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Updated: Jul 7, 2026
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
A decentralized control of interconnected systems using neural networks.
IEEE Transactions on Neural Networks
|February 5, 2008
Summary
We developed a novel decentralized neural-network (NN) controller for complex nonlinear systems. This innovative approach ensures system stability by addressing nonlinearities and high-order interconnections effectively.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Nonlinear Dynamics
Background:
- Large-scale nonlinear systems present significant control challenges due to complex dynamics and interconnections.
- Existing control methods often struggle with approximating unknown nonlinearities and managing high-order system interactions.
Purpose of the Study:
- To design a decentralized neural-network (NN) controller for large-scale nonlinear systems.
- To address the challenges posed by unknown nonlinearities and high-order interconnections within these systems.
- To ensure the stability of the controlled large-scale systems.
Main Methods:
- Development of a mixed neural-network (NN) controller architecture.
- Utilizing a conventional NN to approximate unknown nonlinearities in subsystems.
- Employing a specialized NN to counteract high-order interconnections.
Main Results:
- The proposed mixed NN controller effectively approximates nonlinearities.
- The specialized NN successfully mitigates high-order interconnections.
- Stability of the large-scale nonlinear system is mathematically proven with the developed controller.
Conclusions:
- The developed decentralized NN controller provides a stable control solution for large-scale nonlinear systems.
- The mixed NN structure is effective in handling both unknown nonlinearities and complex interconnections.
- This approach offers a promising direction for advanced control of complex dynamical systems.
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