Driver torque estimation in Electric Power Steering system using an H ∞ /H 2 Proportional Integral Observer

International audienceThis paper deals with the design of a Proportional Integral (PI) observer to estimate the driver torque in an Electric Power Steering (EPS) system. The PI observer is obtained by solving a multi-objective optimization problem: it should both be barely sensitive to road disturbances and sensor noise, and converge swiftly. The performance of the proposed observer is illustrated by simulation results using experimental data

Design of an Unknown Input Observer to Enhance Driver Experience of Electric Power Steering Systems

Electric power steering (EPS) systems assist the driver during manoeuvres by applying an additional steering torque generated by an electric motor. Although there are many advantages for electric actuated steering systems including fuel efficiency, they are known to deteriorate the feel of the steering as experienced by the driver. This paper presents a sliding mode observer based estimation concept which provides signals to evaluate and improve perception and feel of the steering as experienced by the driver. The proposed strategy is based on a physically motivated dynamic model of a power steering system and the measurements considered are typically available in any modern vehicle. The performance of the estimator is investigated using numerical simulation as well as experimental results obtained using a laboratory steering testbed

Neural Network Based Approach for the Generation of Road Feel in a Steer-By-Wire System

Steer-by-wire is an advanced steering system which connects the steering wheel with the front wheel by using motors and sensors. Generating the road feel in steer by wire system is an important criterion since there is no mechanical connection between the steering wheel and the front wheel. In present work, Neural Network method is proposed for generating artificial road feel to the driver using the vehicle dynamic parameters such as vertical displacement, self-aligning moment and front wheel angle as inputs. Proposed neural network model was trained using the vehicle dynamic models for estimating the current to be supplied to the feedback motor according to the changing road conditions. Three different road profiles are selected such as dry, wet and icy for the simulation purpose and the estimated motor current values for the road surfaces using neural network are presented. From the simulation results for the sinusoidal road surface and sinusoidal steering angle driver input, it is clear that the neural network based method is able to produce the varying road feel to the driver for the different road conditions

스티어 바이 와이어 시스템의 목표 조향감 재현을 위한 조향 반력 제어

학위논문(박사)--서울대학교 대학원 :공과대학 기계공학부,2020. 2. 이경수.This dissertation focused on the development of and steering assist torque control algorithm of Electric-Power-Steering (EPS) system from the conventional steering system perspective and Steer-by-Wire (SBW) system. The steering assist torque control algorithm has been developed to overcome the major disadvantage of the conventional method of time-consuming tuning to achieve the desired steering feel. A reference steering wheel torque map was designed by post-processing data obtained from target performance vehicle tests with a highly-rated steering feel for both sinusoidal and transition steering inputs. Adaptive sliding-mode control was adopted to ensure robustness against uncertainty in the steering system, and the equivalent moment of inertia damping coefficient and effective compliance were adapted to improve tracking performance. Effective compliance played a role in compensating the error between the nominal rack force and the actual rack force. For the SBW system, the previously proposed EPS assist torque algorithm has been also enhanced using impedance model and applied to steering feedback system. Stable execution and how to give the person the proper steering feedback torque of contact tasks by steering wheel system interaction with human has been identified as one of the major challenges in SBW system. Thus, the problem was solved by utilizing the target steering torque map proposed above. The impedance control consists of impedance model (Reference model with the target steering wheel torque map) and controller (Adaptive sliding mode control). The performance of the proposed controller was evaluated by conducting computer simulations and a hardware-in-the-loop simulation (HILS) under various steering conditions. Optimal steering wheel torque tracking performances were successfully achieved by the proposed EPS and SBW control algorithm.본 논문은 종래의 조향 시스템 관점에서 전동식 동력 조향 (EPS) 시스템과 스티어 바이 와이어 (SBW) 조향 보조 토크 제어 알고리즘의 개발을 중점으로 하였습니다. 기존 조향 보조 토크 제어 알고리즘은 원하는 조향감을 구현하기 위해 종래의 시간 소모적 인 튜닝 방법을 사용합니다. 이러한 주요 단점을 극복하기 위해 새로운 조향 보조 제어 알고리즘을 개발하였습니다. 목표 스티어링 휠 토크 맵은 정현파(Weave test) 및 등속도 스티어링 입력 (Transition test) 모두에 대해 높은 등급의 조향감을 차량 테스트에서 얻은 후 데이터 처리를 하여 설계되었습니다. 스티어링 시스템의 불확실성에 대한 강건성을 보장하기 위해 적응 형 슬라이딩 모드 제어가 채택되었으며, 관성 모멘트 감쇠 계수와 컴플라이언스 계수(Effective compliance)가 제어기 성능을 개선하도록 적응형 파라미터로 선정되었습니다. 컴플라이언스 계수는 계산된 랙 힘과 실제 랙 힘 사이의 차이를 보상하는 역할을 했습니다. SBW 시스템의 경우, 이전에 제안 된 EPS 지원 토크 알고리즘을 개선하고 향상시키기 위해 임피던스 모델을 사용하였으며 스티어링 피드백 시스템에 적용되었습니다. SBW 시스템의 주요 과제 중 하나는 사람과 스티어링 휠 시스템 상호 작용에 의해 안정적인 작동과 사람에게 적절한 스티어링 피드백 토크를 제공하는 방법입니다. 임피던스 제어는 임피던스 모델 (타겟 스티어링 휠 토크 맵)과 컨트롤러 (적응 슬라이딩 모드 제어)로 구성됩니다. 따라서, 상기 제안 된 목표 조향 토크 맵을 이용함으로써 스티어 바이 와이어에서 스티어링 피드백 토크를 절절히 적용 됨을 확인 하였습니다. 제안 된 컨트롤러의 성능은 다양한 조향 조건에서 컴퓨터 시뮬레이션 및 HILS (Hardware-in-the-loop) 시뮬레이션을 수행하여 평가되었습니다. 제안 된 EPS 및 SBW 제어 알고리즘을 통해 최적의 스티어링 휠 토크 추적 성능을 달성했습니다.Chapter 1 Introduction 1 1.1. Background and Motivation 1 1.2. Previous Researches 4 1.3. Thesis Objectives 9 1.4. Thesis Outline 10 Chapter 2 Dynamic Model of Steering Systems 11 2.1. Dynamic model of Hydraulic/Electrohydraulic Power-Assisted Steering Model 11 2.2. Dynamic model of Electric-Power-Assisted-Steering Model 17 2.3. Dynamic model of Steer-by-Wire Model 21 2.4. Rack force characteristic of steering system 23 Chapter 3 Target steering wheel torque tracking control 28 3.1. Target steering torque map generation 28 3.2. Adaptive sliding mode control design for target steering wheel torque tracking with EPS 30 3.2.1. Steering states estimation with a kalman filter 38 3.3. Impedance Control Design for Target Steering Wheel Torque Tracking with SBW 43 Chapter 4 Validation with Simulation and Hardware-in-the-Loops Simulation 49 4.1. Computer Simulation Results for EPS system 49 4.2. Hardware-in-the-Loops Simulation Results for EPS system 61 4.3. Computer Simulation Results for SBW system 77 4.4. Hardware-in-the-Loops Simulation Results for SBW system 82 Chapter 5 Conclusion and Future works 89 Bibliography 91 Abstract in Korean 97Docto

