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Neuroadaptive Performance Guaranteed Control for Multiagent Systems With Power Integrators and Unknown Measurement
IEEE Transactions on Neural Networks and Learning Systems
|March 29, 2022
Summary
This study introduces a novel adaptive control strategy for multiagent systems (MASs) with power integrators, ensuring guaranteed tracking performance and finite-time convergence of relative position errors despite unknown measurement sensitivity.
Area of Science:
- Control Theory
- Robotics
- Systems Engineering
Background:
- Multiagent systems (MASs) with power integrators present unique control challenges.
- Existing methods often struggle with unknown measurement sensitivity and achieving prescribed performance.
Purpose of the Study:
- To develop an adaptive performance guaranteed tracking control for MASs with power integrators.
- To address unknown measurement sensitivity and ensure finite-time convergence of errors.
Main Methods:
- A novel 'adding a power integrator' technique is employed for consensus.
- Nussbaum gain and neural networks are utilized to handle unknown measurement sensitivity.
- Lyapunov functional method is used for stability and convergence proofs.
Main Results:
- Guaranteed tracking control is achieved for MASs with power integrators.
- Relative position errors converge within prescribed boundaries in finite time.
- The proposed method relaxes constraints on unknown measurement sensitivity.
Conclusions:
- The developed control strategy effectively ensures adaptive performance and guaranteed tracking.
- The approach is validated through simulations, demonstrating its practical applicability.
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