3篇
您的检索式：作者名="NEGRUT Dan"
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| 1 | 基于MPC的考虑时间最优速度的高速无人驾驶车辆路径跟踪和PID速度控制显示文摘为了尽可能快地跟踪期望路径,提出一种新的基于模型预测控制(MPC)和PID速度控制方法,考虑高速无人驾驶车辆的物理约束,通过前向后向积分策略生成轮胎路面附着极限内的时间最优速度曲线,并将该方法进一步扩展应用于单个滚动预测窗口中。在MPC算法框架的设计中,以8自由度车辆模型作为预测模型,以高置信度的14自由度车辆模型作为被控对象,对于横向控制,通过MPC控制器生成最优的前轮转角,对于纵向控制,通过嵌入模型预测控制优化求解中的PID控制器生成总的车辆驱动/制动力矩。以任意连续变化曲率的路径为参考轨迹,在MATLAB中实现所提出的控制器,仿真结果表明车辆的横向位置和纵向位置的跟踪误差较小,通过车轮转角、车轮驱动/制动力矩的联合控制,车辆的轨迹跟踪和速度跟踪性能良好。另外,将最优速度曲线生成方法进一步扩展应用于单个滚动预测窗口中,其所需路径跟踪的时间比在整个路径上应用该策略时需要的时间短。 | 陈舒平 熊光明 陈慧岩 NEGRUT Dan | 2020 | Journal of Central South University2020，27，12： | 17 |
| 2 | Implementation of MPC-Based Path Tracking for Autonomous Vehicles Considering Three Vehicle Dynamics Models with Different Fidelities显示文摘Model predictive control(MPC)algorithm is established based on a mathematical model of a plant to forecast the system behavior and optimize the current control move,thus producing the best future performance.Hence,models are core to every form of MPC.An MPC-based controller for path tracking is implemented using a lower-fidelity vehicle model to control a higher-fidelity vehicle model.The vehicle models include a bicycle model,an 8-DOF model,and a 14-DOF model,and the reference paths include a straight line and a circle.In the MPC-based controller,the model is linearized and discretized for state prediction;the tracking is conducted to obtain the heading angle and the lateral position of the vehicle center of mass in inertial coordinates.The output responses are discussed and compared between the developed vehicle dynamics models and the CarSim model with three different steering input signals.The simulation results exhibit good path-tracking performance of the proposed MPC-based controller for different complexity vehicle models,and the controller with high-fidelity model performs better than that with low-fidelity model during trajectory tracking. | Shuping Chen Huiyan Chen Dan Negrut | 2020 | Automotive Innovation2020，3，4： | 5 |
| 3 | Implementation of MPC-Based Trajectory Tracking Considering Different Fidelity Vehicle Models显示文摘In order to investigate how model fidelity in the formulation of model predictive control(MPC)algorithm affects the path tracking performance,a bicycle model and an 8 degrees of freedom(DOF)vehicle model,as well as a 14-DOF vehicle model were employed to implement the MPC-based path tracking controller considering the constraints of input limit and output admissibility by using a lower fidelity vehicle model to control a higher fidelity vehicle model.In the MPC controller,the nonlinear vehicle model was linearized and discretized for state prediction and vehicle heading angle,lateral position and longitudinal position were chosen as objectives in the cost function.The wheel step steering and sine wave steering responses between the developed vehicle models and the Carsim model were compared for validation before implementing the model predictive path tracking control.The simulation results of trajectory tracking considering an 8-shaped curved reference path were presented and compared when the prediction model and the plant were changed.The results show that the trajectory tracking errors are small and the tracking performances of the proposed controller considering different complexity vehicle models are good in the curved road environment.Additionally,the MPC-based controller formulated with a high-fidelity model performs better than that with a low-fidelity model in the trajectory tracking. | Shuping Chen Huiyan Chen Dan Negrut | 2020 | Journal of Beijing Institute of Technology2020，29，3： | 2 |
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