An adaptive algorithm implemented on a digital signal processor is used in conjunction with an analog multiplier circuit to compensate for the feedthrough capacitance of a piezoelectric sensoriactuator. The mechanical response of the piezoelectric sensoriactuator is resolved from the electrical response by adaptively altering the gain imposed on the electrical circuit used for compensation. For broadband, stochastic input disturbances, the feedthrough dynamic capacitance of the sensoriactuator can be identified on-line, providing a means of implementing directrate-feedback control in analog hardware. Rate feedback control is demonstrated on a cantilevered beam using the hybrid/adaptive circuit. The ability of the circuit to readapt to a change in capacitance is demonstrated by a simple experiment.= volts, voltage w = digital filter weight 3 = partial differential operator S (n) = Dirac delta function 0 = electromechanical coupling matrix of piezoceramic I.L = convergence parameter xy(n) -discrete correlation function between x(n) and y(ri) J7 2 = matrix of natural frequencies V = gradient operator Subscripts DSP = digital signal processor elec = electrical mech = mechanical Out = output signal p = piezoceramic pa = power amplifier R = reference leg 01 = leg 1 of conditioning circuit 02 = leg 2 of conditioning circuit 03 = leg 3 of conditioning circuit

This paper demonstrates active structural acoustic control using multiple input/output adaptive sensoriactuators combined with radiation filters and a feedback control paradigm. A new method of reduced order modeling/design of radiation filters termed radiation modal expansion (RME) is presented. For the experiments detailed in this paper, the RME technique reduced the modeling of the radiation matrix from 400 transfer functions to 6 transfer functions (multiplied by a constant transformation matrix). Experimental results demonstrate reductions of radiated sound power on the order of 5 dB over the bandwidth of 0-800 Hz.

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Due to civilian noise complaints and damage claims, there is a need to establish an accurate record of impulse noise generated at military installations. Current noise monitoring systems are susceptible to false positive detection of impulse events due to wind noise. In order to analyze the characteristics of noise events, multiple channel data methods were investigated. A microphone array was used to collect four channel data of military impulse noise and wind noise. These data were then analyzed using cross-correlation functions to characterize the input waveforms. Four different analyses of microphone array data are presented. A new value, the min peak correlation coefficient, is defined as a measure of the likelihood that a given waveform originated from a correlated noise source. Using a sound source localization technique, the angle of incidence of the noise source can be calculated. A method was also developed to combine the four individual microphone channels into one. This method aimed to preserve the correlated part of the overall signal, while minimizing the effects of uncorrelated noise, such as wind. Lastly, a statistical method called the acoustic likelihood test is presented as a method of determining if a signal is correlated or not.

In recent years adaptive feedforward control algorithms have been successfully implemented to attenuate the response of systems under persistent disturbances such as single and multiple tones as well as random inputs. System causality is not an issue when the excitation is sinusoidal because of its deterministic nature. However, causality is a very important factor in broadband control. Though significant deterioration in the performance of noncausal control systems have been reported in the literature, analytical tools are virtually nonexistent to predict the behavior of broadband controllers. The main objective of this research is to develop an approach to investigate system causality. A formulation is presented to address the effectiveness of a control configuration as a function of the filter size, delay time, and dynamic properties of the structure. The technique is illustrated in a simple numerical example and the results are also corroborated experimentally.