Multi-constrained model predictive control for autonomous ground vehicle trajectory tracking

A multi-constrained model predictive control (MPC) algorithm for trajectory tracking of an autonomous ground vehicle is proposed and tested in this paper. First, to simplify the computation, an active steering linear error model is applied in the MPC controller. Then, a control increment constraint and a relaxing factor are taken into account in the objective function to ensure the smoothness of the trajectory, using a softening constraints technique. In addition, the controller can obtain optimal control sequences which satisfy both the actual kinematic constraints and the actuator constraints. The circular trajectory tracking performance of the proposed method is compared with that of another MPC controller. To verify the trajectory tracking capabilities of the designed controller at different desired speed, the simulation experiments are carried out at the speed of 3m/s, 5m/s and 10m/s. The results demonstrate the MPC controller has a good speed adaptability.     

基于线性矩阵不等式的智能车轨迹跟踪控制

针对传统的基于精确数学模型的智能车轨迹跟踪控制器跟踪精度低,鲁棒性弱,很难适应复杂多变的驾驶环境等问题,结合线性矩阵不等式(LMI)鲁棒控制具有易于求解、抗干扰能力强等优点,提出基于LMI的智能车轨迹跟踪控制方法. 将车辆侧向动力学状态空间模型进行坐标变换,得到基于跟踪误差的车辆侧向动力学状态空间模型,采用饱和线性轮胎得到车辆侧向动力学多胞型模型;设计LMI反馈控制器,在控制器中引入前馈控制量,以消除侧向位置稳态误差. Carsim和Matlab/Simulink的联合仿真表明,该控制器在保证车辆稳定性的基础上具有较高的跟踪精度,对车速和路面附着系数具有较强的鲁棒性. 与模型预测控制器(MPC)和预瞄驾驶员模型(PDM)控制器进行对比,结果表明,设计的该控制器轨迹跟踪精度更优.     

基于串级MPC和EPS的集成驾驶员转向车道线保持

针对电动助力转向( EPS)作为转向执行机构的车道线保持的控制系统设计及保留驾驶员对车辆操控问题,提出基于串级模型预测控制( MPC)和EPS集成驾驶员转向的车道线保持系统. 在车道线识别视觉系统空间,建立车道线保持状态空间模型,设计基于MPC的车道线保持控制器( LMPC) . 建立EPS状态空间模型,设计基于MPC的EPS车辆前轮转角控制器( EMPC) . LMPC与EMPC经逆转向机构模型组成串级控制结构. 分析驾驶员转向对车道线保持控制的影响,进而通过保留驾驶员对车辆控制来提高处理紧急事件的能力. 仿真结果表明:在不同车速和不同曲率道路下,该控制策略均能快速消除横向位置偏差和航向角偏差,保证车辆沿着车道线行驶,具有较好的适应性和鲁棒性. 驾驶员转向可以改善车道线保持和提高车辆主动安全性.     

基于模型预测控制的汽车底盘集成控制

为了实现汽车底盘的集成控制,提出了模型预测控制(MPC)方法。在紧急避障操纵时,利用四轮差动制动和主动前轮转向集成控制的方式实现对汽车理想操纵特性的跟随。上层集成控制器通过模型预测控制方法决策下层子控制器所需的附加横摆力矩和前轮附加转角。建立了七自由度非线性车辆模型作为预测模型,并通过实车试验对其精度进行了验证。在Carsim中的虚拟试验对模型预测控制方法的控制效果进行了验证。     

汽车高速紧急避障路径跟踪与主动防侧翻控制

为提高匹配机械弹性车轮汽车在高速紧急避障时的效率与安全性,在Simulink中建立了整车非线性八自由度模型,并基于车轮样机台架试验数据,利用Matlab遗传算法工具箱对机械弹性车轮模型参数进行分级辨识.综合考虑行驶车速、轨迹跟踪误差、方向盘转角以及侧翻评价指标,建立了八自由度驾驶员—汽车预瞄跟随闭环系统模型.分析了汽车在不同行驶车速时所需的方向盘角输入信息与侧翻状态响应,总结出汽车高速转向时的侧翻动态特性.为高速安全通过规划的避障路径,在转向控制驾驶员模型基础上建立了速度控制驾驶员模型,当侧翻评价指标超过安全阈值时利用制动踏板降低车速,当纵向车速小于期望安全车速时利用加速踏板提高车速.仿真分析表明建立的高速避障路径跟踪与控制策略能高效完成避障路径跟踪,同时能有效降低紧急避障时的侧翻风险.     

Path planning and stability control of collision avoidance system based on active front steering

Vehicle collision avoidance system is a kind of auxiliary driving system based on vehicle active safety,which can assist the driver to take the initiative to avoid obstacles under certain conditions,so as to effectively improve the driving safety of vehicle.This paper presents a collision avoidance system for an autonomous vehicle based on an active front steering,which mainly consists of a path planner and a robust tracking controller.A path planner is designed based on polynomial parameterization optimized by simulated annealing algorithm,which plans an evasive trajectory to bypass the obstacle and avoid crashes.The dynamic models of the AFS system,vehicle as well as the driver model are established,and based on these,a robust tracking controller is proposed,which controls the system to resist external disturbances and work in accordance with the planning trajectory.The proposed collision avoidance system is testified through CarSim and Simulink combined simulation platform.The simulation results show that it can effectively track the planning trajectory,and improve the steering stability and anti-interference performance of the vehicle.     

