Approximate adaptive output feedback stabilization via passivation of MIMO uncertain systems using neural networks
Artemis K Kostarigka1, George A Rovithakis
1Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Summary
This study introduces an adaptive neural network controller for uncertain nonlinear systems. The controller ensures system stability and boundedness of states and outputs using a novel switching strategy.
Area of Science:
- Control Theory
- Nonlinear Systems
- Neural Networks
Background:
- Adaptive control is crucial for uncertain nonlinear systems.
- Output feedback control offers practical advantages over state feedback.
- Neural networks provide powerful tools for approximating complex system dynamics.
Purpose of the Study:
- To design an adaptive output feedback neural network controller.
- To achieve strict passivity for affine-in-the-control uncertain multi-input-multi-output (MIMO) nonlinear systems.
- To guarantee uniform ultimate boundedness of system states and outputs without stringent assumptions.
Main Methods:
- Design of an adaptive output feedback neural network controller.
- Utilizing a switching control strategy to avoid division by zero while ensuring continuity.
- Analysis of system stability and boundedness properties.
Main Results:
- The proposed controller renders the uncertain MIMO nonlinear systems strictly passive.
- Uniform ultimate boundedness of system states and outputs is guaranteed, even with small sets.
- All closed-loop signals are bounded, and chattering is alleviated through controller continuity.
Conclusions:
- The adaptive output feedback neural network controller effectively stabilizes uncertain nonlinear systems.
- The controller's design overcomes limitations of existing methods, such as normal form requirements and zero-state detectability.
- The approach is validated through simulations, demonstrating its practical applicability.
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