Active collision avoidance system has received more and more attraction, which has the capability to avoid potential accidents and reduce driver burden. This paper proposes an active collision avoidance system which consists of a path planner and a coordinated lateral controller. In the path planner, cubic B-spline is developed to obtain collision-free trajectories to bypass the obstacle by steering. Based on this, a coordinated lateral dynamic control of autonomous ground vehicles is presented to improve the accuracy and robustness of path following and simultaneously ensure vehicle stability via active front steering and direct yaw moment control. Then, second-order sliding mode control, based on super-twisting algorithm, is applied to reduce lateral offset and heading angle deviation as much as possible and avoid chattering phenomenon of tradition sliding mode control. Meanwhile, a new form of sliding mode control based on improved reaching law is devoted to forcing the vehicle state sideslip angle and yaw rate to stability envelope with less chattering in the case of low road friction coefficient. Eventually, the effectiveness and robustness of active collision avoidance system against external disturbance and parametric uncertainties are confirmed through different test cases in the MATLAB/Simulink simulation platform.
Wind energy is widely used as clean, renewable and mature new energy. However, the inhomogeneity and non-steady state of the operating environment of the wind turbine lead to the randomness of the load, which will cause the fluctuation of the voltage and frequency of the power grid, and affect the power quality of the power grid; the wind turbine will also have various faults, which will cause the unit to stop and reduce the utilization rate of the unit. Artificial intelligence technology can diagnose and predict wind power for wind turbines, so that new energy can be better complemented with traditional hydro thermal power. Finally, an example of fault diagnosis of daily monitoring data of doubly fed induction generator in Dabancheng, Xinjiang is given to demonstrate the application of artificial intelligence technology in wind power generation.
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