A fast implementation of MPC-based motion cueing algorithms for mid-size road vehicle motion simulators

The use of dynamic driving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called motion cueing algorithms (MCAs). In recent years several MCAs based on model predictive control (MPC) techniques have been proposed. The main drawback associated with the use of MPC is its computational burden, that may limit their application to high performance dynamic simulators. In the paper, a fast, real-time implementation of an MPC-based MCA for 9 DOF, high performance platform is proposed. Effectiveness of the approach in managing the available working area is illustrated by presenting experimental results from an implementation on a real device with a 200?Hz control frequency.     

Motion Cueing in the Renault Driving Simulator

Motion cueing in a driving simulator is necessary for advanced studies requiring drivers to accurately perceive and control the motion of their vehicle. The Renault Dynamic Simulator uses a 6-axes electro-mechanical mobile platform with an adequate motion control software. The physical and perceptual validity of the motion cueing is analyzed with respect to actual vehicle acceleration data and human self-motion per-ception criteria. Within the actuator displacement limits, it is capable of directly rendering transient vehicle accelerations whereas sustained linear acceleration cues are simulated by a coordinated platform tilt. Accel-eration transients are extracted by high-pass filtering, but a classical implementation based on linear filters may produce artifacts in some key driving situations. A non-linear motion cueing algorithm was developed to anticipate and reduce these false motion cues.     

Motion Cueing in the Renault Driving Simulator

Motion cueing in a driving simulator is necessary for advanced studies requiring drivers to accurately perceive and control the motion of their vehicle. The Renault Dynamic Simulator uses a 6-axes electro-mechanical mobile platform with an adequate motion control software. The physical and perceptual validity of the motion cueing is analyzed with respect to actual vehicle acceleration data and human self-motion per-ception criteria. Within the actuator displacement limits, it is capable of directly rendering transient vehicle accelerations whereas sustained linear acceleration cues are simulated by a coordinated platform tilt. Accel-eration transients are extracted by high-pass filtering, but a classical implementation based on linear filters may produce artifacts in some key driving situations. A non-linear motion cueing algorithm was developed to anticipate and reduce these false motion cues.     

基于模型预测控制的智能车轨迹跟踪控制研究

为了完成智能车的轨迹跟踪,提出一种基于模型预测控制的轨迹跟踪方法,利用将运动学模型这个非线性系统线性化的方案,来获得必须的线性时变系统,采取模型预测控制的三要素来设计控制器。并且基于MPC在控制过程中能增加多种约束的优点,建立基于车辆运动学模型的约束做轨迹跟踪仿真实验,最后,基于山东理工大学智能车平台上GPS提供的定位信息,在校园中采集路线并对前提规划好的的轨迹进行实车验证。实验结果表明:基于MPC算法所设计的控制器能快速且稳定地跟踪期望轨迹。     

MPC-BASED steering control for backward-driving vehicle using stereo vision

We propose a steering control algorithm for autonomous backward driving in a narrow corridor. Passable spaces are detected using a stereo camera, and the steering angle is controlled by a model predictive controller (MPC). For passable space detection, an UV-disparity map is calculated from the original disparity map. Information regarding passable spaces collected by the stereo camera is used in steering control. Backward driving requires the driver’s preemptive actions, which can be learned by experience because of the non-intuitive responses (the initial motion of the vehicle is opposite to the driver’s steering angle input). This occurs because a backward-driving vehicle is a non-minimum phase system. One of the most popular steering control algorithms is Stanley method, which is based on the feedback of lateral displacement error and heading angle error. The method is very intuitive and works well for forward driving, but it exhibits significant undershoot for backward driving cases. Furthermore, the method does not explicitly consider any constraints on control inputs and states. We designed a steering controller based on the MPC technique that requires future information but can handle constraints explicitly. Because we have near-future information from the stereo camera under limited passable spaces, MPC can be effectively implemented. We performed several simulations and experiments to show the performance and superiority of the suggested method over a simple feedback-based control algorithm.     

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.     

商用车电液复合转向系统的车道保持策略

为了提高商用车的行驶安全性,避免因驾驶人的分心驾驶出现车辆偏离车道的问题,提出一种基于电液复合转向系统的商用车车道保持策略;在建立电液复合转向系统模型、二自由度车辆模型、预瞄驾驶人模型的基础上,设计基于驾驶人在环的MPC和ADRC串级的车道保持控制策略。首先,采用MPC算法将车辆横向位置控制的最优问题转化为二次规划求得目标前轮转角;然后,考虑电液复合转向系统的不确定和干扰问题,利用ADRC算法对目标转向盘转角和实际驾驶人的转向盘转角差值以转矩信号的形式进行补偿。同时研究车道保持系统对驾驶人的干预问题,引入干预系数的概念,采用模糊控制的方法,将驾驶人手力和车辆的运动状态作为输入变量,干预系数作为输出变量,保证整车行驶安全性的前提下减小车道保持辅助系统对驾驶人的干预。最后,通过MATLAB/Simulink仿真和硬件在环试验对所设计的控制策略进行验证。研究结果表明：所设计的基于商用车电液复合转向系统的车道保持策略能够及时地纠正因驾驶人的分心驾驶而导致车辆偏离所在行驶车道的行为,特别是在弯道处出现驾驶人转向不足或过度转向的情况时,能够将车辆维持在车道线之内,保证车辆的行驶安全性,同时由于干预系数的设计,使得驾驶人也有良好的人机交互体验感。     

