고성능 한계 핸들링을 위한 인휠모터 토크벡터링 제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 이경수.지난 10년 동안 차량 자세 제어시스템(ESC)은 치명적인 충돌을 방지하기 위해 많은 상용 차량에서 비약적으로 발전되고 개발되고 있다. 특히, 차량 자세 제어 시스템은 악천후로 인한 미끄러운 도로와 같은 위험한 도로에서 불안정한 차량 주행 조건에서 사고를 피하는데 큰 역할을 한다. 그러나, 최근의 경우, 고성능 차량 또는 스포츠카 등의 경우 제동제어의 빈번한 개입은 운전의 즐거움을 감소시키는 불만도 존재한다. 최근 차량의 전동화와 함께, 자량 자세 제어시스템의 작동 영역인 한계 주행 핸들링 조건에서 각 휠의 독립적인 구동을 적용 할 수 있는 시스템 중 하나인 인휠 모터 시스템을 사용하여 차량의 종, 횡방향 특성을 제어 가능하게 하는 토크 벡터링 제어기술에 대한 연구가 활발하다. 따라서, 본 연구에서는 차량의 선회 한계 핸들링 조건에서 안정성과 주행 다이나믹 성능을 향상시킬 수 있는 토크 벡터링 제어기를 제안하고자 한다. 먼저, 차량의 비선형 주행 구간인 한계 핸들링 조건에 대한 자동 드리프트 제어 알고리즘을 제안한다. 이 알고리즘을 이용하여 토크벡터링제어에 차량의 다이나믹한 주행모드에 대한 통찰력을 제공하고 미끄러운 도로에서 차량의 높은 슬립 각도의 안정성 제어를 제공 할 수 있다. 또한, 인휠 모터 시스템을 차량의 전륜에 2개 모터로 사용하여 차량 고유의 특성인 차량 언더스티어 구배를 직접적 제어를 수행하여, 차량의 핸들링 성능을 향상시켰다. 제어기의 채터링 효과를 줄이고 빠른 응답을 얻기 위해 새로운 과도 매개 변수가 이용하여 수식화하여 구성하였으며, 차량의 정상 상태 및 과도 특성 향상을 검증하기 위하여 ISO 기반 시뮬레이션 및 차량 실험을 수행하였다. 마지막으로 요 제어기와 횡 슬립 각도 제어기로 구성된 MASMC (Multiple Adaptive Sliding Mode Control) 접근 방식을 사용하는 4륜 모터 시스템을 사용한 동적 토크벡터링 제어를 수행하였다. 높은 비선형 특성을 가진 차량의 전후륜 타이어의 코너링 강성은 적응제어기법을 이용하여 예측하였다. 따라서, 안전모드와 다이나믹 모드를 구성하여, 운전자로 하여금 원하는 주행의 조건에 맞게 선택할 수 있는 알고리즘을 구현하였다. 이 MASMC 알고리즘은 향후 전동화 차량에 주행안정성 향상과 다이나믹한 주행의 즐거움을 주는 기술로써, 전차량 시뮬레이션을 이용하여 검증하였다.In the last ten decades, vehicle stability control systems have been dramatically developed and adapted in many commercial vehicles to avoid fatal crashes. Significantly, ESC (Electric Stability Control) system can help escape the accident from unstable driving conditions with dangerous roads such as slippery roads due to inclement weather conditions. However, for the high performed vehicle, frequent intervention from ESC reduces the pleasure of fun-to-drive. Recently, the development of traction control technologies has been taking place with that of the electrification of vehicles. The IWMs (In-Wheel Motor system), which is one of the systems that can apply independent drive of each wheel, for the limit handling characteristics, which are the operation areas of the ESC, is introduced for the control that enables the lateral characteristics of the vehicle dynamics. Firstly, the automated drift control algorithm can be proposed for the nonlinear limit handling condition of vehicles. This approach can give an insight of fun-to-drive mode to TV (Torque Vector) control scheme, but also the stability control of high sideslip angle of the vehicle on slippery roads. Secondly, using IWMs system with front two motors, understeer gradient of vehicle, which is the unique characteristics of vehicle can be used for the proposed control strategy. A new transient parameter is formulated to be acquired rapid response of controller and reducing chattering effects. Simulation and vehicle tests are conducted for validation of TV control algorithm with steady-state and transient ISO-based tests. Finally, dynamic torque vectoring control with a four-wheel motor system with Multiple Adaptive Sliding Mode Control (MASMC) approach, which is composed of a yaw rate controller and sideslip angle controller, is introduced. Highly nonlinear characteristics, cornering stiffnesses of front and rear tires are estimated by adaptation law with measuring data. Consequently, there are two types of driving modes, the safety mode and the dynamic mode. MASMC algorithm can be found and validated by simulation in torque vectoring technology to improve the handling performance of fully electric vehicles.Chapter 1 Introduction 7 1.1. Background and Motivation 7 1.2. Literature review 11 1.3. Thesis Objectives 15 1.4. Thesis Outline 15 Chapter 2 Vehicle dynamic control at limit handling 17 2.1. Vehicle Model and Analysis 17 2.1.1. Lateral dynamics of vehicle 17 2.1.2. Longitudinal dynamics of vehicle 20 2.2. Tire Model 24 2.3. Analysis of vehicle drift for fun-to-drive 28 2.4. Designing A Controller for Automated Drift 34 2.4.1. Lateral controller 35 2.4.2. Longitudinal Controller 37 2.4.3. Stability Analysis 39 2.4.4. Validation with simulation and test 40 Chapter 3 Torque Vectoring Control with Front Two Motor In-Wheel Vehicles 47 3.1. Dynamic Torque Vectoring Control 48 3.1.1. In-wheel motor system (IWMs) 48 3.1.2. Dynamic system modeling 49 3.1.3. Designing controller 53 3.2. Validation with Simulation and Experiment 59 3.2.1. Simulation 59 3.2.2. Vehicle Experiment 64 Chapter 4 Dynamic handling control for Four-wheel Drive In-Wheel platform 75 4.1. Vehicle System Modeling 76 4.2. Motion Control based on MASMC 78 4.2.1. Yaw motion controller for the inner ASMC 80 4.2.2. Sideslip angle controller for the outer ASMC 84 4.3. Optimal Torque Distribution (OTD) 88 4.3.1. Constraints of dynamics 88 4.3.2. Optimal torque distribution law 90 4.4. Validation with Simulation 91 4.4.1. Simulation setup 91 4.4.2. Simulation results 92 Chapter 5 Conclusion and Future works 104 5.1 Conclusion 104 5.2 Future works 106 Bibliography 108 Abstract in Korean 114박

Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control

Vehicle safety remains an important topic in the automotive industry due to the large number of vehicle accidents each year. One of the causes of vehicle accidents is due to vehicle instability phenomena. Vehicle instability can occur due to unexpected road profile changes, during full braking, obstacle avoidance or severe manoeuvring. Three main instability phenomena can be distinguished: the yaw-rate instability, the rollover and the jack-knife phenomenon. The main goal of this study is to develop a yaw-rate and rollover stability controller of an Autonomous Scaled Ground Vehicle (ASGV) using Nonlinear Model Predictive Control (NMPC). Open Source Software (OSS) known as Automatic Control and Dynamic Optimisation (ACADO) is used to design and simulate the NMPC controller based on an eight Degree of Freedom (8 DOF) nonlinear vehicle model with Pacejka tire model. Vehicle stability limit were determined using load transfer ratio (LTR). Double lane change (DLC) steering manoeuvres were used to calculate the LTR. The simulation results show that the designed NMPC controller is able to track a given trajectory while preventing the vehicle from rolling over and spinning out by respecting given constraints. A maximum trajectory tracking error of 0.1 meters (on average) is reported. To test robustness of the designed NMPC controller to model mismatch, four simulation scenarios are done. Simulation results show that the controller is robust to model mismatch. To test disturbance rejection capability of the controller, two simulations are performed, with pulse disturbances of 0.02 radians and 0.05 radians. Simulations results show that the controller is able to reject the 0.02 radians disturbance. The controller is not able to reject the 0.05 radians disturbance

