In this article, the nonlinear damping characteristics of magnetorheological damper are expressed with hyperbolic tangent model to simulate its mechanical experimental results. The fitted hyperbolic tangent model can represent hysteretic behavior for magnetorheological damper exactly. Based on the hyperbolic tangent model, a quarter-car model with magnetorheological damper is established, and a new hybrid fuzzy and fuzzy proportional-integral-derivative (HFFPID) controller integrated with hybrid fuzzy control and fuzzy proportional-integral-derivative control is developed to improve the semi-active suspension performance, which can overcome the absence of precise mathematical model. Furthermore, numerical simulations for fuzzy proportional-integral-derivative (PID), hybrid fuzzy proportional-integral-derivative (HFPID), and HFFPID controllers are investigated to demonstrate the effectiveness of the proposed approaches. The simulation results show that the body acceleration, suspension deflection, and tyre displacement can be reduced more effectively using HFFPID controller under sinusoidal road excitation. It can be further concluded that the suspension performance is improved more effectively by using HFFPID controller under random road excitation, especially in the peak points.
Articulated wheeled loader vehicles have frequent rollover accidents as they operate in the complex outdoor environments. This article proposes an active anti-rollover control method based on a set of single-frame control moment gyro stabilizer installed on the rear body of the vehicle. The rollover dynamic model is first established for articulated wheeled loader vehicle with gyro stabilizer. The proposed control strategy is then applied in simulation to verify the rollover control effect on the vehicle under steady-state circumferential conditions. Finally, a home-built articulated wheel loader vehicle with gyro stabilizer is used to further verify the proposed control strategy. The results show that the vehicle can quickly return to the stable driving state and effectively avoid the vehicle rollover when a suitable anti-roll control moment can be provided by the gyro stabilizer. As a result, the articulated wheeled loader vehicle is able to operate safely in a complex outdoor environment.
This paper presents an optimal NARX neural network identification model for a magnetorheological (MR) damper with the force-distortion behavior. An intensive experimental study is conducted for designing the NARX network architecture to enhance modeling accuracy and availability, and the activation function selection, weights, and biases of the selected network are optimized by differential evolution algorithm. Different experimental training and validation samples are used for network training. The prediction capability of the optimal NARX model is verified by new measured test data. The test and comparative results show that the optimal NARX network model can satisfactorily emulate the dynamic behavior of MR damper and effectively capture its distortion behavior occurred with the increased current. The developed inverse NARX network model can effectively estimate the required current and track desired damping force. Moreover, the effects of different noise disturbance on the NARX network model performance are analyzed, and the model error varies slightly with a small noise disturbance. The accuracy of the results supports the use of this modeling technique for identifying irregular non-linear models of MR damper and similar devices.
Visual semantic segmentation is a key technology to realize scene understanding for autonomous driving and its accuracy is affected by the light changes in images. This paper proposes a novel multi-exposure fusion approach to visual semantic enhancement of autonomous driving. Firstly, a multi-exposure image sequence is aligned to construct a stable image input. Secondly, high contrast regions of multi-exposure image sequences are evaluated by context aggregation network (CAN) to predict image weight map. Finally, the high-quality image is generated by weighted fusion of multi-exposure image sequences. The proposed approach is validated by using Cityscapes’ HDR dataset and real environment data. The experimental results show that the proposed method effectively restores lost features in the light changing images and enhances accuracy of subsequent semantic segmentation.
Lane detection algorithms play a key role in Advanced Driver Assistance Systems (ADAS), which are however unable to achieve accurate lane recognition in low-light environments. This paper presents a novel deep network structure, namely LLSS-Net (low-light images semantic segmentation), to achieve accurate lane detection in low-light environments. The method integrates a convolutional neural network for low-light image enhancement and a semantic segmentation network for lane detection. The image quality is firstly improved by a low-light image enhancement network and lane features are then extracted using semantic segmentation. Fast lane clustering is finally performed by using the KD tree models. Cityscapes and Tusimple datasets are utilized to demonstrate the robustness of the proposed method. The experimental results show that the proposed method has an excellent performance for lane detection in low-light roads.
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