基于改进粒子群优化算法的多目标自适应巡航控制

跟驰过程中,在保证安全性的前提下为了提升自适应巡航控制(ACC)系统的舒适性和燃油经济性,研究了多目标自适应巡航控制算法.在建立车间纵向运动学模型的基础上,根据模型预测控制理论,设计综合考虑安全性、舒适性、燃油经济性以及车辆自身限制等因素的目标函数和约束条件,并引入松弛因子向量软化硬约束边界解决无可行解问题.进一步在滚...     

Motion cueing in high-performance vehicle simulators

The majority of motion cueing algorithms have been developed for passenger car applications, with correspondingly less research dedicated to race-car and high-performance vehicle simulators. In the high-performance context, the focus is on cueing the vehicle's behavioural and handling characteristics, particularly when driving near the limits of performance. In race-car simulators the cueing requirements are therefore quite different, with the problem made all the more challenging by the presence of large accelerations. To understand the drivers' cueing needs, the vehicle's stability and handling response characteristics must be examined near the performance boundary. Frozen-time eigenvalue analyses are used to determine stability and response characteristics across all vehicle operating conditions, including accelerating and braking under cornering, with the results used to determine motion cueing algorithm requirements. Lateral acceleration and yaw cueing filters are designed in order to retain information critical to understanding the vehicle's behaviour on its performance boundary. Cueing filters are then tested, with the help of a professional race-car driver, and are found to provide the cues necessary for the driver to control the vehicle on the limit of performance.     

Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching the motion limitations.     

Modelling minimum-time manoeuvering with global optimisation of local receding horizon control

In this paper, we explore the notion that a human driver uses a receding horizon model predictive control (MPC) scheme for minimum-time manoeuvering. However, MPC is an inherently sub-optimal control scheme because not all future information is incorporated into its finite preview horizon. In many practical applications, this sub-optimality is tolerated as the solution is sufficiently close to optimal. However, it is known that professional drivers have the ability to learn driving circuits and exploit its features to minimise their global manoeuvering time. In this paper, we will model their process with a cascaded optimisation structure. Therein, the inner-loop features a local MPC scheme tasked with finding the control inputs that achieve a blended objective of minimising time and maximising velocity in each preview horizon/distance. The outer loop of this cascaded structure computes the best set of weights for the two components of the local objectives in order to minimise the global manoeuvering time. The proposed cascaded optimisation and control approach is compared against a straight-forward fixed-cost time optimal MPC applied to minimum-time manoeuvering over two well-known race courses. The paper also includes an extended literature review and details of the computational formulation of the model approach.     

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。     

A theoretical justification of the sine with dwell manoeuvre

The sine with dwell (SWD) manoeuvre has received much attention within the context of vehicle stability testing. This manoeuvre is used in a test developed by the NHTSA, designed to certificate electronic stability control systems in light vehicles. The test is used in legislations as well as in consumer tests all over the globe. The SWD manoeuvre was designed using test vehicles on a test track and experimentally validated. The paper at hand uses optimal control theory to theoretically justify the use of the SWD manoeuvre to produce a severe lateral motion and over-steering based on steering input. It is shown that a manoeuvre similar to the SWD manoeuvre can be obtained from an optimal control problem using simple vehicle dynamics models. The optimal control method is further used to analyse the manoeuvre's robustness with respect to vehicle dimensions and tyre properties. It is shown that the manoeuvre is robust in dimensions, which theoretically motivates its application for various sizes of vehicles.     

Modelling and model predictive control for a bicycle-rider system

This study proposes a bicycle-rider control model based on model predictive control (MPC). First, a bicycle-rider model with leaning motion of the rider’s upper body is developed. The initial simulation data of the bicycle rider are then used to identify the linear model of the system in state-space form for MPC design. Control characteristics of the proposed controller are assessed by simulating the roll-angle tracking control. In this riding task, the MPC uses steering and leaning torques as the control inputs to control the bicycle along a reference roll angle. The simulation results in different cases have demonstrated the applicability and performance of the MPC for bicycle-rider modelling.     

