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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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.

S N Huang¹, K K Tan, T H Lee

¹Dept. of Electr. and Comput. Eng., Nat. Univ. of Singapore, Singapore.

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.

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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.