.
• Developed a novel finite-time command filter approach to mitigate complexity explosion and a finite-time auxiliary system to handle input saturation.

Main Results:

• The proposed control inputs ensure that the outputs of follower agents converge to the output of the leader agent within a finite time.
• Mathematical analysis proves the system is semiglobally finite-time stable in probability (SGFSP).
• Consensus tracking errors converge to a small neighborhood of zero in finite time, validated by a robot manipulator simulation.

Conclusions:

• The developed control scheme effectively achieves finite-time consensus for stochastic nonlinear MASs under complex conditions.
• The integration of RBFNNs, command filtering, and auxiliary systems provides a robust solution for unknown dynamics and input saturation.
• The proposed method demonstrates superior performance and stability, confirmed by numerical simulations.