ASA 129th Meeting - Washington, DC - 1995 May 30 .. Jun 06

3pEA8. The use of neural networks for optimum actuator grouping in time domain active control applications.

G. Clark Smith

Chris R. Fuller

Vib. and Acoust. Labs., Dept. of Mech. Eng., Virginia Polytechnic Inst. and State Univ., Blacksburg, VA 24061-0238

Richard J. Silcox

NASA Langley Res. Ctr., Hampton, VA 23665-0001

Previous work has demonstrated the benefit of grouping actuators to decrease the number of degrees of freedom in an active control system. In this work, a time-domain cost function was developed for on-line actuator grouping and active structural acoustic control (ASAC) of a simply-supported beam excited with a broadband disturbance. Actuators are considered grouped when their compensators are equal. Therefore, the cost function presented here incorporates a mean-square error term related to the structure-borne noise and an additional nonquadratic term which penalizes the controller for differences between respective compensator coefficients. The backpropagation neural network algorithm provides the proper procedure to determine the minimum of this cost function. The main disadvantage of using such a stochastic gradient technique while searching the prescribed control surface is converging to local minima. A resolution to this problem is discussed which incorporates using a variety of initialization conditions. Two scenarios are considered here: grouping actuators based upon weights determined by converging the filtered-x LMS algorithm and simultaneously grouping and controlling with the compensator weights started at zero. Computer simulations demonstrate the ability of this new form of the cost function to simultaneously group actuators and control the structure-borne noise with either initial conditions.