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Coordinated decentralized adaptive output feedback control of interconnected systems
Naira Hovakimyan1, Eugene Lavretsky, Bong-Jun Yang
1Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. nhovakim@vt.edu
IEEE Transactions on Neural Networks
|March 1, 2005
Summary
This study introduces a decentralized adaptive control method for large interconnected systems using neural networks to manage system interconnections and ensure stable performance. The approach guarantees bounded error signals for improved tracking accuracy.
Area of Science:
- Control Engineering
- Systems Science
- Artificial Intelligence
Background:
- Large-scale interconnected systems present significant control challenges due to complex interdependencies.
- Achieving robust tracking performance in such systems requires advanced control strategies.
- Existing methods may struggle with the dynamic nature and scale of these systems.
Purpose of the Study:
- To design a decentralized adaptive output feedback control strategy for large-scale interconnected systems.
- To mitigate the impact of interconnections on system tracking performance.
- To ensure the stability and boundedness of error signals within the control framework.
Main Methods:
- A decentralized adaptive control architecture is proposed.
- Linearly parameterized neural networks are employed for each subsystem.
- Lyapunov's direct method is utilized to prove signal boundedness.
Main Results:
- The proposed control design effectively manages interconnections.
- Partial cancellation of interconnection effects on tracking performance is achieved.
- The boundedness of error signals is rigorously demonstrated.
Conclusions:
- The developed decentralized adaptive control is effective for large-scale interconnected systems.
- The use of neural networks offers a viable approach for handling system interconnections.
- The control strategy ensures system stability and reliable tracking performance.