Xiaomin Dong

A new variable stiffness absorber based on magneto-rheological elastomer

A new adaptive variable stiffness absorber was proposed based on a smart material, magnetorheological elastomer (MRE), and its vibration control performance was investigated. Before developing the proposed absorber, the MREs were firstly fabricated by curing a mixture of 704 silicon rubber, carbonyl iron particles and a small amount of silicone oil under an external magnetic field. Then the mechanical properties of the fabricated MREs were measured. On the basis of the measured mechanical characteristics, the MRE absorber was developed and its working characteristics were also tested under various input currents and excited frequencies. Finally, the control responses of a two-degree-of-freedom dynamic system with a MRE absorber were presented under a chirp input and used to evaluate the effectiveness of the MRE absorber.

Neural network compensation of semi-active control for magneto-rheological suspension with time delay uncertainty

This study presents a new intelligent control method, human-simulated intelligent control (HSIC) based on the sensory motor intelligent schema (SMIS), for a magneto-rheological (MR) suspension system considering the time delay uncertainty of MR dampers. After formulating the full car dynamic model featuring four MR dampers, the HSIC based on eight SMIS is derived. A neural network model is proposed to compensate for the uncertain time delay of the MR dampers. The HSIC based on SMIS is then experimentally realized for the manufactured full vehicle MR suspension system on the basis of the dSPACE platform. Its performance is evaluated and compared under various road conditions and presented in both time and frequency domains. The results show that significant gains are made in the improvement of vehicle performance. Results include a reduction of over 35% in the acceleration peak-to-peak value of a sprung mass over a bumpy road and a reduction of over 24% in the root-mean-square (RMS) sprung mass acceleration over a random road as compared to passive suspension with typical original equipment (OE) shock absorbers. In addition, the semi-active full vehicle system via HSIC based on SMIS provides better isolation than that via the original HSIC, which can avoid the effect of the time delay uncertainty of the MR dampers.

Fuzzy Neural Network Control for Vehicle Stability Utilizing Magnetorheological Suspension System

This paper presents the stability of a passenger vehicle system featuring magnetorheological (MR) suspensions. As a first step, MR damper is devised and its field-dependent damping force is experimentally evaluated. A full-car model equipped with the MR damper is established by considering roll motion which is directly related to the vehicle stability. A fuzzy neural network controller (FNNC) incorporated with the self-learn knowledge is then formulated in order to improve vehicle stability. In addition, in order to eliminate adverse effect of the system coupling, nonlinearity and time delay a correction component for updating the weighting matrix and adjusting controller outputs is designed. Both computer simulation and road test are undertaken in order to demonstrate the effectiveness of the proposed control method. The control results obtained in this work indicate that the proposed control scheme with the MR suspension can considerably improve the stability of vehicles with high performance in vibration isolation.

Magnetorheological elastomer and its application on impact buffer

In this study, a new magnetorheological elastomer (MRE) based buffer is proposed and its vibration isolation performance is investigated. The MRE buffer with a compact structure is first designed in order to accomplish the maximization of the variable stiffness range. The working characteristics of the MRE buffer are then measured and the model of MRE is established. On the basis of the experimental data, the control model of the MRE buffer is also formulated. A two-degree-of-freedom dynamic model with an MRE buffer is then developed. An intelligent control strategy, human simulated intelligent control (HSIC), is proposed to reduce the impact during the drop crash. Finally, the proposed MRE buffer and controller are validated numerically and experimentally. The results show that the proposed MRE buffer and the control strategy can reduce the impact acceleration effectively.

Adaptive fuzzy neural network control for transient dynamics of magneto-rheological suspension with time-delay

Since Magneto-rheological (MR) suspension has nonlinearity and time-delay, the application of linear feedback strategy has been limited. This paper addresses the problem of control of MR suspension with time-delay when transient dynamics are presented. An adaptive Fuzzy-Neural Network Control (FNNC) scheme for the transient course is proposed using fuzzy logic control and artificial neural network methodologies. To attenuate the adverse effects of time-delay on control performance, a Time Delay Compensator (TDC) is established. Then, through a numerical example of a quarter car model and a real road test with a bump input, the comparison is made between passive suspension and semi-active suspension. The results show that the MR vehicle with FNNC strategy can depress the peak acceleration and shorten the setting time, and the effect of time-delay can be attenuated. The results of road test with the similarity of numerical study verify the feasibility of the control strategy.

