Active Vibration Control of the Smart Plate Using Artificial Neural Network Controller

Mohit Mohit,Deepak Chhabra,Suresh Kumar

doi:10.1155/2015/137068

Mohit Mohit, Deepak Chhabra + Show 1 more

Open Access

https://doi.org/10.1155/2015/137068

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Abstract

The active vibration control (AVC) of a rectangular plate with single input and single output approach is investigated using artificial neural network. The cantilever plate of finite length, breadth, and thickness having piezoelectric patches as sensors/actuators fixed at the upper and lower surface of the metal plate is considered for examination. The finite element model of the cantilever plate is utilized to formulate the whole strategy. The compact RIO and MATLAB simulation software are exercised to get the appropriate results. The cantilever plate is subjected to impulse input and uniform white noise disturbance. The neural network is trained offline and tuned with LQR controller. The various training algorithms to tune the neural network are exercised. The best efficient algorithm is finally considered to tune the neural network controller designed for active vibration control of the smart plate.

Highlights

In the present time, the research on the control of noise and vibration of the flexible structure is increasing remarkably
The idea consists of detectors to see these structural dynamics/vibrations, a controller to collect the detectors/sensors signals and give a suitable signal to the actuators, and the actuators to do its job according to the controller
With the changing initial parameters like forces, inputs and neural network controller are more efficient than Linear Quadratic Gaussian (LQG)

Summary

Introduction

The research on the control of noise and vibration of the flexible structure is increasing remarkably. With the changing initial parameters like forces, inputs and neural network controller are more efficient than Linear Quadratic Gaussian (LQG) In some experiments, they are utilized as a powerful device to control jerks to a building type structure which shows that it can be used in the prevention of earthquake and volcano-like natural disaster. Kumar et al [19] examined Linear Vector Quantisation (LVQ) neural network for reducing vibrations of an inverted L shape structure for the first three natural frequencies It overcomes the drawback of LQR controller as it cannot bear up with system parameters changing very rapidly like pay loads and position. An efficient controlling system is designed which can predict the controlling action to stop vibration of the cantilever plate

Methodology

Finite Element and State Space Formulation

Experimental Setup with Results and Discussion

Conclusion