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.     

Model predictive driving simulator motion cueing algorithm with actuator-based constraints

The simulator motion cueing problem has been considered extensively in the literature; approaches based on linear filtering and optimal control have been presented and shown to perform reasonably well. More recently, model predictive control (MPC) has been considered as a variant of the optimal control approach; MPC is perhaps an obvious candidate for motion cueing due to its ability to deal with constraints, in this case the platform workspace boundary. This paper presents an MPC-based cueing algorithm that, unlike other algorithms, uses the actuator positions and velocities as the constraints. The result is a cueing algorithm that can make better use of the platform workspace whilst ensuring that its bounds are never exceeded. The algorithm is shown to perform well against the classical cueing algorithm and an algorithm previously proposed by the authors, both in simulation and in tests with human drivers.     

On model-based longitudinal feed-forward motion control for low velocities on known road profiles

ABSTRACT

So far, longitudinal motion control has focused on situations like highway driving, where disturbances of the road profile can be neglected. In this paper, we show how the Two Point Tire Model can be used to derive a novel feed-forward control law for a vehicle's longitudinal motion that considers the effects of the road profile and can complement existing control approaches. For this purpose, we recapitulate the basic model assumptions and equations and briefly discuss how it can be used on arbitrary road profiles. Two approaches for implementation in a real vehicle are presented. Comparisons of these approaches in simulation and to a human driver of an experimental vehicle show that the controller can deal with stepped obstacles of up to 14?cm in height. However, the control performance is essentially limited by the actuator delay and human drivers outperform the controller due to their ability of sensing subtle vehicle motions. The results indicate that the control performance can be further improved by using a preview on the necessary drive torque, which can be provided by the solution that we propose.     

面向瓶颈多簇运动波消除的拥堵吸收智能驾驶模型

高速公路瓶颈运动波是诱发通行延误与事故风险主要原因.智能网联下精细化感知与精准化控制为消除运动波影响提供了技术环境.在构建一种在线、主动消除广域运动波的拥堵吸收智能驾驶模型的基础上,解析车头间距与运动波传播关联机制,确定运动波完全消除设计实施条件,提出消除单个运动波最佳驾驶策略;面向多簇运动波形成时间[位置[规模等特性...     

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.     

混合动力传动系 Tip-in/out工况的模型预测控制

为抑制混合动力汽车加减速过程中传动系统振荡,以电机转矩为控制量,提出一种基于模型预测主动控制混合动力传动系统振荡的策略,基于 Matlab/Simulink平台搭建动态系统模型,实时计算电机转矩补偿优化发动机输出转矩,准确跟踪目标转矩的同时减少传动系统振荡。探索不同控制器参数设置对于驾驶动力性和舒适性的增益效果,通过硬件在环 （Hardware-in-Loop,HIL） 试验表明,所设计的 MPC控制器能使汽车平稳地加减速,迅速跟踪目标转速,求解时间控制在10 ms以内,具有较好的实时性,同时对传动系统中的非线性因素和参数变化有较好的鲁棒性。     

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。     

基于自适应模型预测的智能汽车横向轨迹跟踪控制

当路面附着情况和车辆行驶状态不断变化时,基于恒定侧偏刚度的模型预测控制(MPC)不能考虑轮胎非线性特性的影响,难以保证车辆轨迹跟踪的适应性。为此,提出一种考虑轮胎侧向力计算误差的自适应模型预测控制(AMPC),以提高智能汽车在不确定工况下的轨迹跟踪性能。分析了路面附着系数和垂向载荷对轮胎侧向力的影响,基于平方根容积卡尔曼滤波(SCKF)算法,设计了利用侧向加速度和横摆角速度作为测量变量的前后轮胎侧向力估计器。利用轮胎侧向力线性计算值与估计值的差值计算得到侧偏刚度修正因子,设计了前后轮胎侧偏刚度的自适应修正准则,进而提出了一种基于时变修正刚度的AMPC控制方法。基于CarSim与MATLAB/Simulink联合仿真和硬件在环测试平台,对AMPC控制的有效性和实时性进行了验证。研究结果表明：在不同的路面附着情况和车辆行驶状态下,AMPC控制都能够降低横向位置偏差和航向角偏差,有效提高车辆的轨迹跟踪精度,其控制效果明显优于基于恒定侧偏刚度的标准MPC控制。尤其在低附着工况下,标准MPC控制会因为线性轮胎力的计算误差过大而导致车辆在轨迹跟踪时严重失稳,而AMPC控制通过估计轮胎力修正侧偏刚度依然能够保证车辆稳定有效的跟踪参考轨迹。所提出的AMPC控制在保证控制精度的同时具有良好的实时性,对智能汽车控制系统的设计与优化具有重要参考价值。     

