An improved Takagi–Sugeon–Kang-based FxLMS algorithm and its active noise control in electric bus driving position

This paper proposed an improved filtered-x least mean square (FxLMS) algorithm based on the fuzzy control rule of Takagi–Sugeon–Kang (TSK) to solve the drawback of slow convergence for standard FxLMS algorithm. The TSK-FxLMS is a control framework with two inputs and three outputs constructed by the error signal and its integral as input variables. To validate its effectiveness and applicability, in-vehicle active noise control (ANC) modelling and adaptive noise reduction at the driver’s position of an electric bus are thoroughly investigated. Firstly, the four noise signals for the different working conditions at 50 km/h, acceleration, coasting and braking are collected, and their spectral analyses are performed. Secondly, the effects of step size and fuzzy control parameters of ANC model on weight convergence and noise reduction effect are analysed and determined. Finally, the results of calculating and comparing the residual signals’ waveforms, frequency spectra, sound pressure levels and tracking performance indicate that the proposed TSK-FxLMS algorithm outperforms the standard FxLMS algorithm with faster convergence and better noise reduction effect.