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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.

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.

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

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.