Closed-loop controller for post-impact vehicle dynamics using individual wheel braking and front axle steering

This paper presents a vehicle path controller for reducing the maximum lateral deviation (Ymax) after an initial impact in a traffic accident. In previous research, a Quasi-Linear Optimal Controller (QLOC) was proposed and applied to a simple vehicle model with individually controlled brake actuators. QLOC uses non-linear optimal control theory to provide a semiexplicit approximation for optimal post-impact path control, and in principle can be applied to an arbitrary number of actuators. The current work extends and further validates the control method by analysing the effects of adding an active front axle steering actuator at different post-impact kinematics, as well as increasing the fidelity of the vehicle model in the closed-loop controlled system. The controller performance is compared with the results from open-loop numerical optimisation which uses the same vehicle model. The inherent robustness properties of the QLOC algorithm are demonstrated by its direct application to an independent high-fidelity multi-body vehicle model. Towards real-time implementation, the algorithm is further simplified so that the computational efficiency is enhanced, whereas the performance is shown not to be degraded

Model-Based Vehicle Dynamics Control for Active Safety

The functionality of modern automotive vehicles is becoming increasingly dependent on control systems. Active safety is an area in which control systems play a pivotal role. Currently, rule-based control algorithms are widespread throughout the automotive industry. In order to improve performance and reduce development time, model-based methods may be employed. The primary contribution of this thesis is the development of a vehicle dynamics controller for rollover mitigation. A central part of this work has been the investigation of control allocation methods, which are used to transform high-level controller commands to actuator inputs in the presence of numerous constraints. Quadratic programming is used to solve a static optimization problem in each sample. An investigation of the numerical methods used to solve such problems was carried out, leading to the development of a modified active set algorithm.Vehicle dynamics control systems typically require input from a number of supporting systems, including observers and estimation algorithms. A key parameter for virtually all VDC systems is the friction coefficient. Model-based friction estimation based on the physically-derived brush model is investigated

VEHICLE DYNAMICS MODELING FOR AUTONOMOUS DRIFTING AND CLOTHOID BASED WAYPOINT INTERPOLATION

The advent of autonomous vehicles necessitates a redefinition of road safety regulations, considering a controller can possess better driving skills than an average person. The work presented here partly focuses on a vehicle dynamics model development to help imitate and control vehicle drifting maneuvers. As we see, a professional driver drifting through the traffic while keeping the car safe, it can be utilized to avoid accidents at high speeds, if required. Although drifting can produce higher yaw rates than the regular driving regime, these control capabilities have not yet been exploited in the current automotive control systems. Therefore, this report focuses on developing a vehicle dynamics model to simulate the drifting of an autonomous vehicle that utilizes this high yaw rate property. Drifting control capabilities will increase the vehicle\u27s ability to avoid collisions scenarios where higher than typical yaw rates are required. The other part of the report uses vehicle kinematics equations to generate feasible path planning algorithms for any autonomous vehicle. If we constraint these vehicle kinematics equations for linearly varying curvature of the path, they are called Clothoid curves. This linearly varying curvature is analogous to having a continuous lateral acceleration on the vehicle while cornering, i.e., avoiding jerks. Here, Clothoids are used for the interpolation of waypoints along the path. A mission planner defines waypoints a few meters apart from each other; then, GPS coordinates are used to check whether the vehicle is following these waypoints correctly. Therefore, waypoint interpolation is required for continuous feed of track coordinates to the controller to make faster corrections for the cross-track error and not to wait every time for a low rate GPS signal. Also, the Clothoid curves are used in road construction, thus also provides the ability to create a road model along with a vehicle model which can further be used for better state estimation

