Miao Yu

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.

Research on Vehicle Magneto-rheological Suspensions Vibration Control and Test

A vehicle with independent magneto-rheological (MR) suspensions is regarded as a fleetly moving robot. To decrease MR suspension model simplification errors and to enhance the effect of MR suspension complicated vibration control, a hierarchical intelligent control (HIC) system is proposed. There are four independent local adaptive fuzzy (AF) controllers in control level and one coordination controller in coordination level. To restrain vibration of vehicle sprung mass and un-sprung mass, at the control level, a semi-active AF controller is designed for each MR suspension system, which can online adjust quantization factors and scale factor of fuzzy controller by human-simulation intelligent parameter modifying algorithm. At the coordination level, a controller is designed to coordinate the four local independent AF controllers, by adjusting their output parameters according to vehicle running attitude and characteristics of MR suspensions in different conditions. To validate the real results of hierarchical control, a MR suspension control and test system is set up and implemented on a mini automotive vehicle, which is equipped with four controllable MR dampers. Road tests under various conditions indicate that the HIC system can effectively reduce vertical vibration, improve the ride comfort, handle stability of vehicle, and show adaptiveness and robustness.

Modeling of Magneto-rheological Fluid Damper Employing Recurrent Neural Networks

Due to inherent nonlinear behaviors of magneto-rheological (MR) fluid dampers, one of challenges for utilizing effectively these devices as actuators to control vibration of mechanical system is to develop accurate models. A recurrent neural networks, with 3 input neurons and 1 output neuron in input layer and out layer respectively and 7 recurrent neurons in the hidden layer, is used to simulate behaviors of automotive MR fluid damper to develop control algorithms for suspension systems. The recursive prediction error algorithms are applied to train the recurrent neural networks using test data from lab where the MR fluid dampers were tested by the MTS electro-hydraulic servo vibrator system. Training of recurrent neural networks has been done by means of recursive prediction error algorithms presented in this paper and data generated from test above-mentioned. In comparison with experimental results of MR fluid damper, the recurrent neural networks are reasonably accurate to depict performances of MR fluid damper over a wide range of operating conditions

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.

Neural Networks Based Image Recognition: A New Approach

In this paper, a new application algorithm for image recognition based on neural network has been pro-posed. The present algorithm including recognition algorithm and algorithm for training BP neural network can recognize continually changing large gray image. This algorithm has been applied to deflection measurement of bridge health monitoring, and achieved a great success.

A recurrent neural network modeling for automotive magnetorheological fluid shock absorber

Automotive Magnetorheological (MR) fluid shock absorbers have been previously characterized by a series of nonlinear differential equations, which have some difficulties in developing control systems. This paper presents a recurrent neural network with 3 input neurons, 1 output neuron and 5 recurrent neurons in the hidden layer to simulate behavior of MR fluid shock absorbers to develop control algorithms for suspension systems. A recursive prediction error algorithm has been applied to train the recurrent neural network using test data from lab where the MR fluid shock absorbers were tested by the MTS electro-hydraulic servo vibrator system. Training of neural network model has been done by means of the recursive prediction error algorithm presented in this paper and data generated from test in laboratory. In comparison with experimental results of MR fluid shock absorbers, the neural network models are reasonably accurate to depict performances of MR fluid shock absorber over a wide range of operating conditions.