The core objective of Active Vibration and Noise Control (AVNC) is to enhance system performance by generating real-time counter-phase signals of equal amplitude to cancel out vibration and noise interference from mechanical or structural systems. As the demand for low-noise, low-vibration environments grows in fields such as new energy vehicles (NEVs), aerospace, and high-precision manufacturing, traditional AVNC methods—which rely on precise linear models and have poor adaptability to nonlinear and time-varying conditions—struggle to meet the dynamic requirements of complex engineering scenarios. However, with advancements in artificial intelligence (AI) technology, AI-driven Active Vibration and Noise Control (AI-AVNC) technology has garnered significant attention from both industry and academia. Based on a thorough investigation into the state-of-the-art AI-AVNC methods, this survey has made the following contributions: (1) Introducing the theoretical foundations of AVNC and its historical development; (2) Classifying existing AI-AVNC methods from the perspective of control strategies; (3) Analyzing engineering applications of AI-AVNC; (4) Discussing current technical challenges and future development trends of AI-AVNC.

In-wheel motors (IWMs) have become a promising solution for electric vehicles due to their compact design, high integration, and flexible torque control. However, their exposure to harsh operating conditions increases the risk of mechanical, electrical, and magnetic faults, making reliable fault diagnosis essential for ensuring driving safety and system reliability. Although considerable progress has been made in fault diagnosis techniques related to IWMs, a systematic review in this area is still lacking. To address this gap, this paper provides a comprehensive review of fault diagnosis techniques for IWMs. First, typical faults in IWMs are analyzed with a focus on their unique structural and failure characteristics. Then, the applications and recent research progress of three major categories of fault diagnosis approaches—model-based, signal-based, and knowledge-based methods—in the context of IWMs are critically reviewed. Finally, key challenges and pain points in IWM diagnosis are discussed, along with promising future research directions.

The housing and pre-compressed spring of the integrated piezo longitudinal actuator can protect the piezoelectric stack from shear stress and tensile stress. In this paper, the limitations of this actuator model in existing vibration control projects are pointed out. An electromechanical coupling dynamic model in the transfer matrix form is established for the longitudinal vibration of the integrated piezo longitudinal actuator. The model includes all parameters of the integrated piezo longitudinal actuator: force and velocity at both ends, material and structural parameters, voltage and current, which is suitable for any mechanical and electrical boundary conditions; The transfer matrix method has a simple structure and clear physical definition, which is convenient to be applied to the vibration control system. This model is verified by the finite element software and experiments. In the experimental verification, an electromechanical coupling dynamic experiment system of the integrated piezo longitudinal actuator is established. The accuracy of this model is verified by comparing the frequency domain dynamic response of various parameters in the simulation and experiments. The integrated piezo longitudinal actuator model provides a wide range of possibilities for model-based controller design, active and semi-active control, electric power estimation, and optimal vibration isolation performance estimation.

Current control laws for active control of helicopter structural vibration are designed for steady‐state flight conditions, while the vibration response of maneuvering flight has not been taken into consideration yet. In order to obtain full‐time vibration suppression capability, the authors propose a filtered least mean square‐mixed sensitivity robust control method based on reference signal reconstruction (LMS‐MSRC), driving piezoelectric stack actuators to suppress helicopter structural vibration response in maneuvering flight. When feedback controller designed by H_∞ theory is implemented, active damping is added on the secondary path to weaken the adverse effects of its sudden changes in maneuvering flight state. Furthermore, a reference signal reconstruction scheme is given concerning equivalent secondary path. In addition, the reconstruction accuracy, the convergence speed, stability, and global validity of the hybrid controller are analysed. Compared with multichannel Fx‐LMS, numerical simulations of LMS‐MSRC for vibration suppression are undertaken with a helicopter simplified finite element model under several typical flight conditions. Further experiments of real‐time free‐free beam vibration control are performed, driven by a stacked piezoelectric actuator. The instantaneous overshoot of measured response is 42% less than the peak value and its attenuation reaches 85% within 2.5 s. Numerical and experimental results reveal that the proposed algorithm is practical for suppressing transient disturbance and multifrequency helicopter vibration response during maneuvering flight with faster convergence speed and better robustness.

To solve the problem of secondary path mutation and external disturbance abrupt changes during helicopter maneuver flight, the previous research proposed a hybrid active vibration control law. To improve the engineering applicability, the original algorithm is ameliorated to the least mean square-input-output-based robust (LMS-IOBR) algorithm. The system model within the target frequency band can be identified through the input-output data to avoid constructing complex state observers. In addition, the output form of the feedback controller is constructed by an autoregressive moving average model with extra input, which is beneficial to improve operational efficiency. Numerical simulations demonstrate that compared with the original algorithm, controller real-time computation can be reduced by 52% with control effects guaranteed at the same time. Furthermore, to verify the effectiveness and adaptability of LMS-IOBR, multi-input multioutput vibration control experiments are carried out on a specially developed simple platform for simulating helicopter maneuver states. Comparative tests in various typical states are performed between the LMS-IOBR and the multichannel least mean square algorithm. Under the complex circumstances of simulating continuous subduction uplift, the peak response of closed-loop system attenuates by 80% and 70%, and the vibration of two points is reduced to 15% and 20%, respectively, within 3 s. The experimental results demonstrate that the proposed LMS-IOBR algorithm shows stronger transient adaptability and robustness against external disturbance excitation and secondary channel mutation in helicopter maneuver flight.