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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]

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]