Active suspension is considered to be a good way to improve the ride comfort of high-speed trains. According to the output index requirements of train's active suspension, a giant magnetostrictive actuator(GMA) is...
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Active suspension is considered to be a good way to improve the ride comfort of high-speed trains. According to the output index requirements of train's active suspension, a giant magnetostrictive actuator(GMA) is proposed. This is mainly because giant magnetostrictive materials(GMM) has the characteristics of fast response, large output force and high energy conversion rate. It is verified by experiments that the output force is proportional to the excitation current. It is found in the experiment that the excitation frequency should be greater than 120Hz to obtain a stable output force, and it is also found that preload and excitation frequency will affect response time. On the basis of experiments, a 2-DOF physical and mathematical model of the vertical quarter train is built. An mpc algorithm is designed to control GMA active suspension. Through simulation analysis, the proposed control algorithm is compared with passive suspension and active suspension based on PID control algorithm. Both theory and practice show that the proposed control algorithm is effective.
In emergency situations, it is difficult to meet the requirements of safe driving only by relying on the braking system, and the probability of accidents can be reduced by employing an emergency lane-changing mode. To...
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In emergency situations, it is difficult to meet the requirements of safe driving only by relying on the braking system, and the probability of accidents can be reduced by employing an emergency lane-changing mode. To improve the adaptability of the distributed electric vehicle adaptive cruise control (ACC) strategy to complicated and volatile conditions, a multimode ACC strategy with emergency lane-changing function is proposed. Firstly, the ACC is divided into four modes aimed at the problem of complex conditions, and a switching strategy is designed to control the switching of them. Simultaneously, the car-following mode is divided in greater detail based on time to collision (TTC), and the acceleration weighted average algorithm is adopted for accuracy and output continuity during switching. Then, the ACC is established with a hierarchical control framework, in which a PID-based cruise mode and a multi-objective optimized car-following mode based on model predictive control (mpc) are devised. The target brake wheel cylinder pressure is selected as the emergency brake pressure in takeover mode. In addition to the mpc-based system, the emergency lane-changing mode incorporates a yaw moment controller in the upper-level controller to improve body stability during emergency lane changing in the upper-level controller. In the lower-level controller, the upper-level output is converted into driving torque, wheel cylinder pressure, and front wheel angle to control vehicle travel and generate additional yaw moment. Finally, the results indicate that the presented multimode switching strategy can adapt to complex and instable transportation environments. In the cruise control scenario, the host vehicle can rapidly reach cruising speed within 5 s. In the car-following scenario, the host vehicle can stably follow the preceding vehicle with an acceleration of -5-3.5 m/s2 and a jerk of -2-2 m/s3 throughout the entire process, maintaining a safe distance from the preceding ve
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