Biologically inspired controller for sound applications

Abstract

A biologically inspired control approach for reducing radiated sound power from distributed elastic systems has been experimentally verified for harmonic, narrowband excitation. The control paradigm approximates natural biological systems for initiating movement, hi that a small number of signals are sent from an advanced, centralized controller (analogous to the brain) and are then distributed by local rules and actions to multiple actuators (analogous to muscle fiber). The controller was applied to attenuate radiated sound power from a plate. Experimental investigations of two different local learning rules, the phase variation and optimal methods, were carried out. Performance of the various methods decreased radiated sound power from a clamped plate by up to 16 dB for harmonic off-resonance excitation and up to 22 dB for on-resonance excitation. In general the results have demonstrated that the biological control approach has the potential to control multimodal response in distributed elastic systems using an array of many actuators with a reduced-order main controller. Thus significant reductions in control system computational complexity have been realized by this approach. Nomenclature / = cost function M = number of microphones v = error sensor voltage y = slave actuator complex gains \JL = convergence parameter I. Introduction R ECENT work has demonstrated the potential of active control of distributed elastic systems using multiple, independent actuators and sensors. In work concerned with the control of sound radiation from vibrating panels, the importance of number of channels of control and optimization of the transducer position and shape has been demonstrated.1 However, these investigations were carried out for a fixed frequency, and it is apparent that for good control over a bandwidth of frequencies the control actuators and sensors need to be adaptive in shape. At first sight this problem could be solved using an overall transducer broken up into many individual small elements, each connected by an individual control channel. In this situation the control transducer would effectively reoptimize its configuration for different conditions by adaptively weighting each transducer segment. Meirovitch and Norris2 have demonstrated the advantage of such an approach by considering fully distributed control in reducing control spillover. The disadvantage of this approach is that, for systems with a high modal density, the number of actuators and sensors required becomes extremely large. A high number of control channels has a number of problems mainly associated with memory requirements and computational time in the hardware systems used to implement the control. In addition, collinearity of transducer transfer functions (i.e., the transducer transfer functions are not fully linear independent) causes stability problems in systems with a high number of transducers. A new approach of controlling distributed elastic systems is presented. The approach is inspired by the action of biological natural systems where a small number of main signals are transmitted from the brain to a large area of muscle tissue to activate many independent segments of muscle. The signals then stimulate local action that is governed by, for example, chemical interaction of locally