Masters Theses


"In this study, an adaptive critic-based neural network is developed for optimal control of structures and implemented on a Multi-Input Multi-output (MIMO) system in an attempt to minimize its vibrations. This MIMO system consists of two vertically suspended cantilever beams interconnected by three blocks of wood and is embedded with piezoelectric sensors and actuators and interfaced to a PC with a DAS 1602 data acquisition card. The system model is first acquired by experimental methods. The adaptive critic-based controller for the system model consists of two multi-layer perceptron networks called the action and the critic networks. The action network outputs the optimal control while the critic network outputs the co-state vector and critiques the control output from the action network. The networks are trained offline for a wide range of initial conditions using the equations obtained by Dynamic Programming methodology. After a required degree of convergence is achieved the resulting action network is used for optimal control in a feedback loop. Since all the states are not available for feedback, a Kalman estimator is designed for state estimation. Using the state estimates the neuro-controller is implemented on the structure and the experimental results are obtained. These results are compared with that of an optimal output feedback controller. A reduced order model of the system is then determined and a neurocontroller is designed for the same and implemented on the system. A robust controller is also designed for the second order system to provide “extra control” when there exists uncertainties in the system that makes the existing controller lose its optimality. The performance characteristic of the reduced order neuro-controller is compared with the full-order neuro-controller and the robustness of the neuro-controller is analyzed"--Abstract, page 3.


Balakrishnan, S. N.

Committee Member(s)

Liou, Frank W.
Saygin, Can


Mechanical and Aerospace Engineering

Degree Name

M.S. in Mechanical Engineering


National Science Foundation (U.S.)


The support received from the National Science Foundation grant ECS 9976588 for this research is gratefully appreciated.


University of Missouri--Rolla

Publication Date

Summer 2002


vii, 66 pages

Note about bibliography

Includes bibliographical references (pages 62-65).


© 2002 Vijayakumar Janardhan, All rights reserved.

Document Type

Thesis - Restricted Access

File Type




Subject Headings

Vibration -- Control
Neural networks (Computer science)

Thesis Number

T 8120

Print OCLC #


Link to Catalog Record

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