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The automotive industry has transitioned from internal combustion engine (ICE) vehicles to electric vehicles (EVs) in recent years. Also essential to the development of the next generation are the enhancement of engine performance and production of lightweight vehicle bodies. Through this method, it is possible to increase vehicle output. However, as the vehicle's excitation strength increases, structural noise and vibrations caused by the engine increase. Intelligent structure-based active mounting systems are widely used in the automotive industry to reduce vibration and noise. It can continuously control the dynamic characteristics of the mount system, enabling real-time control of active mounting systems. When determining the position of the active mounting system, it must be installed in the most efficient position possible. Therefore, the purpose of this study is to determine the optimal placement criteria for an active mounting system consisting of a piezoelectric actuator and a rubber grommet. The lumped parameter model is used to model the plate structure with two hybrid mounting systems based on the source–path–receiver structure. In addition, the forces of two active mounts are calculated using static and dynamic approaches. When the calculated force on the hybrid mount system is relatively low, the location with the greatest vibration reduction can be determined, and this location can be regarded as the optimal location for the active mounting system. In addition, a feasibility test is conducted to validate the proposed criteria and validate the static and dynamic results. The findings indicate that the optimal location for the active engine mounting system can be determined. [ABSTRACT FROM AUTHOR]

A digital adaptive filtering system is applied to various fields such as current disturbance, noise cancellation, and active vibration and noise control. The least mean squares (LMS) algorithm is widely adopted, owing to its simplicity and low computational burden. A limitation of the LMS algorithm with fixed step size is the trade-off between convergence speed and stability. Several studies have tried to overcome this limitation by varying the step size according to filter input and error; however, the related algorithms with variable step size have not been suitable for signals with complex frequency spectra. As the error decreases, the quality of the output signal deteriorates due to the increase in the higher-order components, depending on the characteristics of the algorithm. Therefore, a novel adaptive filtering algorithm was proposed to overcome these drawbacks. It increased the stability of the system by decreasing the step size using an exponential function. In addition, the error was reduced through normalization using the power of the input signal in the initial state, and the misadjustments in the system were adjusted properly by introducing an energy autocorrelation function of instantaneous error. Furthermore, a novel multi-staged adaptive LMS (MSA-LMS) algorithm was introduced and applied to active periodic structures. The proposed algorithm was validated by simulation and observed to be superior to the conventional LMS algorithms. The results of this study can be applied to active control systems for the reduction of vibration and noise signals with complex spectra in next-generation powertrains, such as hybrid and electric vehicles. [ABSTRACT FROM AUTHOR]

Traditional adaptive filtering algorithms are non-recursive systems that cannot use a time-variant reference input in real time and are not appropriate for control signals with uncertainties and unanticipated conditions. The main purpose of this research is to design novel adaptive digital filtering algorithms based on internal model control (IMC). The new methods consist of a process model for the target plant so as to estimate its dynamic behavior for active vibration and noise attenuation schemes in order to improve the stability, robustness, and tracking performance. On the basis of on the existing least mean squares, the methods are combined with an internal model controller, or the whole adaptive filtering system could become a feedback control system structure based on IMC. The performances were validated in numerical simulations with various conditions that could have happened in realistic applications, and the results were compared with the original algorithms. This study shows that the active noise and vibration systems that are applied to vehicles, mechanical systems, and other targets are enhanced through improving the performance of conventional adaptive filtering algorithms and by using internal model control effectively. [ABSTRACT FROM AUTHOR]

This article focuses on the active control of a combination of incommensurate sinusoids and its subsets. Two sliding mode control (SMC) algorithms are proposed to enhance the conventional least mean squares (LMS) algorithm maintaining its inherent advantages. The SMC is utilized to update the filter coefficients, and a feedback loop with the SMC is introduced in the adaptive filtering system. The proposed methods are evaluated for several cases by comparing the results with the LMS algorithm. An experiment with an active strut is also constructed and longitudinal vibrations excited by an amplitude modulated type signal are measured before and after the control for each adaptive filtering algorithm. Overall, the proposed algorithms demonstrate faster convergence and reduce amplitudes at multiple peaks even when only one frequency is targeted. [ABSTRACT FROM PUBLISHER]