Integrated fuzzy/optimal vehicle dynamic control

There are basically two methods to control yaw moment which is the most efficient way to improve vehicle stability and handling. The first method is indirect yaw moment control, which works based on control of the lateral tire force through steering angle control. It is mainly known as active steering control (ASC). Nowadays, the most practical approach to steering control is active front steering (AFS). The other method is direct yaw moment control (DYC), in which an unequal distribution of longitudinal tire forces (mainly braking forces) produces a compensating external yaw moment. It is well known that the AFS performance is limited in the non-linear vehicle handling region. On the other hand, in spite of a good performance of DYC in both the linear and non-linear vehicle handling regions, continued DYC activation could lead to uncomfortable driving conditions and an increase in the stopping distance in the case of emergency braking. It is recommended that DYC be used only in high-g critical maneuvers. In this paper, an integrated fuzzy/optimal AFS/DYC controller has been designed. The control system includes five individual optimal LQR control strategies; each one, has been designed for a specific driving condition. The strategies can cover low, medium, and high lateral acceleration maneuvers on high-μ or low-μ roads. A fuzzy blending logic also has been utilized to mange each LQR control strategy contribution level in the final control action. The simulation results show the advantages of the proposed control system over the individual AFS or DYC controllers.     

汽油机开放式电控系统设计

以高性能MPC564单片机为平台开发了汽油机开放式电控系统,设计必要的外围电路并编写相应的控制程序,以479QE汽油发动机为验证对象,实现了该机从起动到怠速的控制。台架试验表明,该电控系统能按规定控制要求和控制策略实现各项控制功能。     

Trends in vehicle motion control for automated driving on public roads

ABSTRACT

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed.     

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.     

Vehicle motion simulators,a key step towards road vehicle dynamics improvement

Real road vehicle tests are time consuming, laborious, and costly, and involve several safety concerns. Road vehicle motion simulators (RVMS) could assist with vehicle testing, and eliminate or reduce the difficulties traditionally associated with conducting vehicle tests. However, such simulators must exhibit a high level of fidelity and accuracy in order to provide realistic and reliable outcomes. In this paper, we review existing RVMS and discuss each of the major RVMS subsystems related to the research and development of vehicle dynamics. The possibility of utilising motion simulators to conduct ride and handling test scenarios is also investigated.     

Model Predictive Coordinated Control for Dual-Mode Power-Split Hybrid Electric Vehicle

Power-split hybrid electric vehicles (HEVs) have great potential fuel efficiency and have attracted extensive research attention with regard to their control system. The coordinated controller in HEV plays an important role in tracking the optimal state reference generated by the energy management strategy (EMS), so as to reach the desired fuel efficiency. Meanwhile, the coordinated controller also has a significant impact on driving performance. To improve its performance, the design of a model predictive control (MPC) based coordinated controller in power-split HEV is presented. First, a non-linear, time-varying constrained control oriented transmission model of a dual-mode power-split HEV is formulated to describe this control problem. Then, to solve this problem, the non-linear part in the transmission model is linearised, and a linear MPC is used to obtain the control signals for the motors and engine at each time step. To meet the requirements of real-time computation, a fast MPC method is also applied to reduce the online computation effort. Simulations and experiments demonstrate the effectiveness of the proposed MPC-based coordinated controller.     

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.     

Vehicle dynamics testing in motion based driving simulators

ABSTRACT

This article investigates the potential of a motion-based driving simulator in the field of vehicle dynamics testing, specifically for heavy vehicles. For this purpose, a case study was prepared embodying the nature of a truck dynamics test setup. The goal was to investigate if the drivers in the simulator could identify the handling differences owed to changes in vehicle parameters, while driving the simulated trucks. Results show that the drivers could clearly identify the differences in vehicle behaviour for most of the performed tests, which motivates further investigative work in this area and exposes the feasibility of heavy vehicle dynamics testing in simulators.     

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.     

Robust cascade control for the horizontal motion of a vehicle with single-wheel actuators

The article presents a cascade control for the horizontal motion of a vehicle with single-wheel actuators. The outer control loop for the longitudinal and lateral accelerations and the yaw rate ensures a desired vehicle motion. By a combination of state feedback control and observer-based disturbance feedforward the inner control loop robustly stabilises the rotating and steering motions of the wheels in spite of unknown frictions between tyres and ground. Since the three degrees of freedom of the horizontal motion are affected by eight tyre forces, the vehicle considered is an over-actuated system. Thus additional control objectives can be realised besides the desired motion trajectory as, for example, a maximum in driving safety. The corresponding analytical tyre force allocation also guarantees real-time capability because of its relatively low computational effort. Provided suitable fault detection and isolation are available, the proposed cascade control has the potential of fault-tolerance, because the force allocation is adaptable. Another benefit results from the modular control structure, because it allows a stepwise implementation. Besides, it only requires a small number of measurements for control purposes. These measurements are the rotational speeds and steering angles of the wheels, the longitudinal and lateral acceleration and the yaw rate of the vehicle.     

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。