Combined Feedforward–Feedback Active Control of Road Noise Inside a Vehicle Cabin

Abstract

Conventional active control of road noise inside a vehicle cabin generally uses a pure feedforward control system with the conventional filtered-x least mean square (FXLMS) algorithm. While it can yield satisfactory noise reduction when the reference signal is well correlated with the targeted noise, in practice, it is not always possible to obtain a reference signal that is highly coherent with a broadband response typically seen in road noise. To address this problem, an active noise control (ANC) system with a combined feedforward–feedback controller is proposed to improve the performance of attenuating road noise. To take full advantage of the feedforward control, a subband (SFXLMS) algorithm, which can achieve more noise attenuation over a broad frequency range, is used to replace the conventional FXLMS algorithm. Meanwhile, a feedback controller, based on internal model control (IMC) architecture, is introduced to reduce the road noise components that have strong response but are poorly correlated with the reference signals. The proposed combined feedforward–feedback ANC system has been demonstrated by a simulation model with six reference accelerometers, two control loudspeakers and one error microphone, using actual data measured from a test vehicle. Results show that the performance of the proposed combined controller is significantly better than using either a feedforward controller only or a feedback controller only, and is able to achieve about 4 dBA of overall sound pressure level reduction.

A Hybrid Active Noise Control System for the Attenuation of Road Noise Inside a Vehicle Cabin

“…Moreover, in actual implementation, triaxial accelerometers will be replaced by uniaxial accelerometers, so the goal became the selection of dominant vibration signals among all signals. As suggested by [ 7 , 11 ], the vibration signals can be ranked by calculating the average coherence between the vibration signals and each error signal in the control range, and the flowchart is shown in Figure 3 . At the beginning, all the reference signals were calculated respectively to find the signal having the highest average coherence with the error signals, which was defined as the first dominant signal.…”

“…However, in practice, due to the complex propagation path between structural and acoustic noise, it is difficult to obtain good coherence in the frequency band of interest. To overcome this limitation, Duan et al [ 7 ] proposed a combined feedforward–feedback ANC system, which was formed by adding a feedback controller to the traditional feedforward ANC (tFANC) system; the simulation results showed that the hybrid control system can suppress the narrowband road noise component, which cannot be attenuated by feedforward structure operating alone, and it achieved a further 1 dBA of overall reduction. In their research, the internal model control (IMC) structure was used to build the feedback controller, which meant that the error signals and the secondary signals filtered by the estimation of secondary path were used to synthesize the reference signals for the feedback controller.…”

Modified Structures for Hybrid Active Sound Quality Control System Disturbed by Gaussian Random Noise

“…Considering that the feedforward and feedback structures have their respective advantages, hybrid structures were developed to reduce correlated noise and eliminate uncorrelated narrowband noise [20][21][22][23]. A decoupled hybrid algorithm was proposed by Wu et al to elevate the performance of the traditional hybrid control algorithm [15].…”

Rejection of unknown periodic disturbances for continuous‐time MIMO systems with dynamic uncertainties

“…In high-accuracy tracking systems, the identification and rejection of vibrational disturbances is crucial. In acoustic systems where low-frequency noise cannot be suppressed by passive absorbers, feedback control can be used to generate anti-noise and enhance the comfort level of an environment [10][11][12][13]. Performance of laser beam control systems is often adversely affected by vibrational disturbances that arise from the medium of propagation and structural vibrations in the platform.…”