Abstract
In this paper, an improved nonlinear Active Noise Control (ANC) system is achieved by introducing an appropriate secondary source. For ANC system to be successfully implemented, the nonlinearity of the primary path and time delay of the secondary path must be overcome. A nonlinear Model Predictive Control (MPC) strategy is introduced to deal with the time delay in the secondary path and the nonlinearity in the primary path of the ANC system. An overall online modeling technique is utilized for online secondary path and primary path estimation. The secondary path is estimated using an adaptive FIR filter, and the primary path is estimated using a Neural Network (NN). The two models are connected in parallel with the two paths. In this system, the mutual disturbances between the operation of the nonlinear ANC controller and modeling of the secondary can be greatly reduced. The coefficients of the adaptive FIR filter and weight vector of NN are adjusted online. Computer simulations are carried out to compare the proposed nonlinear MPC method with the nonlinear Filter-x Least Mean Square (FXLMS) algorithm. The results showed that the convergence speed of the proposed nonlinear MPC algorithm is faster than that of nonlinear FXLMS algorithm. For testing the robust performance of the proposed nonlinear ANC system, the sudden changes in the secondary path and primary path of the ANC system are considered. Results indicated that the proposed nonlinear ANC system can rapidly track the sudden changes in the acoustic paths of the nonlinear ANC system, and ensure the adaptive algorithm stable when the nonlinear ANC system is time variable.
Highlights
Noise reduction can be achieved by two different techniques
A modified nonlinear dynamic matrix control (DMC) algorithm for the active noise control (ANC) system is proposed in this paper
It can deal with the nonlinear of the primary path, the time delay and time variable of the secondary path
Summary
Noise reduction can be achieved by two different techniques. The first is using passive technique, which is based on the absorption and reflection properties of materials. In some practical cases, H(z) can be time varying or nonlinear [5,6] For these cases, on-line modeling of H(z) is required to ensure the convergence of the FXLMS algorithm for the ANC system. A model predictive control (MPC) technique has been introduced to the ANC system with on-line modeling techniques [14] It can ensure the performances of adaptive control algorithm and the adaptive identification algorithm when both primary path and secondary path are linear and time-variable. An MPC technique, using dynamic matrix control (DMC) is modified in the ANC system, whose primary path exhibits nonlinear behavior.
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