Eigenvalue equalization filtered-x (EE-FXLMS) algorithm applied to the active minimization of tractor noise in a mock cabin

The active control of tractor noise requires the ability to track and control a signal that changes in frequency as the speed of the engine, in revolutions per minute (rpm), changes during operation. The most common control approach is typically based on some version of the filtered-x algorithm. For this algorithm, the convergence and tracking speed are functions of the frequency dependent eigenvalues of the filtered-x autocorrelation matrix. To maintain stability, the system must be implemented based on the slowest converging frequency that will be encountered. This often leads to significant degradation in the overall performance of the control system. This paper will present an approach which largely overcomes this frequency dependent performance, maintains a relatively simple control implementation, and improves the overall performance of the control system. The control approach is called the eigenvalue equalization filtered-x least mean squares (EE-FXLMS) algorithm and its effectiveness will be demonstrated through an application to tractor noise in a mock cab. Experimental results will be presented which show that the EE-FXLMS algorithm has faster convergence times and provides on average a 1 dB increase in attenuation. A 3.5 dB increase in attenuation was seen in some of the cases presented.