Synthesis and Analysis of an Active Independent Front Steering (AIFS) System

Technological developments in road vehicles over the last two decades have received considerable attention towards pushing the safe performance limits to their ultimate levels. Towards this goal, Active Front Steering (AFS) and Direct Yaw-moment Control (DYC) systems have been widely investigated. AFS systems introduce corrective steering angles to the conventional system in order to realize a target handling response for a given speed and steering input. An AFS system, however, may yield limited performance under severe steering maneuvers involving substantial lateral load shift and saturation of the inside tire-road adhesion. The adhesion available at the outer tire, on the other hand, would remain under-utilized. This dissertation explores effectiveness of an Active Independent Front Steering (AIFS) system that could introduce a corrective measure at each wheel in an independent manner. The effectiveness of the AIFS system was investigated firstly through simulation of a yaw-plane model of a passenger car. The preliminary simulation results with AIFS system revealed superior potential compared to the AFS particularly in the presence of greater lateral load shift during a high-g maneuver. The proposed concept was thus expected to be far more beneficial for enhancement of handling properties of heavy vehicles, which invariably undergo large lateral load shift due to their high center of mass and roll motion. A nonlinear yaw-plane model of a two-axle single-unit truck, fully and partially loaded with solid and liquid cargo, with limited roll degree-of-freedom (DOF) was thus developed to study the performance potentials of AIFS under a range of steering maneuvers. A simple PI controller was synthesized to track the reference yaw rate response of a neutral steer vehicle. The steering corrections, however, were limited such that none of the tires approach saturation. For this purpose, a tire saturation zone was identified considering the normalized cornering stiffness property of the tire. The controller strategy was formulated so as to limit the work-load magnitude at a pre-determined level to ensure sufficient tire-road adhesion reserve to meet the braking demand, when exists. Simulation results were obtained for a truck model integrating AFS and AIFS systems subjected to a range of steering maneuvers, namely: a J-turn maneuver on uniform as well as split-μ road conditions, and path change and obstacle avoidance maneuvers. The simulation results showed that both AFS and AIFS can effectively track the target yaw rate of the vehicle, while the AIFS helped limit saturation of the inside tire and permitted maximum utilization of the available tire-road adhesion of the outside tire. The results thus suggested that the performance of an AIFS system would be promising under severe maneuvers involving simultaneous braking and steering, since it permitted a desired adhesion reserve at each wheel to meet a braking demand during the steering maneuver. Accordingly, the vehicle model was extended to study the dynamic braking characteristics under braking-in-turn maneuvers. The simulation results revealed the most meritorious feature of the AIFS in enhancing the braking characteristics of the vehicle and reducing the stopping time during such maneuvers. The robustness of the proposed control synthesis was subsequently studied with respect to parameter variations and external disturbance. This investigation also explores designs of fail-safe independently controllable front wheels steering system for implementation of the AIFS concept

A systematic approach to cooperative driving systems based on optimal control allocation

