A nonlinear virtual rider for motorcycles

This work presents a virtual rider for the guidance of a nonlinear motorcycle model. The target motion is defined in terms of roll angle and speed. The virtual rider inputs are the steering torque, the rear-wheel driving/braking torque and front-wheel braking torque. The virtual rider capability is assessed by guiding the nonlinear motorcycle model in demanding manoeuvres with roll angles of 50° and longitudinal accelerations up to 0.8 g. Considerations on the effective preview distance used by the virtual rider are included.     

基于非线性模型预测控制的车辆纵向队列协调控制

本文中针对基于分层控制结构的车辆队列上、下层控制缺少联系的问题,提出了车辆队列跟驰与个体车辆动力学稳定性协调控制的思路,其基本思想是在保证队列中个体车辆安全稳定行驶的同时,尽可能实现队列跟驰控制的目标。基于非线性模型预测控制(nonlinear model predictive control, NMPC)方法设计了车辆队列协调控制方案,设计了包括跟驰间距误差、跟驰速度误差以及车速与车轮圆周速度差3个子目标的优化目标函数,将队列跟驰与车辆动力学稳定性的协调控制转化为约束优化控制问题;基于序列二次规划(sequential quadratic programming, SQP)方法进行求解,得到车辆前、后轴的制动/驱动力矩来实现上层决策输出的期望跟驰加速度。基于由3车辆组成的非线性队列模型对控制方案进行了仿真分析,结果表明,所提出的基于NMPC的车辆队列协调控制策略可以在大范围操纵工况下,在保证车辆安全稳定行驶的基础上实现队列的跟驰控制。     

基于模型预测理论的车辆纵向避撞控制研究

为了实现电动车辆纵向避撞控制,提高车辆的安全性和人性化需求,文章针对分布式电动汽车,建立了平面运动的简化动力学模型;提出了基于 MPC 的纵向避撞控制策略,其中基于传感器获取的障碍物运动信息,采用滚动优化方法设计顶层控制器,以求解车辆期望纵向加速度。底 层分配控制器则基于车辆期望加速度,对车轮转矩进行最优分配,实现基于模型预测控制的车辆纵向避撞控制。最后通过仿真测试控制器的有效性。     

极限工况下主动前轮转向汽车稳定性控制

轮胎对汽车稳定性有重要影响,研究和利用轮胎的非线性特性有助于扩展汽车的稳定域。本文基于非线性轮胎模型,提出一种改进型线性时变模型预测控制(LTV-MPC)方法。该方法能扩展主动前轮转向汽车的稳定范围,提高极限工况下主动前轮转向汽车的稳定性。仿真结果表明,该方法比传统的LTV-MPC方法具有更好的稳定性控制效果。     

A study on model fidelity for model predictive control-based obstacle avoidance in high-speed autonomous ground vehicles

This paper investigates the level of model fidelity needed in order for a model predictive control (MPC)-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles even when the vehicle is close to its dynamic limits. The context of this work is large autonomous ground vehicles that manoeuvre at high speed within unknown, unstructured, flat environments and have significant vehicle dynamics-related constraints. Five different representations of vehicle dynamics models are considered: four variations of the two degrees-of-freedom (DoF) representation as lower fidelity models and a fourteen DoF representation with combined-slip Magic Formula tyre model as a higher fidelity model. It is concluded that the two DoF representation that accounts for tyre nonlinearities and longitudinal load transfer is necessary for the MPC-based obstacle avoidance algorithm in order to operate the vehicle at its limits within an environment that includes large obstacles. For less challenging environments, however, the two DoF representation with linear tyre model and constant axle loads is sufficient.     

A comparative study of equivalent modelling for multi-axle vehicle

In this paper, equivalent modelling methods of a multi-axle vehicle are presented and compared. Firstly, for the sake of comparison, a single-track model of a three-axle and a two-axle vehicle is developed, and then existing equivalent modelling derivations are presented and discussed. Next, the proposed model-based dynamic equivalence of force/moment at the centre of gravity (CG) is introduced and optimised. It represents the approximately equivalent steady-state and transient response of the yaw rate and side slip angle, which allows different cornering stiffness on the central and rear axle. Finally, to demonstrate how the proposed method is advantageous to the other equivalent models available in the literature, different simulation cases are compared in the dimension of time-domain, eigenvalues characteristics and frequency-domain. Furthermore, the proposed method is extended to any multi-axle vehicle configurations and a general expression is formulated.     

基于 MPC 的局部路径规划与路径跟踪控制研究

文章主要针对无人驾驶车辆在进行路径跟踪遇到障碍物时,需要局部重新规划出一条可行路径的问题,首先基于车辆点质量模型的 MPC 局部路径规划算法,得到满足车辆动力学约束并实 现避障功能的局部路径,然后在二自由度车辆动力学模型的基础上基于 MPC 进行路径的跟踪,最后使用 Simulink/Carsim 进行联合仿真验证,结果表明基于该局部路径规划与路径跟踪算法能够可 靠地规划出避开障碍物的局部路径,实现高速下的路径跟踪。     

Model reduction in vehicle dynamics using importance analysis

Previous work by the authors developed a novel model reduction method, namely importance analysis, that offered a unique set of properties: concurrent dynamic and kinematic reduction, applicability to nonlinear systems, preservation of realisation, and trajectory dependence. This paper investigates the utility of importance analysis as a model reduction tool within the context of vehicle dynamics. To this end, a high-fidelity model of a High Mobility Multipurpose Wheeled Vehicle (HMMWV) is considered, and this model is reduced for three different scenarios. Reduction is achieved in both dynamics and kinematics while preserving the original definition and interpretation of state variables and parameters. Furthermore, the resulting reduced models are very different in terms of complexity, containing only what is necessary for their respective scenarios, and providing important insight and computational savings. The conclusion is that importance analysis can be an invaluable reduction tool in vehicle dynamics, offering the aforementioned unique set of properties.     

