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Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems.

Wei Wang, Shaocheng Tong

IEEE Transactions on Cybernetics

|January 17, 2017

Summary

This study presents an adaptive fuzzy control for uncertain nonlinear multiagent systems. The proposed method ensures output consensus and bounded system performance under known control input limits.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Robotics

Background:

Leader-follower multiagent systems often face challenges with uncertain nonlinear dynamics.
Achieving output consensus in such systems requires robust control strategies.
Existing methods may not fully address bounded control inputs and learning efficiency.

Purpose of the Study:

To develop an adaptive fuzzy bounded control scheme for uncertain nonlinear strict-feedback leader-follower multiagent systems.
To guarantee output consensus among all agents under directed communication topologies.
To ensure uniformly ultimate boundedness of the closed-loop systems.

Main Methods:

Integration of fuzzy logic approximation with dynamic surface control techniques.
Introduction of a predictor for estimating error surfaces and optimizing fuzzy parameter vectors.
Utilizing known a priori bounds for control inputs, determined by feedback control gains.

Main Results:

The adaptive fuzzy control scheme guarantees uniformly ultimate boundedness of the closed-loop systems.
Tracking errors converge to a small neighborhood of the origin, indicating effective control.
The proposed strategy demonstrates smooth and fast learning through predictor-based error estimation.

Conclusions:

The developed adaptive fuzzy bounded control is effective for leader-follower multiagent systems with uncertain nonlinear dynamics.
The control strategy ensures system stability and achieves the desired output consensus.
Simulation results validate the performance and efficiency of the presented control approach.