Because of the nonlinear hysteresis characteristics of the magneto-rheological damper, the damper’s inverse model has disadvantages of low fitting accuracy and poor practicality. Therefore, in this study, an optimized genetic algorithm has been proposed to optimize the back propagation neural network’s initial weights and threshold. Compared with other damper controllers, the proposed inverse model improves the control current’s prediction accuracy and tracks the desired damping force in real time. Moreover, the proposed inverse model and designed fuzzy controller are applied to the 1/4 vehicle suspension system simulation. The obtained results show that the optimized neural network model can be applied to a practical control. The root mean square value of body acceleration of semi-active suspension is lower than that of passive suspension under different road excitation. This method provides a foundation for the accurate modeling and semi-active control of the magneto-rheological damper.
There are several main factors affect damping characteristics of a magnetorheological damper, the influence laws of some ones are eager to change with varying working conditions. Therefore, it is challengable to establish the relationship between the two: influence factors and damping characteristics of the magnetorheological damper. This paper proposed a new deep learning-based general inverse model to predict the required current value of a magnetorheological damper under changing complex working conditions. Specifically, a fully-connected multilayer perceptron was chosen to train the general forward model. The complex and time-varying relationships between the main factors and damping characteristics were accurately characterized on use of the high nonlinear mapping ability of the multilayer perceptron. Compared to most of general forward models, the proposed model was more excellent with an accuracy of 99.73% based on ensuring generality. Besides, the network inversion method was used in the inverse solution of the multilayer perceptron general forward model, which simplified the process of inverse solution without requiring a mass of training data. Furthermore, a genetic algorithm was used to replace the gradient descent method which was commonly used in network inversion, with the benefit of solving the problem of selecting the initial value and step size, as well as improving the accuracy. Simultaneously, the experiments were conducted on the proposed general inverse model, the experimental results, being consistent with the simulation results, demonstrated that the proposed model can accurately predict the control current value and track the required damping force in changing complex working conditions.INDEX TERMS General forward model, general inverse model, genetic algorithm, multilayer perceptron, network inversion.
A three-dimensional polymer mode (de)multiplexer based on mode evolution is proposed. The proposed configuration is mainly composed of cascaded two tapered couplers where waveguides with different heights are inversely tapered to achieve mode conversions of the E 11 , E 21 , and E 31 modes. The dimensional parameters and characteristics are analyzed by the beam propagation method. This mode (de)multiplexer exhibits the coupling ratio greater than 0.97, excess loss lower than 0.15 dB and extinction ratio higher than 20 dB for TE and TM polarizations over the wavelength range 1530 ∼ 1625 n m (the C + L band). This design has weak wavelength dependence and polarization dependence, which is promising to be applied to broadband on-board mode multiplexing.
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