A Novel Neural Network Adaptive Approach to Asymptotical Consensus of Uncertain Nonlinear Multiagent Systems With...

Area of Science:

• Control Theory
• Artificial Intelligence
• Robotics

Background:

• Cooperative control of nonlinear multiagent systems (MASs) with general directed topologies remains a challenge.
• Existing neural network (NN) adaptive controllers struggle to decouple nonlinearity learning from cooperative control.
• Achieving asymptotic consensus in MASs over directed graphs is an open problem.

Purpose of the Study:

• To propose a novel class of NN-based adaptive controllers for nonlinear MASs.
• To address the challenge of achieving asymptotic cooperative goals over general directed topologies.
• To decouple nonlinearities learning from cooperative control within a unified framework.

Main Methods:

• Development of a NN-based cooperative modified state observer (CMSO) to approximate unknown nonlinearities.
• Decoupling nonlinearity approximation into local tracking control using the CMSO framework.
• Design of both nonsmooth and smooth adaptive controllers to ensure asymptotic consensus.

Main Results:

• The proposed controllers guarantee asymptotic consensus for MASs with a directed spanning tree topology.
• The CMSO effectively approximates unknown nonlinearities, simplifying the control design.
• Smooth controllers are presented as an alternative to avoid chattering issues associated with nonsmooth controllers.

Conclusions:

• The novel NN-based adaptive controllers successfully achieve asymptotic consensus in nonlinear MASs.
• The proposed framework provides a viable solution for cooperative control over general directed topologies.
• Simulations on multi-robot systems validate the effectiveness of the theoretical results.