48 research outputs found

    Control of Electric Vehicle

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    Practical Coordination of Multi-Vehicle Systems in Formation

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    This thesis considers the cooperation and coordination of multi vehicle systems cohesively in order to keep the formation geometry and provide the string stability. We first present the modeling of aerial and road vehicles representing different motion characteristics suitable for cooperative operations. Then, a set of three dimensional cohesive motion coordination and formation control schemes for teams of autonomous vehicles is proposed. The two main components of these schemes are i) platform free high level online trajectory generation algorithms and ii) individual trajectory tracking controllers. High level algorithms generate the desired trajectories for three dimensional leader-follower structured tight formations, and then distributed controllers provide the individual control of each agent for tracking the desired trajectories. The generic goal of the control scheme is to move the agents while maintaining the formation geometry. We propose a distributed control scheme to solve this problem utilizing the notions of graph rigidity and persistence as well as techniques of virtual target tracking and smooth switching. The distributed control scheme is developed by modeling the agent kinematics as a single-velocity integrator; nevertheless, extension to the cases with simplified kinematic and dynamic models of fixed-wing autonomous aerial vehicles and quadrotors is discussed. The cohesive cooperation in three dimensions is so beneficial for surveillance and reconnaissance activities with optimal geometries, operation security in military activities, more viable with autonomous flying, and future aeronautics aspects, such as fractionated spacecraft and tethered formation flying. We then focus on motion control task modeling for three dimensional agent kinematics and considering parametric uncertainties originated from inertial measurement noise. We design an adaptive controller to perform the three dimensional motion control task, paying attention to the parametric uncertainties, and employing a recently developed immersion and invariance based scheme. Next, the cooperative driving of road vehicles in a platoon and string stability concepts in one-dimensional traffic are discussed. Collaborative driving of commercial vehicles has significant advantages while platooning on highways, including increased road-capacity and reduced traffic congestion in daily traffic. Several companies in the automotive sector have started implementing driver assistance systems and adaptive cruise control (ACC) support, which enables implementation of high level cooperative algorithms with additional softwares and simple electronic modifications. In this context, the cooperative adaptive cruise control approach are discussed for specific urban and highway platooning missions. In addition, we provide details of vehicle parameters, mathematical models of control structures, and experimental tests for the validation of our models. Moreover, the impact of vehicle to vehicle communication in the existence of static road-side units are given. Finally, we propose a set of stability guaranteed controllers for highway platooning missions. Formal problem definition of highway platooning considering constant and velocity dependent spacing strategies, and formal string stability analysis are included. Additionally, we provide the design of novel intervehicle distance based priority coefficient of feed-forward filter for robust platooning. In conclusion, the importance of increasing level of autonomy of single agents and platoon topology is discussed in performing cohesive coordination and collaborative driving missions and in mitigating sensory errors. Simulation and experimental results demonstrate the performance of our cohesive motion and string stable controllers, in addition we discuss application in formation control of autonomous multi-agent systems