基于模型预测控制的半挂汽车主动防侧倾控制方法

提出了一种半挂汽车主动防侧倾控制方法。搭建了一个七自由度动力学模型和一个三自由度参考模型;用无迹Kalman滤波的方法,来估计车辆的横向载荷转移率;确定优化目标,运用模型预测控制(MPC)理论进行最优化求解,得到各车轴的主动防侧倾力矩;在Simulink/Trucksim联合仿真环境中,进行仿真对比与分析。结果表明:在本文的MPC控制器和PID控制器作用下,半挂汽车各状态量皆收敛,且横向载荷转移率保持在0.7以内;相比于PID控制,MPC控制所需的防侧倾力矩更小更均衡,各状态量变化也更加平稳。因此,本MPC控制器在提升半挂汽车侧倾稳定性的同时具有较好的鲁棒性。     

A nonlinear model predictive control formulation for obstacle avoidance in high-speed autonomous ground vehicles in unstructured environments

This paper presents a nonlinear model predictive control (MPC) formulation for obstacle avoidance in high-speed, large-size autono-mous ground vehicles (AGVs) with high centre of gravity (CoG) that operate in unstructured environments, such as military vehicles. The term ‘unstructured’ in this context denotes that there are no lanes or traffic rules to follow. Existing MPC formulations for passenger vehicles in structured environments do not readily apply to this context. Thus, a new nonlinear MPC formulation is developed to navigate an AGV from its initial position to a target position at high-speed safely. First, a new cost function formulation is used that aims to find the shortest path to the target position, since no reference trajectory exists in unstructured environments. Second, a region partitioning approach is used in conjunction with a multi-phase optimal control formulation to accommodate the complicated forms the obstacle-free region can assume due to the presence of multiple obstacles in the prediction horizon in an unstructured environment. Third, the no-wheel-lift-off condition, which is the major dynamical safety concern for high-speed, high-CoG AGVs, is ensured by limiting the steering angle within a range obtained offline using a 14 degrees-of-freedom vehicle dynamics model. Thus, a safe, high-speed navigation is enabled in an unstructured environment. Simulations of an AGV approaching multiple obstacles are provided to demonstrate the effectiveness of the algorithm.     

面向全矢量线控车辆极限运动的分层式协同控制

装配四轮分布式驱动-转向（4WID-4WIS）底盘的全矢量线控车辆具备多可控自由度、高速稳定性强的特点,是极限工况稳定裕度和安全性较高的理想车型。为了解决全矢量线控车辆在极限工况下纵横向控制冲突危害行车安全的问题,提出一种基于模型预测控制 （MPC） 的分层式车辆纵向和横向运动协同控制方法。建立基于单轨模型的期望运动状态识别方法,设计模型预测控制器转换动力学目标,采用泰勒展开和前向欧拉方法对预测模型进行线性离散化处理;设计基于负荷率的轮胎力优化分配方法,利用反正切轮胎逆模型求解控制执行量。仿真结果表明,协同控制方法能显著提高车辆在不同路面下的极限运动稳定性,更精准地跟踪期望运动状态,扩大稳定裕度,保障行车安全。     

Optimal preview game theory approach to vehicle stability controller design

Dynamic game theory brings together different features that are keys to many situations in control design: optimisation behaviour, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In the presented methodology, vehicle stability is represented by a cooperative dynamic/difference game such that its two agents (players), namely the driver and the direct yaw controller (DYC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the DYC control algorithm is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degrees-of-freedom vehicle-handling performance model is put into discrete form to develop the game equations of motion. To evaluate the developed control algorithm, CarSim with its built-in nonlinear vehicle model along with the Pacejka tire model is used. The control algorithm is evaluated for a lane change manoeuvre, and the optimal set of steering angle and corrective yaw moment is calculated and fed to the test vehicle. Simulation results show that the optimal preview control algorithm can significantly reduce lateral velocity, yaw rate, and roll angle, which all contribute to enhancing vehicle stability.     

Implementation and verification of adaptive longitudinal traction control

Human in the loop (HIL) simulation has experienced a significant increase in popularity in recent years. In this work, a novel approach to traction control is developed and implemented in a HIL environment, exploiting the significant advantages of framing the problem in a manner that more closely matches how a human expert drives a vehicle. An adaptive gradient ascent algorithm is at the heart of the proposed solution to longitudinal traction control. A real-time implementation of the gradient ascent algorithm is developed using linear operator techniques, even though the tyre–ground interface is highly non-linear. The adaptive traction control algorithm is based on two separate, but related, estimation algorithms that estimate both the instantaneous traction force and a unique predictive traction force model. This adaptive control algorithm, the necessary estimation algorithms and their real-time implementation are described in this work. The results, when implemented as a driver assist application on a 6-DOF motion platform, with a HIL, are also presented. This work demonstrates the utility of a 6-DOF motion platform in developing and verifying vehicle control algorithms with a HIL.     

Output feedback H ∞/GH 2 preview control of active vehicle suspensions: a comparison study of LQG preview

This study concerns with multi-objective H _∞/GH ₂ preview control of active vehicle suspensions. This control scheme has two main aspects: first, it allows constrained outputs of the system to vary freely as long as they remain within their given bounds, in order that the best possible performance could be delivered. Secondly, the optimisation as well as constraint fulfilment is done for the worst-case road disturbances to cover all road types. To design a system to perform satisfactorily for a wide range of road irregularities, H _∞-norm is used wherever minimisation is required, and generalised H ₂-norm is used to care for the constraints on suspension working space. Moreover, to ensure desired stability margins for the system, pole location constraints are considered in the design. The proposed approach is evaluated on a quarter-car model and compared with the state-of-the-art preview control algorithm in the literature, namely, Linear quadratic Gaussian preview. Simulation results demonstrate the effectiveness of the proposed approach.