The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the ...The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle.展开更多
In order to track the desired path as fast as possible,a novel autonomous vehicle path tracking based on model predictive control(MPC)and PID speed control was proposed for high-speed automated vehicles considering th...In order to track the desired path as fast as possible,a novel autonomous vehicle path tracking based on model predictive control(MPC)and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits,in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit.Moreover,this scheme was further extended along one moving prediction window.In the MPC controller,the prediction model was an 8-degree-of-freedom(DOF)vehicle model,while the plant was a 14-DOF vehicle model.For lateral control,a sequence of optimal wheel steering angles was generated from the MPC controller;for longitudinal control,the total wheel torque was generated from the PID speed controller embedded in the MPC framework.The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory.The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller.Additionally,the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.展开更多
Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model ...Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model Predictive Control(MPC)technique in the presence of various uncertainties and external disturbances.The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays,parametric uncertainties,and disturbances.The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed,namely the hover,transition,and cruise mode.The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component.Moreover,different uncertainties such as parametric,time delays in state and input,are introduced in translational and rotational components.From the previous work,the Linear Quadratic Tracker with Integrator(LQTI)is used for comparison to corroborate the performance of the designed controller.Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy.Finally,discussions are provided to demonstrate the effectiveness of the given methodology.展开更多
In this paper,a predictive sliding mode control method based on multi-sensor fusion is proposed to solve the problem of insufficient accuracy in trajectory tracking caused by actuator delay.The controller,based on the...In this paper,a predictive sliding mode control method based on multi-sensor fusion is proposed to solve the problem of insufficient accuracy in trajectory tracking caused by actuator delay.The controller,based on the kinematics model,uses an inner and outer two-layer structure to achieve decoupling of position control and heading control.A reference positional change rate is introduced into the design of controller,making the automatic guided vehicle(AGV)capable of real-time predictive control ability.A stability analysis and a proof of predictive sliding mode control theory are provided.The experimental results show that the new control algorithm can improve the performance of the AGV controller by referring to the positional change rate,thereby improving the AGV operation without derailing.展开更多
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,mod...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.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51575103,11672127,U1664258)Fundamental Research Funds for the Central Universities of China(Grant No.NT2018002)+1 种基金China Postdoctoral Science Foundation(Grant Nos.2017T100365,2016M601799)the Fundation of Graduate Innovation Center in NUAA(Grant No.k j20180207)
文摘The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle.
基金Project(20180608005600843855-19)supported by the International Graduate Exchange Program of Beijing Institute of Technology,China。
文摘In order to track the desired path as fast as possible,a novel autonomous vehicle path tracking based on model predictive control(MPC)and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits,in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit.Moreover,this scheme was further extended along one moving prediction window.In the MPC controller,the prediction model was an 8-degree-of-freedom(DOF)vehicle model,while the plant was a 14-DOF vehicle model.For lateral control,a sequence of optimal wheel steering angles was generated from the MPC controller;for longitudinal control,the total wheel torque was generated from the PID speed controller embedded in the MPC framework.The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory.The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller.Additionally,the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.
基金co-supported by the National Natural Science Foundation of China(Nos.61225015,61105092,61422102,and 61703040)the Beijing Natural Science Foundation,China(No.4161001)the China Postdoctoral Science Foundation(No.2017M620640)。
文摘Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model Predictive Control(MPC)technique in the presence of various uncertainties and external disturbances.The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays,parametric uncertainties,and disturbances.The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed,namely the hover,transition,and cruise mode.The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component.Moreover,different uncertainties such as parametric,time delays in state and input,are introduced in translational and rotational components.From the previous work,the Linear Quadratic Tracker with Integrator(LQTI)is used for comparison to corroborate the performance of the designed controller.Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy.Finally,discussions are provided to demonstrate the effectiveness of the given methodology.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.61903241,61304223,61603191,61873158,61573237)the China Postdoctoral Science Foundation(Grant No.2018M630424)the Natural Science Foundation of Shanghai Municipality(Grant No.18ZR1415100).
文摘In this paper,a predictive sliding mode control method based on multi-sensor fusion is proposed to solve the problem of insufficient accuracy in trajectory tracking caused by actuator delay.The controller,based on the kinematics model,uses an inner and outer two-layer structure to achieve decoupling of position control and heading control.A reference positional change rate is introduced into the design of controller,making the automatic guided vehicle(AGV)capable of real-time predictive control ability.A stability analysis and a proof of predictive sliding mode control theory are provided.The experimental results show that the new control algorithm can improve the performance of the AGV controller by referring to the positional change rate,thereby improving the AGV operation without derailing.
基金This paper is funded by International Graduate Exchange Program of Beijing Institute of Technology。
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.