    ๊ทนํ•œ ์ฃผํ–‰ ํ•ธ๋“ค๋ง ์„ฑ๋Šฅ ๊ฐœ์„ ์„ ์œ„ํ•œ ํ† ํฌ๋ฒกํ„ฐ๋ง ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๋ถ€, 2023. 2. ์ด๊ฒฝ์ˆ˜.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.๋ณธ ๋…ผ๋ฌธ์€ ์ „๋ฅœ ์ธํœ ๋ชจํ„ฐ์™€ ํ›„๋ฅœ ์ „์ž์‹ ์ฐจ๋™ ์ œํ•œ ์žฅ์น˜๋ฅผ ์ด์šฉํ•˜์—ฌ ์„ ํšŒ ์„ฑ๋Šฅ ๊ฐœ์„ ์„ ์œ„ํ•œ ํ† ํฌ๋ฒกํ„ฐ๋ง ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•ด ํฌ๊ด„์ ์œผ๋กœ ์„ค๋ช…ํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ๋‹ค๋ฃจ๋Š” ์ฃผ์š” ์—ฐ๊ตฌ ๋ฒ”์œ„๋Š” ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ๋ฒ”์ฃผ๋กœ ๋‚˜๋‰  ์ˆ˜ ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์ „๋ฅœ ์ธํœ ๋ชจํ„ฐ๋ฅผ ์ด์šฉํ•œ ๊ฐœ๋ณ„์ ์ธ ํ† ํฌ๋ฒกํ„ฐ๋ง ์ œ์–ด์ด๊ณ , ๋‘ ๋ฒˆ์งธ๋Š” ์ „๋ฅœ ์ธํœ ๋ชจํ„ฐ ๋ฐ ํ›„๋ฅœ ์ „์ž์‹ ์ฐจ๋™์ œํ•œ์žฅ์น˜๋ฅผ ๋ชจ๋‘ ์ด์šฉํ•œ ์ „ํ›„๋ฅœ ํ†ตํ•ฉ ํ† ํฌ๋ฒกํ„ฐ๋ง ์ œ์–ด์ด๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, ํ›„๋ฅœ ๊ตฌ๋™ ์ฐจ๋Ÿ‰ ๋‚ด์—์„œ ๋‘ ๊ฐœ์˜ ์ „๋ฅœ ์ธํœ  ๋ชจํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ์„ ํšŒ ์„ฑ๋Šฅ ๊ฐœ์„ ์„ ์œ„ํ•œ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ธํœ  ๋ชจํ„ฐ ๋…๋ฆฝ ์ œ์–ด๋Š” ์ •์ƒ์ƒํƒœ ์ œ์–ด ์ž…๋ ฅ๊ณผ ๊ณผ๋„์‘๋‹ต ์ƒํƒœ ์ œ์–ด ์ž…๋ ฅ์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ์ •์ƒ์ƒํƒœ ์ œ์–ด ์ž…๋ ฅ์€ ์ฐจ๋Ÿ‰์˜ ์–ธ๋”์Šคํ‹ฐ์–ด ๊ตฌ๋ฐฐ๋ฅผ ๋ณ€ํ˜•ํ•˜๋ฉด์„œ ์ •์ƒ์ƒํƒœ ์„ ํšŒ ๋ฐ˜์‘์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ๊ณ ์•ˆ๋˜์—ˆ๊ณ , ๊ณผ๋„์‘๋‹ต ์ƒํƒœ ์ œ์–ด ์ž…๋ ฅ์€ ์ฐจ๋Ÿ‰์˜ ์š”๋Œํ•‘ ๊ณ„์ˆ˜๋ฅผ ์ฆ๊ฐ€์‹œํ‚ด์œผ๋กœ์จ ์ฐจ๋Ÿ‰์˜ ํšก๋ฐฉํ–ฅ ์•ˆ์ •์„ฑ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์€ ์ปดํ“จํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์ฐจ๋Ÿ‰ ์‹คํ—˜์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ์—์„œ ์•Œ ์ˆ˜ ์žˆ๋“ฏ์ด, ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ œ์–ด ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๋ฉฐ ์ฐจ๋Ÿ‰์˜ ์„ ํšŒ ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ๊ฐ ์—‘์ธ„์—์ดํ„ฐ์˜ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜๊ณ  ๊ณ ์† ์ฃผํ–‰ ์ƒํ™ฉ์—์„œ์˜ ์„ ํšŒ ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด, ๋‘ ๊ฐœ์˜ ์ „๋ฅœ ์ธํœ  ๋ชจํ„ฐ์™€ ํ›„๋ฅœ์˜ ์ „์ž์‹ ์ฐจ๋™ ์ œํ•œ ์žฅ์น˜์˜ ํ†ตํ•ฉ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์„ค๊ณ„๋˜์—ˆ๋‹ค. ๋‘ ๊ฐœ์˜ ์ „๋ฅœ ์ธํœ  ๋ชจํ„ฐ๋Š” ํ”ผ๋“œํฌ์›Œ๋“œ ์ œ์–ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ ํšŒ ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ์ œ์–ด๋˜์—ˆ๊ณ , ํ›„๋ฅœ์˜ ์ „์ž์‹ ์ฐจ๋™ ์ œํ•œ ์žฅ์น˜๋Š” ์š”๋ ˆ์ดํŠธ ํ”ผ๋“œ๋ฐฑ ์ œ์–ด๋ฅผ ์œ„ํ•ด ํ™œ์šฉ๋˜์—ˆ๋‹ค. ์ปดํ“จํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ๊ฐ๊ฐ€์†์„ ํฌํ•จํ•œ ๊ณต๊ฒฉ์ ์ธ ์„ ํšŒ ์ƒํ™ฉ์—์„œ ๊ฐ ์—‘์ธ„์—์ดํ„ฐ์˜ ์ œ์–ด ํšจ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ๊ธฐ ์œ„ํ•ด ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, ์ฐจ๋Ÿ‰ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆ๋œ ์ œ์–ด๊ธฐ๊ฐ€ ์ œ์–ด๋˜์ง€ ์•Š์€ ๊ฒฝ์šฐ์— ๋น„ํ•ด ํ•ธ๋“ค๋ง ํ•œ๊ณ„ ์ƒํ™ฉ์—์„œ์˜ ์„ ํšŒ ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์š”์•ฝํ•˜์ž๋ฉด, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฐจ๋Ÿ‰์˜ ์–ธ๋”์Šคํ‹ฐ์–ด ๊ทธ๋ ˆ๋””์–ธํŠธ์™€ ์š”๋ ˆ์ดํŠธ ๋Œํ•‘ ํŠน์„ฑ์— ๊ธฐ๋ฐ˜ํ•œ ํ•œ๊ณ„ ํ•ธ๋“ค๋ง ์„ฑ๋Šฅ ๊ฐœ์„ ์„ ์œ„ํ•œ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ธํœ ๋ชจํ„ฐ์™€ ์ „์ž์‹ ์ฐจ๋™ ์ œํ•œ ์žฅ์น˜์˜ ๊ฐ ์—‘์ธ„์—์ดํ„ฐ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ธํœ ๋ชจํ„ฐ์™€ ์ „์ž์‹ ์ฐจ๋™ ์ œํ•œ ์žฅ์น˜์˜ ํ†ตํ•ฉ ์ œ์–ด์— ๋Œ€ํ•ด ๋‹ค๋ฃจ์—ˆ๋‹ค. ์ œ์•ˆ๋œ ์ธํœ ๋ชจํ„ฐ ์ œ์–ด๊ธฐ๋Š” ์ •์ƒ์ƒํƒœ ์„ ํšŒ์—์„œ์˜ ์–ธ๋”์Šคํ‹ฐ์–ด ๊ทธ๋ ˆ๋””์–ธํŠธ์™€ ๊ณผ๋„์‘๋‹ต์ƒํƒœ ์„ ํšŒ์—์„œ์˜ ์š”๋ ˆ์ดํŠธ ๋Œํ•‘ ํŠน์„ฑ์„ ๋ณ€ํ˜•ํ•˜๊ธฐ ์œ„ํ•ด ๊ณ ์•ˆ๋˜์—ˆ๊ณ , ์ „์ž์‹ ์ฐจ๋™ ์ œํ•œ ์žฅ์น˜ ์ œ์–ด๋Š” ๋ชฉํ‘œ ์š”๋ ˆ์ดํŠธ๋ฅผ ์ถ”์ข…ํ•˜๊ธฐ ์œ„ํ•ด ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ œ์•ˆ๋œ ์ œ์–ด๊ธฐ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด, ์ปดํ“จํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹ค์ฐจ ์‹คํ—˜์ด ์ง„ํ–‰๋˜์—ˆ๊ณ , ์ฐจ๋Ÿ‰์˜ ์„ ํšŒ ์•ˆ์ •์„ฑ๊ณผ ๋ฏผ์ฒฉ์„ฑ์ด ์ฑ„ํ„ฐ๋ง ๋ฌธ์ œ์—†์ด ํ™•์—ฐํžˆ ๊ฐœ์„ ๋œ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, ๋ ˆ์ด์‹ฑ ํŠธ๋ž™์—์„œ์˜ ์‹ค์ฐจ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฐœ์„ ๋œ ํ•œ๊ณ„ ํ•ธ๋“ค๋ง ์„ฑ๋Šฅ ๋˜ํ•œ ์ œ์‹œ๋˜์—ˆ๋‹ค.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 148๋ฐ•

