Robust control of smart structures using neural network hardware

Rajendra Damle, Vittal Rao, Frank Kern

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In this paper, the use of Intel's Electronically Trainable Analog Neural Network (ETANN) chipi80170NX for implementing single-chip robust controllers for smart structures is successfully demonstrated. Robust controllers like the linear quadratic regulator (LQR) and linear quadratic Gaussian with loop transfer recovery (LQG/LTR) are implemented in various configurations using the ETANN chip on the smart structure test article. The test article is a cantilevered plate with PZTs as actuators and shaded PVDF film as sensors. The spatially distributed sensors allow direct measurement of the states of the system enabling the implementation of the LQR controller. A two step connectionist approach is used to design and implement the neural network based controllers. First a robust controller is designed using conventional design techniques to meet the required closed loop performance. The controller dynamics are copied into the ETANN chip and the trained chip is used to control the test structure. A custom interface board and external signal conditioning circuits are developed to interface the neural network chip with the sensors and actuators on the smart structure test article. The steps involved in training and implementing the robust controllers on a smart structure are detailed. Some of the practical considerations of implementing the controller using the ETANN chip are pointed out and some suggestions made to deal with the limitations. Simulation and experimental results of the closed loop system with all the controller implementation models are presented.

Original languageEnglish
Pages (from-to)301-314
Number of pages14
JournalSmart Materials and Structures
Volume6
Issue number3
DOIs
StatePublished - Jun 1997

Fingerprint

Dive into the research topics of 'Robust control of smart structures using neural network hardware'. Together they form a unique fingerprint.

Cite this