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Minimal-Approximation-Based Distributed Consensus Tracking of a Class of Uncertain Nonlinear Multiagent Systems With
IEEE Transactions on Cybernetics
|April 4, 2017
Summary
This study introduces a novel distributed adaptive consensus tracking method for multiagent systems, reducing the need for multiple function approximators in controllers for improved efficiency in handling nonlinearities and unknown control directions.
Area of Science:
- Control Theory
- Systems Engineering
- Robotics
Background:
- Multiagent systems often face challenges with unknown nonlinearities and control directions.
- Existing consensus tracking methods for strict-feedback systems can be computationally intensive due to multiple function approximators.
Purpose of the Study:
- To develop a minimal-approximation-based distributed adaptive consensus tracking approach.
- To address unknown heterogeneous nonlinearities and control directions in strict-feedback multiagent systems.
- To reduce the number of function approximators required in local controllers.
Main Methods:
- A recursive design methodology employing a new error transformation.
- Utilizing a single function approximator per follower controller, irrespective of system order.
- A bounding lemma for Nussbaum functions to manage unknown control directions.
Main Results:
- Successfully implemented a minimal-approximation-based design for distributed consensus tracking.
- Demonstrated the ability to handle unknown heterogeneous nonlinearities and control directions.
- Achieved stability of the closed-loop system analyzed via Lyapunov methods.
Conclusions:
- The proposed approach offers a more efficient method for consensus tracking in complex multiagent systems.
- This technique simplifies controller design by minimizing the reliance on multiple approximators.
- The framework ensures system stability despite uncertainties and unknown control directions.
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