극한 주행 핸들링 성능 개선을 위한 토크벡터링 제어 알고리즘

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계공학부, 2023. 2. 이경수.This dissertation comprehensively details the design of a torque vectoring control algorithm for enhanced cornering performance using two front in-wheel motors (IWMs) and electronic limited slip differential (eLSD) at the rear axle. The main scopes to be covered in this dissertation can be divided into two categories: 1) individual control of IWM for torque vectoring control at the front axle; 2) integrated control of IWM and eLSD for both front and rear axle. First, an individual control strategy of two front IWMs in a rear-wheel-drive vehicle has been designed to improve the cornering performance. The individual control of IWMs consists of steady-state and transient control input. The steady-state control input is devised to improve the steady-state cornering response with modifying the vehicle understeer gradient, and the transient control input is designed to enhance the lateral stability by increasing the yaw rate damping coefficient. The proposed algorithm has been investigated through both computer simulations and vehicle tests, in order to show that the proposed algorithm can enhance the cornering response achieving the control objectives and to show the superior control performance compared to the other cases, such as yaw rate tracking algorithm and uncontrolled case. Second, the integrated control of two front IWMs and eLSD is designed to enhance the cornering performance at high speeds considering the characteristics of each actuator. The two front IWMs are controlled to improve the cornering performance based on a feedforward control, and the eLSD is utilized for the yaw rate feedback control. The computer simulations are conducted to show the effects of each actuator on the vehicle lateral motion at aggressive cornering with longitudinal acceleration and deceleration. Additionally, vehicle test results show that the proposed controller improves the cornering performance at the limits of handling compared to the uncontrolled case. In summary, this dissertation proposes a control algorithm for an enhanced limit handling performance based on vehicle understeer gradient and yaw rate damping characteristics, addressing also integrated control of in-wheel motors and electronic limited slip differential with considering the characteristics of each actuator. The proposed IWM control law is formulated to shape the understeer characteristics during steady-state cornering and yaw rate damping characteristic during transient cornering, and the eLSD control is designed to track the reference yaw rate. Computer simulations and vehicle tests are conducted to validate the control performance of the proposed algorithm, showing significant improvements in the agility and the stability of a test vehicle without chattering issues. Additionally, the vehicle tests at a racing track confirm the enhanced limit handling performance.본 논문은 전륜 인휠모터와 후륜 전자식 차동 제한 장치를 이용하여 선회 성능 개선을 위한 토크벡터링 제어 알고리즘에 대해 포괄적으로 설명하였다. 본 논문에서 다루는 주요 연구 범위는 크게 두 가지 범주로 나뉠 수 있다. 첫 번째는 전륜 인휠모터를 이용한 개별적인 토크벡터링 제어이고, 두 번째는 전륜 인휠모터 및 후륜 전자식 차동제한장치를 모두 이용한 전후륜 통합 토크벡터링 제어이다. 첫 번째로, 후륜 구동 차량 내에서 두 개의 전륜 인휠 모터를 활용한 선회 성능 개선을 위한 제어 알고리즘이 설계되었다. 인휠 모터 독립 제어는 정상상태 제어 입력과 과도응답 상태 제어 입력으로 구성되어 있다. 정상상태 제어 입력은 차량의 언더스티어 구배를 변형하면서 정상상태 선회 반응을 개선하기 위해 고안되었고, 과도응답 상태 제어 입력은 차량의 요댐핑 계수를 증가시킴으로써 차량의 횡방향 안정성을 개선하기 위해 설계되었다. 제안된 알고리즘의 성능은 컴퓨터 시뮬레이션과 차량 실험을 통해 확인하였다. 실험 결과에서 알 수 있듯이, 제안된 알고리즘은 제어 목표를 달성하며 차량의 선회 성능을 개선할 수 있었다. 두 번째로, 각 엑츄에이터의 특성을 고려하고 고속 주행 상황에서의 선회 성능을 개선하기 위해, 두 개의 전륜 인휠 모터와 후륜의 전자식 차동 제한 장치의 통합 제어 알고리즘이 설계되었다. 두 개의 전륜 인휠 모터는 피드포워드 제어를 기반으로 선회 성능을 개선하기 위해 제어되었고, 후륜의 전자식 차동 제한 장치는 요레이트 피드백 제어를 위해 활용되었다. 컴퓨터 시뮬레이션은 감가속을 포함한 공격적인 선회 상황에서 각 엑츄에이터의 제어 효과를 보여주기 위해 수행되었다. 추가적으로, 차량 실험 결과를 통해 제안된 제어기가 제어되지 않은 경우에 비해 핸들링 한계 상황에서의 선회 성능을 개선할 수 있다는 점을 보여주었다. 요약하자면, 본 연구에서는 차량의 언더스티어 그레디언트와 요레이트 댐핑 특성에 기반한 한계 핸들링 성능 개선을 위한 제어 알고리즘을 제안하였다. 또한, 인휠모터와 전자식 차동 제한 장치의 각 엑츄에이터 특성을 고려하여 인휠모터와 전자식 차동 제한 장치의 통합 제어에 대해 다루었다. 제안된 인휠모터 제어기는 정상상태 선회에서의 언더스티어 그레디언트와 과도응답상태 선회에서의 요레이트 댐핑 특성을 변형하기 위해 고안되었고, 전자식 차동 제한 장치 제어는 목표 요레이트를 추종하기 위해 설계되었다. 제안된 제어기를 검증하기 위해, 컴퓨터 시뮬레이션과 실차 실험이 진행되었고, 차량의 선회 안정성과 민첩성이 채터링 문제없이 확연히 개선된다는 것을 보여주었다. 추가적으로, 레이싱 트랙에서의 실차 실험을 통해 개선된 한계 핸들링 성능 또한 제시되었다.Chapter 1. Introduction 1 1.1. Background and motivation 1 1.2. Previous research on considering tire characteristics 4 1.2. Previous research on vehicle controller design 8 1.3. Thesis objectives 13 1.4. Thesis outline 15 Chapter 2. Vehicle Control System 17 2.1. Vehicle chassis system 17 2.2. Vehicle tire-road interactions 22 2.3. Tire characteristics at the limits of handling 35 Chapter 3. Torque Vectoring Control with In-Wheel Motors (IWMs) 49 3.1. Upper level controller 53 3.1.1. Control strategies for steady-state response 54 3.1.2. Control strategies for transient response 57 3.1.3. Analysis on the closed-loop system with proposed controller 60 3.2. Lower level controller 65 3.2.1. Actuator characteristics of in-wheel motors 65 3.2.2. Torque inputs for yaw moment generation 66 Chapter 4. Integrated Control of Two Front In-Wheel Motors (IWMs) and Rear-Axle Electronic Limited Slip Differential (eLSD) 68 4.1. Upper level controller 71 4.1.1. Analysis on actuator characteristics and vehicle responses 71 4.1.2. Feedforward control using in-wheel motors 79 4.1.3. Feedback control using electronic limited slip differential 80 4.2. Lower level controller 82 4.2.1. Transforming the desired yaw moments to the torque command 82 4.2.2. Saturating the torque inputs considering the actuator and tire friction limit 83 4.2.3. Transferring the eLSD clutch torque in the desired direction 84 Chapter 5. Simulation Results 87 5.1. Effect of IWM control on vehicle motion 87 5.2. Effect of IWM/eLSD integrated control 98 Chapter 6. Vehicle Test Results 108 6.1. Test results for IWM control 108 6.2. Test results for integrated control of IWM and eLSD 116 Chapter 7. Conclusion 121 Appendix A. Integrated control of two front in-wheel motors and rear wheel steering 123 A.1. Prediction model for vehicle motion 124 A.2. Controller design 128 A.3. Simulation results 131 Bibliography 138 Abstract in Korean 148박

Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles

To achieve optimal robot behavior in dynamic scenarios we need to consider complex dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to achieve time-optimal driving on low surface, the vehicle should utilize drifting. Hence many authors have devised rules to split circuits and employ drifting on some segments. These rules are suboptimal and do not generalize to arbitrary circuit shapes (e.g., S-like curves). So, the question "When to go into which mode and how to drive in it?" remains unanswered. To choose the suitable mode (discrete decision), the algorithm needs information about the feasibility of the continuous motion in that mode. This makes it a class of Task and Motion Planning (TAMP) problems, which are known to be hard to solve optimally in real-time. In the AI planning community, search methods are commonly used. However, they cannot be directly applied to TAMP problems due to the continuous component. Here, we present a search-based method that effectively solves this problem and efficiently searches in a highly dimensional state space with nonlinear and unstable dynamics. The space of the possible trajectories is explored by sampling different combinations of motion primitives guided by the search. Our approach allows to use multiple locally approximated models to generate motion primitives (e.g., learned models of drifting) and effectively simplify the problem without losing accuracy. The algorithm performance is evaluated in simulated driving on a mixed-track with segments of different curvatures (right and left). Our code is available at https://git.io/JenvBComment: Accepted to the journal Engineering Applications of Artificial Intelligence; 19 pages, 18 figures, code: https://git.io/JenvB. arXiv admin note: text overlap with arXiv:1907.0782

A Human Driver Model for Autonomous Lane Changing in Highways: Predictive Fuzzy Markov Game Driving Strategy

