Title

Development and Implementation of Adaptive Critic Based Optimal Neurocontroller on a Cantilever Plate

Abstract

In this study an adaptive critic-based optimal neurocontroller was developed and applied to a non-linear cantilevered plate system to improve its vibrational characteristics. The smart structural system which incorporates a two dimensional aluminum plate mounted with PZT actuators and PVDF sensors is interfaced to a PC with a DAS1600 data acquisition card. The neural network plant model was acquired from a previous study on the setup. Model based design approach was used to design the optimal neuro-controller which minimizes an infinite horizon quadratic cost function by solving the equations obtained by the Dynamic Programming methodology. Two multi-layer perceptron networks the action and the critic were trained off-line for a wide range of initial conditions in the specified range for the state vector. The critic (supervisor) network in our case outputs the co-state vector and critiques the output of the controller network. After convergence to a desired degree of accuracy the action (controller) network gives an optimal output in a feedback form and can be directly incorporated in the control loop. The controller is finally tested for a range of initial conditions and the experimental results were compared for a time response of the plant with and without control.

Meeting Name

American Control Conference (1999, San Diego, CA)

Department(s)

Mechanical and Aerospace Engineering

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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