This dissertation proposes a systematic approach to vehicle dynamic control, where interaction between the human driver and on-board automated driving systems is considered a fundamental part of the overall control design. The hierarchical control system is to address motion control in three regions. First is normal driving, where the vehicle stays within the linear region of the tyre. Second is limit driving, where the vehicle stays within the nonlinear region of the tyre. Third is over-limit driving, where the driver demands go beyond the tyre force limits. The third case is addressed by a proposed control moderator (CM). The aim is to consider all three cases within a consistent hierarchical chassis control framework. The upper-level of the hierarchical control structure relates to both optimal vehicle control under normal and limit driving, and saturating driver demands for over-limit driving, these corresponding to a fully autonomous controller and driver assistance controller respectively. Model Predictive Control (MPC) is used as the core control technique for path following under normal driving conditions, and a Moderated Particle Reference (MPR) control strategy is proposed for the road departure mitigation during limit and over-limit driving. The MPR model is validated to ensure predictable and stable operation near the friction limits, maintaining controllability for curvature and speed tracking, which effectively limits demands on the vehicle while preserving the control interaction of the driver. In the next level of the hierarchical control structure, a novel control allocation (CA) approach based on pseudo-inverse method is proposed, while a general linearly constrained quadratic programming (CQP) approach is considered as a benchmark. From extended simulation experiments, it is found that the proposed Pseudo-Inverse CA (PICA) method can achieve a close match to CQP performance in normal driving conditions. This applies for multiple control targets (including path tracking, energy-efficient, etc.) and PICA is found to achieve improved performance in limit and over-limit driving, again addressing multiple control targets (including road departure mitigation, energyefficient, etc.). Furthermore, the PICA method shows its inherent advantages of achieving the same control performance with much less computational cost and is guaranteed to provide a feasible control target for the actuators to track during the highly dynamic driving scenarios. In addition, it can effectively solve the constrained optimal control problem with additional mechanical and electronic actuator constraints. Thus, the proposed PICA method, which uses Control Re-Allocation (making multiple calls to the pseudo-inverse operator) can be considered a feasible and novel alternative approach to control allocation, with advantages over the standard CQP method. Finally, in the lower-level of the hierarchical control structure, the desired tyre control variables are obtained through an analytical inverse tyre model and a sliding mode controller (SMC) is employed for the actuators to track the control target. The proposed hierarchical control system is validated with both driving simulator studies and from testing a real vehicle, considering a wide range of driving scenarios, from low-speed path tracking to safety-critical vehicle dynamic control. It therefore opens up a systematic approach to extended vehicle control applications, from fully autonomous driving to driver assistance systems and control objects from passenger cars to vehicles with higher centre of gravity (CoG) like SUVs, trucks and etc. . .

State Estimation and Control of Active Systems for High Performance Vehicles

In recent days, mechatronic systems are getting integrated in vehicles ever more. While stability and safety systems such as ABS, ESP have pioneered the introduction of such systems in the modern day car, the lowered cost and increased computational power of electronics along with electrification of the various components has fuelled an increase in this trend. The availability of chassis control systems onboard vehicles has been widely studied and exploited for augmenting vehicle stability. At the same time, for the context of high performance and luxury vehicles, chassis control systems offer a vast and untapped potential to improve vehicle handling and the driveability experience. As performance objectives have not been studied very well in the literature, this thesis deals with the problem of control system design for various active chassis control systems with performance as the main objective. A precursor to the control system design is having complete knowledge of the vehicle states, including those such as the vehicle sideslip angle and the vehicle mass, that cannot be measured directly. The first half of the thesis is dedicated to the development of algorithms for the estimation of these variables in a robust manner. While several estimation methods do exist in the literature, there is still some scope of research in terms of the development of estimation algorithms that have been validated on a test track with extensive experimental testing without using research grade sensors. The advantage of the presented algorithms is that they work only with CAN-BUS data coming from the standard vehicle ESP sensor cluster. The algorithms are tested rigorously under all possible conditions to guarantee robustness. The second half of the thesis deals with the design of the control objectives and controllers for the control of an active rear wheel steering system for a high performance supercar and a torque vectoring algorithm for an electric racing vehicle. With the use of an active rear wheel steering, the driver’s confidence in the vehicle improves due a reduction in the lag between the lateral acceleration and the yaw rate, which allows drivers to push the vehicle harder on a racetrack without losing confidence in it. The torque vectoring algorithm controls the motor torques to improve the tire utilisation and increases the net lateral force, which allows professional drivers to set faster lap times

Integrated Path-tracking and Control Allocation Controller for Autonomous Electric Vehicle under Limit Handling Condition

In current literature, a number of studies have separately considered path-tracking (PT) control and control allocation (CA) method, but few of studies have integrated them together. This study proposes an integrated PT and CA method for autonomous electric vehicle with independent steering and driving actuators in the limit handling scenario. The high-level feedback PT controller can determine the desired total tire forces and yaw moment, and is designed to guarantee yaw angle error and lateral deviation converge to zero simultaneously. The low-level CA method is formulated as a compact quadratic programming (QP) optimization formulation to optimally allocate individual control actuator. This CA method is designed for a prototype experiment electric vehicle with particularly steering and driving actuator arrangement. The proposed integrated PT controller is validate through numerical simulation based on a high-fidelity CarMaker model on highspeed limit handling scenario