This study presents an integrated hybrid solution to mandatory lane changing problem to deal with accident avoidance by choosing a safe gap in highway driving. To manage this, a comprehensive treatment to a lane change active safety design is proposed from dynamics, control, and decision making aspects. My effort first goes on driver behaviors and relating human reasoning of threat in driving for modeling a decision making strategy. It consists of two main parts; threat assessment in traffic participants, (TV s) states, and decision making. The first part utilizes an complementary threat assessment of TV s, relative to the subject vehicle, SV , by evaluating the traffic quantities. Then I propose a decision strategy, which is based on Markov decision processes (MDPs) that abstract the traffic environment with a set of actions, transition probabilities, and corresponding utility rewards. Further, the interactions of the TV s are employed to set up a real traffic condition by using game theoretic approach. The question to be addressed here is that how an autonomous vehicle optimally interacts with the surrounding vehicles for a gap selection so that more effective performance of the overall traffic flow can be captured. Finding a safe gap is performed via maximizing an objective function among several candidates. A future prediction engine thus is embedded in the design, which simulates and seeks for a solution such that the objective function is maximized at each time step over a horizon. The combined system therefore forms a predictive fuzzy Markov game (FMG) since it is to perform a predictive interactive driving strategy to avoid accidents for a given traffic environment. I show the effect of interactions in decision making process by proposing both cooperative and non-cooperative Markov game strategies for enhanced traffic safety and mobility. This level is called the higher level controller. I further focus on generating a driver controller to complement the automated car’s safe driving. To compute this, model predictive controller (MPC) is utilized. The success of the combined decision process and trajectory generation is evaluated with a set of different traffic scenarios in dSPACE virtual driving environment. Next, I consider designing an active front steering (AFS) and direct yaw moment control (DYC) as the lower level controller that performs a lane change task with enhanced handling performance in the presence of varying front and rear cornering stiffnesses. I propose a new control scheme that integrates active front steering and the direct yaw moment control to enhance the vehicle handling and stability. I obtain the nonlinear tire forces with Pacejka model, and convert the nonlinear tire stiffnesses to parameter space to design a linear parameter varying controller (LPV) for combined AFS and DYC to perform a commanded lane change task. Further, the nonlinear vehicle lateral dynamics is modeled with Takagi-Sugeno (T-S) framework. A state-feedback fuzzy H∞ controller is designed for both stability and tracking reference. Simulation study confirms that the performance of the proposed methods is quite satisfactory

Search-Based Motion Planning for Performance Autonomous Driving

Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference trajectories of dynamic vehicle states with the goal to achieve the minimum lap time on slippery roads. The search-based approach enables to explicitly consider a nonlinear vehicle dynamics model as well as constraints on states and inputs so that even challenging scenarios can be achieved in a safe and optimal way. The algorithm performance is evaluated in simulated driving on a track with segments of different curvatures.Comment: Accepted to IAVSD 201

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

Robust Adaptive Learning-based Path Tracking Control of Autonomous Vehicles under Uncertain Driving Environments

This paper investigates the path tracking control problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached by employing a 2-degree of freedom vehicle model, which is reformulated into a newly defined parametric form with the system uncertainties being lumped into an unknown parametric vector. On top of the parametric system representation, a novel robust adaptive learning control (RALC) approach is then developed, which estimates the system uncertainties through iterative learning while treating the external disturbances by adopting a robust term. It is shown that the proposed approach is able to improve the lateral tracking performance gradually through learning from previous control experiences, despite only partial knowledge of the vehicle dynamics being available. It is noteworthy that a novel technique targeting at the non-square input distribution matrix is employed so as to deal with the under-actuation property of the vehicle dynamics, which extends the adaptive learning control theory from square systems to non-square systems. Moreover, the convergence properties of the RALC algorithm are analysed under the framework of Lyapunov-like theory by virtue of the composite energy function and the λ-norm. The effectiveness of the proposed control scheme is verified by representative simulation examples and comparisons with existing methods

Risk-based autonomous vehicle motion control with considering human driver’s behaviour

The selected motions of autonomous vehicles (AVs) are subject to the constraints from the surrounding traffic environment, infrastructure and the vehicle’s dynamic capabilities. Normally, the motion control of the vehicle is composed of trajectory planning and trajectory following according to the surrounding risk factors, the vehicles’ capabilities as well as tyre/road interaction situations. However, pure trajectory following with a unique path may make the motion control of the vehicle be too careful and cautious with a large amount of steering effort. To follow a planned trajectory, the AVs with the traditional path-following control algorithms will correct their states even if the vehicles have only a slight deviation from the desired path or the vehicle detects static infrastructure like roadside trees. In this case, the safety of the AVs can be guaranteed to some degree, but the comfort and sense of hazards for the drivers are ignored, and sometimes the AVs have unusual motion behaviours which may not be acceptable to other road users. To solve this problem, this study aims to develop a safety corridor-based vehicle motion control approach by investigating human-driven vehicle behaviour and the vehicle’s dynamic capabilities. The safety corridor is derived by the manoeuvring action feedback of actual drivers as collected in a driving simulator when presented with surrounding risk elements and enables the AVs to have safe trajectories within it. A corridor-based Nonlinear Model Predictive Control (NMPC) has been developed which controls the vehicle state to achieve a smooth and comfortable trajectory while applying trajectory constraints using the safety corridor. The safety corridor and motion controller are assessed using four typical scenarios to show that the vehicle has a human-like or human-oriented behaviour which is expected to be more acceptable for both drivers and other road users