AGV建模和自适应模糊滑模跟踪控制研究

介绍了自动导引小车AGV的跟踪控制技术的研究现状;建立了AGV跟踪误差系统运动学和动力学的混合模型;针对AGV的初始位存在误差或给定轨迹不连续时,传统PID轨迹跟踪控制器会使其初始速度产生一个较大跳变的问题,将滑模变结构控制技术与自适应模糊控制技术相结合,提出了一种基于模糊滑模的AGV自适应模糊路径跟随和轨迹跟踪控制器。仿真和实验结果表明,给出的自适应模糊滑模控制器不仅有较好的轨迹跟踪控制效果,而且具有较强的鲁棒性。     

基于Radau伪谱法和MPC的智能电动车辆生态驾驶

为优化智能电动车能源消耗,提出了基于Radau伪谱法和模型预测控制算法的智能电动车辆生态驾驶的方案。建立生态驾驶控制模型和能耗模型,结合边界约束和路径约束,构建生态驾驶能耗优化的最优控制问题。通过能耗优化得到最优车速轨迹,将此轨迹作为期望输入,基于模型预测控制( MPC)算法完成生态驾驶控制。实验结果表明：以纯电动车和实际规划路径为例,以最优车速自动行驶的能源消耗少于人工驾驶的能源消耗,验证了文中策略的有效性。     

基于RBF和模糊预测的AGV轨迹跟踪控制

针对AGV系统存在初始位姿误差,而且给定轨迹又不连续时,应用传统的轨迹跟踪控制方法,就会使其初始速度产生较大跳变的问题,基于径向基函数(RBF)神经网络的非线性动态系统在线建模,将模糊控制技术与预测控制技术相结合,提出基于反演(Backstepping)方法的速度控制器和基于RBF的模糊预测转矩控制器,实施AGV路径跟随和轨迹跟踪控制.仿真和实验结果表明,设计的速度控制器和转矩控制器使AGV系统不仅有较好的动态性能,而且具有较强的鲁棒性.     

高速无人驾驶车辆轨迹跟踪和稳定性控制

针对高速无人驾驶车辆运动控制过程中轨迹跟踪精度和稳定性难以同时保障的问题,提出综合前馈-反馈及自抗扰控制(ADRC)补偿相结合的横向控制算法. 通过车速和道路曲率信息计算前馈稳态前轮转向角,将质心侧偏角引入航向偏差,以车辆航向角偏差和侧向偏差作为参考量进行反馈控制,通过前馈-反馈控制提升瞬态轨迹跟踪性能. 设计自抗扰控制器,通过扩张状态观测器对未建模动态和内外界干扰进行估计,通过将后轮侧偏角控制在参考值附近来补偿前轮转角,提升无人驾驶车辆的转向稳定性和控制器的鲁棒性. 不同工况下的仿真结果表明,利用该方法可以保证高速无人驾驶车辆稳定地跟踪期望路径行驶,轨迹跟踪偏差较小,对车辆参数变化和外界干扰具有较强的鲁棒性.     

基于径向基函数神经网络PID与模型预测控制的车辆轨迹跟踪控制

为提高无人驾驶车辆的稳定性和鲁棒性,提出一种基于径向基函数神经网络自适应比例积分微分（RBFNN?PID）算法和模型预测控制（MPC）算法相结合的车辆轨迹跟踪控制方法. 基于自适应RBFNN?PID算法、MPC算法以及车辆动力学模型,建立智能车辆纵向速度控制和横向控制的仿真模型并将其结合起来. 在此基础上,以横向MPC控制和LQR?PID控制算法为基准,验证所提出的控制方法在轨迹跟踪方面的优越性. 仿真结果表明,新方法比对照组具有更高的精度. 最后,对新控制方法的硬件在环验证表明,该轨迹跟踪控制算法在轨迹跟踪精度和稳定性方面具有一定的有效性和先进性.     

汽车转向的力输入控制与角输入控制及其对驾驶员-汽车闭环系统的影响

讨论了与两种不同转向控制策略相应的人－车闭环系统的运动特征，即通过给转向盘施加转向力矩或使转向盘转过一定角度来实施其转向的策略。仿真计算表明，当不足转向度小的汽车高速行驶时，驾驶员采用给转向盘施加转向力矩的控制策略具有更小的跟随误差，并且校正参数的变化范围也小得多，这说明采用此策略对车况的改变有更好的适应性。     

Path tracking for vehicle parallel parking based on ADRC controller

A novel path tracking controller for parallel parking based on active disturbance rejection control (ADRC) was presented in this paper. A second order ADRC controller was used to solve the path tracking robustness, which can estimate and compensate model uncertainty caused by steering kinematics and disturbances caused by parking speed and steering system delay. Collision-free path planning technology was adopted to generate the reference path. The simulation results validate that the performance of the proposed path tracking controller is better than the conventional PID controller. The actual vehicle tests show that the proposed path tracking controller is effective and robust to model uncertainty and disturbances.     