    Enhanced active front steering control using sliding mode control under varying road surface condition

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    In vehicle lateral dynamic control, the handling quality or steering ability of the vehicle is determined by the yaw rate response performances. The uncertainty of tire cornering stiffness due to varying tire-road adhesion coefficient, u caused by road surfaces perturbation during cornering manoeuvre may influence the transient performances of yaw rate response. Therefore, in this research, the enhanced control law of robust yaw rate tracking controller using the Sliding Mode Control (SMC) algorithm is proposed for active front steering (AFS) control strategy to improve the yaw rate response as desired. The vehicle lateral dynamics behaviors are described using the linear and nonlinear vehicle models. The linear 2 degree-of-freedom (DOF) single track model is used for controller design while the nonlinear 7 DOF two-track model is used for simulation and controller evaluations. The sliding surface of SMC is design based on yaw rate tracking error information. The control law equation is enhanced by integrating the uncertainty of cornering stiffness at the front wheels and to ensure the controller stability, the Lyapunov stability theory is applied. The transient performances and performance indices of AFS control responses are evaluated using the step steer and single lane change cornering manoeuvres test for varying values of u at dry, wet and snow or icy road surfaces. The simulations results demonstrated that the proposed enhanced control law using SMC is able to track the reference yaw rate with similar transient response performances. The proposed enhanced control law also provided low performance indices of ITAE and IAE compared to the conventional control law using SMC and robust CNF control for lower value of u at wet and snow or icy road surface. In terms of percentage of differential performance indices, the proposed control law has a better tracking ability of up to 58.45% compared to two other control laws. Therefore, this research concluded that the proposed enhanced control law using SMC has overcome the cornering stiffness uncertainty in AFS control strategy for different road surfaces during cornering manoeuvre and this enhancement is expected as a knowledge contribution to vehicle lateral dynamic study

    Advances in Intelligent Vehicle Control

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    This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

