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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Related Experiment Video

Updated: Sep 16, 2025

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

Dynamic neural network switching for active control of nonlinear systems.

Xander Pike¹, Jordan Cheer¹

¹Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, United Kingdom.

The Journal of the Acoustical Society of America

|July 7, 2025

Summary

This study introduces a novel switching neural network (NN) strategy for active noise and vibration control. This approach enhances performance in nonlinear systems by efficiently managing computational load compared to traditional methods.

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Area of Science:

Control Engineering
Applied Mathematics
Signal Processing

Background:

Traditional feedforward active noise and vibration control (ANC/AVC) systems often use linear digital filters, such as the filtered reference least mean squares (LMS) algorithm.
Linear controllers struggle to accurately model and control systems exhibiting nonlinear dynamics, leading to suboptimal performance.
Neural network (NN) controllers show promise for nonlinear control but can be computationally intensive, limiting their real-time application.

Purpose of the Study:

To develop a computationally efficient control strategy for active noise and vibration control in nonlinear systems.
To maintain high control performance across a range of nonlinear system behaviors.
To address the computational expense associated with traditional neural network controllers in dynamic environments.

Main Methods:

A novel control strategy employing dynamically switching neural networks (NNs) is proposed.
Individual NNs are trained for specific operating ranges of the nonlinear system.
The system dynamically switches between these smaller, specialized NNs to adapt to varying nonlinearities.

Main Results:

The proposed switching NN approach demonstrated superior control performance compared to a single, larger generalized NN controller.
Performance advantage was also observed over a functional link artificial neural network (FLANN) based controller.
Simulations included a system with nonlinear stiffness and offline tests on a physical nonlinear dynamical system.

Conclusions:

Dynamically switching between smaller, specialized NNs is an effective strategy for improving active noise and vibration control in nonlinear systems.
This method offers a practical solution to the computational challenges of NN-based control in nonlinear dynamic environments.
The proposed approach provides a significant performance advantage over existing NN and linear control techniques for nonlinear applications.