European advanced driver training programs: Reasons for optimism

Post license advanced driver training programs in the US and early programs in Europe have often failed to accomplish their stated objectives because, it is suspected, that drivers gain self perceived driving skills that exceed their true skills—leading to increased post training crashes. The consensus from the evaluation of countless advanced driver training programs is that these programs are a detriment to safety, especially for novice, young, male drivers.Some European countries including Sweden, Finland, Austria, Luxembourg, and Norway, have continued to refine these programs, with an entirely new training philosophy emerging around 1990. These ‘post-renewal’ programs have shown considerable promise, despite various data quality and availability concerns. These programs share in common a focus on teaching drivers about self assessment and anticipation of risk, as opposed to teaching drivers how to master driving at the limits of tire adhesion. The programs focus on factors such as self actualization and driving discipline, rather than low level mastery of skills. Drivers are meant to depart these renewed programs with a more realistic assessment of their driving abilities. These renewed programs require considerable specialized and costly infrastructure including dedicated driver training facilities with driving modules engineered specifically for advanced driver training and highly structured curriculums. They are conspicuously missing from both the US road safety toolbox and academic literature. Given the considerable road safety concerns associated with US novice male drivers in particular, these programs warrant further attention.This paper reviews the predominant features and empirical evidence surrounding post licensing advanced driver training programs focused on novice drivers. A clear articulation of differences between the renewed and current US advanced driver training programs is provided. While the individual quantitative evaluations range from marginally to significantly effective in reducing novice driver crash risk, they have been criticized for evaluation deficiencies ranging from small sample sizes to confounding variables to lack of exposure metrics. Collectively, however, the programs sited in the paper suggest at least a marginally positive effect that needs to be validated with further studies. If additional well controlled studies can validate these programs, a pilot program in the US should be considered.     

Two degrees of freedom PID multi-controllers to design a mathematical driver model: experimental validation and robustness tests

This paper proposes a mathematical driver model based on PID multi-controllers having two degrees of freedom. Each PID controller making up this model is synthesised by the Ziegler–Nichols oscillation method, using the linear time invariant models which are obtained around their nominal operating points. Different PID controllers are combined using nonlinear optimisation and the H _∞ constraint. To demonstrate its robustness, it was tested on two models: a linear parameter variant model and a nonlinear four-wheel model. It was also tested in situations of high dynamic demand. The driver model showed good performance, stability and trajectory tracking. The performance tests were carried out using experimental data acquired by a Laboratory Peugeot 307 developed by INRETS-MA. This driver model was developed for an application known as ‘Itinerary Rupture DIagnosis’ (DIARI), which aims to evaluate the physical limits of a vehicle negotiating a bend. DIARI requires a tool to determine the steering commands to be applied to a vehicle model, making extrapolations with respect to speed.     

Optimisation of lateral car dynamics taking into account parameter uncertainties

Simulation studies on an active all-wheel-steering car show that disturbance of vehicle parameters have high influence on lateral car dynamics. This motivates the need of robust design against such parameter uncertainties. A specific parametrisation is established combining deterministic, velocity-dependent steering control parameters with partly uncertain, velocity-independent vehicle parameters for simultaneous use in a numerical optimisation process. Model-based objectives are formulated and summarised in a multi-objective optimisation problem where especially the lateral steady-state behaviour is improved by an adaption strategy based on measurable uncertainties. The normally distributed uncertainties are generated by optimal Latin hypercube sampling and a response surface based strategy helps to cut down time consuming model evaluations which offers the possibility to use a genetic optimisation algorithm. Optimisation results are discussed in different criterion spaces and the achieved improvements confirm the validity of the proposed procedure.     

Improving the robustness of an MPC-based obstacle avoidance algorithm to parametric uncertainty using worst-case scenarios

Previous work by the authors focused on obstacle avoidance in large, high-speed autonomous ground vehicles within unknown and unstructured environments. This work resulted in a nonlinear model predictive control based algorithm that simultaneously optimises both the speed and steering commands. The algorithm can exploit the dynamic limits of the vehicle to navigate it to a target position as quickly as possible without compromising safety. In the algorithm, a model of the vehicle is used explicitly to predict and optimise future actions, but in practice the model parameter values are not known exactly. Thus, in this paper, the robustness of the algorithm to parametric uncertainty is evaluated. It is first demonstrated that using nominal parameter values in the algorithm leads to safety issues in 24% of the evaluated scenarios with the considered parametric uncertainty distributions. To improve the algorithm's robustness, a novel double-worst-case formulation is developed that simultaneously accounts for the robust satisfaction of the two safety requirements of high-speed obstacle avoidance: collision-free and no-wheel-lift-off. Results from simulations with stratified random scenarios and worst-case scenarios show that the double-worst-case formulation renders the algorithm robust to all uncertainty realisations tested. The trade-off between robustness and the task completion performance is also quantified.     

Validation of vehicle dynamics simulation models – a review

In this work, a literature survey on the validation of vehicle dynamics simulation models is presented. Estimating the dynamic responses of existing or proposed vehicles has a wide array of applications in the development of vehicle technologies, e.g. active suspensions, controller design, driver assistance systems, etc. Although simulation environments, measurement tools and mathematical theories on vehicle dynamics are well established, the methodical link between the experimental test data and validity analysis of the simulation model is still lacking. This report presents different views on the definition of validation, and its usage in vehicle dynamics simulation models.