Response time of MR suspension system and control compensation

Semi-active suspension system utilizing magneto-rheological (MR) fluid is being accepted by the automotive industry due to their controllable force characteristics. However the time delay of MR suspension system is long enough to affect the control performance. To address this problem, an experimental apparatus was founded to test the systempsilas response time. And a human-simulation intelligent control (HSIC) strategy was designed to compensation it. Simulation and road test verified effects of HSIC in solving the problem of time-delay of MR suspension.

Rapid control prototyping development of intelligent control system of vehicle semi-active suspension

This study discusses the rapid control prototyping approach used for development and investigation of control algorithm in magneto-rheological (MR) semi-active suspension system. Firstly, a multibody dynamic model based on MR suspension system is developed with the aid of VEDYNA software. Based on the model, a new intelligent control algorithm, human simulated intelligent control (HSIC) is proposed and designed. Finally, by means of dSPACE groupware system, the semi-active control prototype of real car has been built up, road test of real car and parameters of online adjustment have also been carried out. The results of offline simulation and real time control show that HSIC can achieve better comfort under assuring stability. They also show that rapid control prototyping (RCP) on the basis of Matlab/Simulink and dSPACE can shorten the research period of vehicle semi-active controller and reduce the cost of research. It is very significant to promote the industrialization of vehicle semi-active controller.

A Novel Bench of Quarter Vehicle Semi-active Suspension

According to the requirements of automotive performance evaluation standards, a typical quarter vehicle suspension model was established, and the correlation between the suspension model transfer function and the vehicle performance evaluation criteria was analyzed. Because the current lack of a standard bench of car suspension, this paper has established a universal and efficient standard bench of quarter car semi-active suspension. The bench is composed of the hardware system part, the sensing scheme and software system part. The bench can not only be used as a passive suspension development platform, but also can be used as a semi-active suspension control test platform. With the technology of Rapid Control Prototype (RCP), a variety of control schemes for verifying the semi-active suspension can be compared to find a suitable control algorithm, which is very significant for improving ride comfort and safety of vehicle suspension. In addition, the bench is equipped with a force sensor on the piston rod of the damper to monitor the real-time output force value of the damper, and to achieve real-time tracking and correction of the control output in the semi-active suspension control test.

Attitude control for rapid robot with Human simulated intelligent control theory

It is very important to hold the wheeled robotpsilas attitude while running. The adaptive suspension system with magneto-rheological (MR) damper can optimize its attitude by providing continuously variable real-time damping control based on road profile. To address this problem, a control strategy with schema-based human simulated intelligent control (HSIC) theory is proposed and studied. According to a seven DOFs dynamic model, eight states of robotpsilas attitude are divided and relevant schemas are designed. The performances of the control system under two types of road excitations are evaluated by computer simulation. The results indicate that HSIC can achieve better attitude stability than LQG and passive system.

Adaptive fuzzy logical control for impact absorbing

A new adaptive fuzzy logical control (FLC) strategy using a hybrid Taguchi genetic algorithm (HTGA) is proposed to absorb the impact of car body caused by road bump. The controller consists of two control loops. The inner open loop controls a nonlinear magnet-orheological (MR) damper to achieve tracking of a desired force. The outer loop implements a fuzzy logic controller using HTGA. The HTGA is used to tune the membership functions and control rules of FLC with initial skyhook control rules. To verify the control performance, simulation and road test of the adaptive FLC are carried out. The simulation and experiment results show that adaptive FLC can achieve smaller acceleration peak and shorter adjusting time than sky-hook and passive system under bump input.