基于反演法的智能车辆弯路换道轨迹跟踪控制

基于车辆的运动学模型,研究了具有非完整约束特性的智能车辆弯路换道的轨迹跟踪控制问题。结合弯曲车道的曲率半径,针对换道过程中车辆横向运动和纵向运动对换道轨迹的耦合作用,提出了一种适用于弯道的换道轨迹规划方法。根据换道轨迹,利用积分反演法,引入中间虚拟控制量,设计了轨迹跟踪控制算法,并用Lyapunov稳定性理论证明了控制系统的稳定性。仿真结果表明:该控制器可以保证车辆在弯路换道的轨迹跟踪误差一致有界收敛,且具有快速、准确跟踪和全局稳定的特性。     

智能车辆弧线跟踪控制算法

建立了车辆预瞄运动学模型,通过最优控制选取切换超平面,采用滑模变结构控制实现了智能车辆弧线跟踪中弧线曲率小变化时的导航控制。根据模糊控制快速跟踪弧线曲率大变化时的导航路径,结合车辆转向系统当前状态和最快动态响应能力建立了智能车辆弧线跟踪时的变结构控制输出集。仿真分析和实验验证了该融合控制算法在智能车辆弧线跟踪过程中的平稳性和可靠性。     

Vehicle Active Damping Control with Considering Time Delay

A model predictive control (MPC) based active damping controller for automotive driveline oscillations with time-delay consideration is proposed.A simplified driveline model considering time delay is modeled and converted to a linear parameter varying state space equation.Based on the model and model predictive control theory,an active damping controller is designed for drivability and comfortability improvement.In order to verify the designed controller,a driveline with engine is modeled to simulate the tip-in/out driving operation.An MPC active damping controller without considering time delay is simulated together with the proposed controller.The simulation results show that,by adopting the new MPC active damping controller,the vibration of the vehicle is reduced and the drivability and comfortability are improved.     

汽车转向操纵的单神经元自适应控制

基于汽车操纵稳定性总方差评价方法和神经网络控制理论,建立了汽车转向单神经元自适应PID控制算法。利用操纵稳定性评价中的轨迹跟随误差和方向盘忙碌程度的评价指标,建立了神经元学习的二次型性能指标函数,并采用梯度下降算法实现了单神经元控制器的连接权值的调整。最终采用汽车7自由度非线性动力学模型进行了不同行驶速度下的仿真计算。结果表明:该控制算法可较为精确地控制汽车对预期行驶轨迹的转向运动,并体现出良好的鲁棒性和自适应性。     

汽车稳定性控制系统硬件在环仿真

首先建立了八自由度的汽车动力学模型,设计了模糊PI控制器和汽车稳定性控制策略。建立了基于Matlab/Simulink平台下的硬件在环试验平台,针对转向制动和双移线转向行驶两种工况进行了硬件在环仿真试验。结果表明:所设计的模糊PI控制器及控制策略可以通过制动力控制有效地抑制汽车的过多转向或不足转向趋势,提高汽车的操纵稳定性。     

最优跟踪车载式自行火炮多桥动态转向系统

建立了包含车身侧倾特性的车载式火炮多桥转向三自由度模型动力学方程普适公式,并构造了用于重构车辆状态的降维观测器,设计了基于最优跟踪控制策略的车辆转向控制器。分析结果表明,能够通过控制车辆后轮转向角来减小汽车瞬时出现的过多转向或不足转向,增强了汽车的动态转向稳定性。所构造的降维观测器也能使反映车辆操纵性能的车辆状态量估计值与实际值一致。     

Chassis integrated control for 4WIS distributed drive EVs with model predictive control based on the UKF observer

Four-wheel independent steering(4 WIS) system and direct yaw moment control(DYC) have an important influence on vehicle lateral stability. However, DYC has a great effect on the longitudinal velocity, and the capability of 4 WIS is limited to stability.To decrease the influence on the longitudinal velocity and improve the stability of electrical vehicles, a chassis controller integrated with a 4 WIS system and a DYC system with model predictive control(MPC) is designed. The framework consists of an unscented Kalman filter(UKF) observer and an MPC that contains three blocks: supervisor blocks, upper blocks and lower blocks. First, the sideslip angle, longitudinal velocity and lateral tire forces are estimated by the UKF observer; second, a bicycle model is utilized in the supervisor to calculate the desired values; third, the upper blocks are designed with the MPC to optimize the target steering angles and longitudinal tire forces under the constraints of subsystems; to facilitate the design of the MPC, a nonlinear tire is simplified based on the Taylor expansion method; finally, the target steering angles and longitudinal tire forces are achieved by the lower blocks. The integrated controller is simulated on the co-simulation platform of MATLAB-Carsim. The results show that the proposed integrated controller has less impact on longitudinal velocity and could effectively improve vehicle stability.