    Multi-Actuated Vehicle Control and Path Planning/Tracking at Handling Limits

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    The increasing requirements for vehicle safety along with the impressive progress in vehicle actuation technologies have motivated manufacturers to equip vehicles with multiple control actuations that enhance handling and stability. Moreover, multiple control objectives arise in vehicle dynamics control problems, such as yaw rate control and rollover prevention, therefore, vehicle control problems can be defined as multi-actuation multi-objective vehicle control problems. Recently, the importance of integrating vehicle control systems has been highlighted in the literature. This integration allows us to prevent the potential conflicting control commands that could be generated by individual controllers. Existing studies on multi-actuated vehicle control offer a coordinated control design that shares the required control effort between the actuations. However, they mostly lack an appropriate strategy for considering the differences among vehicle actuations in their energy usage, capabilities, and effectiveness in any given vehicle states. Therefore, it is very important to develop a cost-performance strategy for optimally controlling multi-actuated vehicles. In this thesis, a prioritization model predictive control design is proposed for multi-actuated vehicles with multiple control objectives. The designed controller prioritizes the control actuations and control objectives based on, respectively, their advantages and their importance, and then combines the priorities such that a low priority actuation will not kick in unless a high priority objective demands it. The proposed controller is employed for several actuations, including electronic limited slip differential (ELSD), front/rear torque shifting, and differential braking. In this design, differential braking is engaged only when it is necessary, thus limiting or avoiding its disadvantages such as speed reduction and maintenance. In addition, the proposed control design includes a detailed analysis of the above-mentioned actuations in terms of modelling, control, and constraints. A new vehicle prediction model is designed for integrated lateral and roll dynamics that considers the force coupling effect and allows for the optimal control of front/rear torque distribution. The existing methods for ELSD control may result in chattering or unwanted oversteering yaw moments. To resolve this problem, a dynamic model is first designed for the ELSD clutch to properly estimate the clutch torque. This ELSD model is then used to design an intelligent ELSD controller that resolves the issues mentioned above. Experimental tests with two different vehicles are also carried out to evaluate the performance of the prioritization MPC controller in real-time. The results verify the capability of the controller in properly activating the control actuations with the designed priorities to improve vehicle handling and stability in different driving maneuvers. In addition, the test results confirm the performance of the designed ELSD model in ELSD clutch torque estimation and in enabling the controller to prevent unwanted oversteering yaw moments. The designed stability controller is extended to use for emergency collision avoidance in autonomous vehicles. This extension in fact addresses a local path planning/tracking problem with control objectives prioritized as: 1) collision avoidance, 2) vehicle stability, and 3) tracking the desired path. The controller combines a conservative form of torque/brake vectoring with front steering to improve the lateral agility and responsiveness of the vehicle in emergency collision avoidance scenarios. In addition, a contingency MPC controller is designed with two parallel prediction horizons - a nominal horizon and a contingency horizon - to maintain avoidance in identified road condition uncertainties. The performance of the model predictive controllers is evaluated in software simulations with high fidelity CarSim models, in which different sets of actuation configurations in various driving and road conditions are assessed. In addition, the effectiveness of the local path planning/tracking controller is evaluated in several emergency and contingency collision avoidance scenarios

    Modeling and control of actuators and co-surge in turbocharged engines

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    Optimal torque vectoring control strategies for stabilisation of electric vehicles at the limits of handling

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    The study of chassis control has been a major research area in the automotive industry and academia for more than fifty years now. Among the popular methods used to actively control the dynamics of a vehicle, torque vectoring, the method of controlling both the direction and the magnitude of the torque on the wheels, is of particular interest. Such a method can alter the vehicleโ€™s behaviour in a positive way under both sub-limit and limit handling conditions and has become even more relevant in the case of an electric vehicle equipped with multiple electric motors. Torque vectoring has been so far employed mainly in lateral vehicle dynamics control applications, with the longitudinal dynamics of the vehicle remaining under the full authority of the driver. Nevertheless, it has been also recognised that active control of the longitudinal dynamics of the vehicle can improve vehicle stability in limit handling situations. A characteristic example of this is the case where the driver misjudges the entry speed into a corner and the vehicle starts to deviate from its path, a situation commonly referred to as a โ€˜terminal understeerโ€™ condition. Use of combined longitudinal and lateral control in such scenarios have been already proposed in the literature, but these solutions are mainly based on heuristic approaches that also neglect the strong coupling of longitudinal and lateral dynamics in limit handling situations. The main aim of this project is to develop a real-time implementable multivariable control strategy to stabilise the vehicle at the limits of handling in an optimal way using torque vectoring via the two independently controlled electric motors on the rear axle of an electric vehicle. To this end, after reviewing the most important contributions in the control of lateral and/or longitudinal vehicle dynamics with a particular focus on the limit handling solutions, a realistic vehicle reference behaviour near the limit of lateral acceleration is derived. An unconstrained optimal control strategy is then developed for terminal understeer mitigation. The importance of constraining both the vehicle state and the control inputs when the vehicle operates at the limits of handling is shown by developing a constrained linear optimal control framework, while the effect of using a constrained nonlinear optimal control framework instead is subsequently examined next. Finally an optimal estimation strategy for providing the necessary vehicle state information to the proposed optimal control strategies is constructed, assuming that only common vehicle sensors are available. All the developed optimal control strategies are assessed not only in terms of performance but also execution time, so to make sure they are implementable in real time on a typical Electronic Control Unit
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