Vibration present on various levels in many engineering fields and hence vibration mitigation has become a subject of intense study. The nonlinear vibration isolation devices are effective for broad frequency bandwidth and can provide better vibration isolation than linear devices. The need for nonlinearity in stiffness and damping characteristics has motivated researchers to apply the nonlinearity found in mechanisms or materials in the passive vibration control devices. This review discusses the applications of nonlinearity in the passive vibration control devices to provide an understanding of how the nonlinearity is applied and useful in the implemented system. Further, applications for nonlinearity can also be extended in the energy harvesting devices, Nonlinear energy sink, metamaterials for the purpose of vibration isolation and energy harvesting. The need for nonlinearity also encouraged research work through inspiration from the nature called bio-inspired devices. The bio-inspired devices mimic the nonlinearity of the biological system to suppress the vibrations. The nonlinear passive isolation is effective for wide frequency bandwidth than the linear isolation system. Further, the nonlinear systems also reduce transmissibility much efficiently than the linear system. The nonlinear energy harvesting system shows a great scope to harvest energy from wide ranges of excitations. The bio-inspired devices also are proven to be effective in vibration isolation. Additionally the design of the metamaterial with nonlinearity in the microstructure, proves to be promising in the vibration suppression applications. Based on the review, the nonlinearity introduced into the systems has greater benefits than the linear systems.

Applications of Nonlinearity in Passive Vibration Control: A Review

This paper aims to fabricate star polygon thin walled energy absorption structure that tends to relapse the intensity of set in decelerations during impact while escalating the amount of energy absorbed. The crashworthiness topology optimization is used for structural optimization of various foam-filled tubes. The relative advantages of 14 configurations are discussed, and the effects of filling five types of foam into the best configuration of the star-shaped tube over an empty one is investigated. Specific Energy Absorption, Peak Crush Force and response of weight of the members during frontal impact are the main dimensions parameters of the member's performance. Numerical simulation is carried out using the explicit dynamics of Ansys 17.1, and obtained results are compared with experimental results conferring crash behavior and energy absorption characteristics. Based on results, the suited configuration with required performance in crashworthiness is suggested, which shall be incorporated in automobiles for safety consideration of passengers during an impact. The results show an increment of 40% in Specific Energy Absorption suggesting a better choice of a particular type of foam over a hollow tube.

Crashworthiness analysis of foam filled star shape polygon of thin-walled structure

The performance of vehicle body vibration and ride comfort of active or semi-active suspension with proper control is better than that with passive suspension. The key to achieve good control effect is that the suspension control system should have strong real-time learning ability according to changes in the road surface and suspension parameters. In the control strategies adopted by previous researchers, the classical neural network controller has some learning ability, but it is mainly based on offline learning with a large number of samples. In this paper, the deep reinforcement learning strategy is used to solve the above problems.Aiming at the continuity of state space and execution action in vehicle active suspension system, the control of the semi-active suspension is realized by using improved DDPG (Deep Deterministic Policy Gradient) algorithm. To overcome the shortcoming of low efficiency of this algorithm in the initial stage of learning, the DDPG algorithm is improved and using empirical samples in the learning method is proposed. Based on Mujoco, the physical model of semi-active suspension is established, and its dynamic characteristics are analyzed under the condition of various road level and vehicle speed. The simulation results show that compared with the passive suspension, the semi-active suspension based on improved DDPG algorithm with learning method using experienced samples can better adapt to various road level, more effectively reduce the vertical acceleration of the vehicle body and the dynamic deflection of the suspension, and further improve the ride comfort.

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Semi-Active Suspension Control Based on Deep Reinforcement Learning

In this paper, the semi-active suspension in railway vehicles based on the controlled magnetorheological (MR) fluid dampers is examined, and compared with the semi-active low and semi-active high s...

Design and Development of Smart Semi Active Suspension for Nonlinear Rail Vehicle Vibration Reduction

This paper is based on an application of mobile sensing: sensing and gathering the surface condition of roads. We will fabricate a system and mention the required algorithms to sense the road anomalies by making a portable sensor that can be equipped in any car or public transport. We will call this system pothole detection system (PDS), it will use the mobility of the particular vehicle on which the system will be fitted, and side by side gather data from the vibrations and the GPS sensors, and further process and filter the data to monitor road surface condition. At first, we will deploy the PDS on our own vehicle and test it out in a particular sector of Noida. Using the machine learning approach, we were able to identify and classify the potholes and other road anomalies from the accelerometer data. From the continuous testing and gathering data on a particular stretch of road, we were able to put an algorithm that will successfully detect a pothole with 4.3% chance of failure or if the pothole is too small to be detected. It was further conducted a manual inspection of the reported potholes and found that 80% of the road anomalies reported are in need of serious repair.

Pothole Detection and Warning System for Indian Roads

In this paper, Bouc–Wen type magnetorheological fluid damper has been used to monitor the ride quality of a prevailing rail vehicle in lateral vibrations. Modelling of the rail vehicle is done in such a manner that it has an entire 9 degrees of freedom by significant considerations of lateral, roll and yaw motions of the car body, rear, and the front chassis. 200 km/h is considered as train speed for tracks with two varying disturbances. A system consisting of multibody in VI-rail software is provided by a track input and ergo, wheel response it obtained. SIMULINK (software) is responsible for the representation of the motions of the wheel as mathematical models. Two different types of analysis are done firstly with conventional passive lateral damper and secondly with semi-active MR lateral damper in subordinate suspension. To diminish lateral vibrations, the disturbance refusal and non-stop state controller algorithms were executed to manage the damper force. Results acquired are in the form of acceleration and displacement of the center of mass of the body under consideration is done by comparing in terms of reduction indices of their vibrations. A significant improvement in the index is seen in which a semi-active lateral damper is mounted. The results show that the proposed system significantly improves both, the vibration attenuation ability and the ride quality of the vehicle.

Umesh Saini

Papers

Semi-active Control to Reduce Lateral Vibration of Passenger Rail Vehicle Using Disturbance Rejection and Continuous State Damper Controllers