Multivariable Neural Network Based Controllers for Smart Structures

Abstract

This paper details identification and robust control of smart structures using artificial neural networks. To demonstrate the use of artificial neural networks in the control of smart structural systems, two smart structure test articles were fabricated. Active materials like piezoelectric (PZT), polyvinylidene (PVDF) and shape memory alloys (SMA) were used as actuators and sensors. The Eigensystem Realization Algorithm (ERA), a structural identification method has been utilized to determine a minimal order discrete time state space model of the test articles. The ERA requires the Markov parameters of the physical system. A neural network based method has been developed to estimate the Markov parameters of a multi input multi output system from experimental test data. The accelerated adaptive learning rate algorithm and the adaptive activation function were utilized to improve the learning characteristics of the network and reduce the learning time. The identified models were used to design a robust controllers for vibration suppression of smart structures using a modified Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) method. This control design methodology has better loop transfer recovery properties while accommodating the limited control force available from the SMA and the PZT actuators. This controller was copied into a feedforward neural network using the connectionist approach. This neural network controller was implemented using a PC based data acquisition system. The closed loop performance and robustness properties of the conventional and the neural network based controller are compared experimentally.