Dynamics and Model-Based Control of Electric Power Steering Systems

Many automobile manufacturers are switching to Electric Power Steering (EPS) systems for their better performance and cost advantages over traditional Hydraulic Power Steering (HPS) systems. EPS compared to HPS offer lower energy consumption, lower total weight, and package flexibility at no cost penalty. Furthermore, since EPS systems can provide assistance to drivers independent of the vehicle driving conditions, new technologies can be implemented to improve the steering feel and safety, simultaneously. In this thesis, a neuromusculoskeletal driver and a high-fidelity vehicle model are developed in MapleSim to provide realistic simulations to study the driver-vehicle interactions and EPS systems. The vehicle model consists of MacPherson and multilink suspensions at front and rear equipped with a column-type EPS system. The driver model is a fully neuromusculoskeletal model of a driver arm holding the steering wheel, controlled by the driver's central nervous system. A hierarchical approach is used to capture the complexity of the neuromuscular dynamics and the central nervous system in the coordination of the driver's upper extremity activities. The proposed motor control framework has three layers: the first layer, or the path-planning layer, plans a desired vehicle trajectory and the required steering angles to perform the desired trajectory, the second layer (or the force distribution controller) actuates the musculoskeletal arm, and the final layer is added to ensure the precision control and disturbance rejection of the motor control units. The overall goal of this thesis is to study vehicle-driver interactions and to design a model-based EPS controller that considers the driver's characteristics. To design such an EPS controller, the high-fidelity driver-vehicle model is simplified to reduce the computational burden associated with the multibody and biomechanical systems. Then, four driver types are introduced based on the physical characteristics of drivers such as age and gender, and the corresponding parameters are incorporated in the model. Last but not least, a new model-based EPS controller is developed to provide appropriate assistance to each of the predefined driver types. To do this, the characteristic curves are tuned using a systematic optimization procedure to provide appropriate assistance to drivers with different physical strength, in order to have a similar road and steering feel. In this thesis, it is recommended that muscle fatigue be used as a measure of steering feel. Then, based on the tuned EPS characteristic curves, an observer-based optimal disturbance rejection controller, consisting of a linear quadratic regulator controller and a Kalman filter observer augmented with a shaping filter, is developed to deliver the assistance while attenuating external disturbances. The results show that it is possible to develop a model-based EPS controller that is optimized for a given driver population

Steering control for haptic feedback and active safety functions

Steering feedback is an important element that defines driver–vehicle interaction. It strongly affects driving performance and is primarily dependent on the steering actuator\u27s control strategy. Typically, the control method is open loop, that is without any reference tracking; and its drawbacks are hardware dependent steering feedback response and attenuated driver–environment transparency. This thesis investigates a closed-loop control method for electric power assisted steering and steer-by-wire systems. The advantages of this method, compared to open loop, are better hardware impedance compensation, system independent response, explicit transparency control and direct interface to active safety functions.The closed-loop architecture, outlined in this thesis, includes a reference model, a feedback controller and a disturbance observer. The feedback controller forms the inner loop and it ensures: reference tracking, hardware impedance compensation and robustness against the coupling uncertainties. Two different causalities are studied: torque and position control. The two are objectively compared from the perspective of (uncoupled and coupled) stability, tracking performance, robustness, and transparency.The reference model forms the outer loop and defines a torque or position reference variable, depending on the causality. Different haptic feedback functions are implemented to control the following parameters: inertia, damping, Coulomb friction and transparency. Transparency control in this application is particularly novel, which is sequentially achieved. For non-transparent steering feedback, an environment model is developed such that the reference variable is a function of virtual dynamics. Consequently, the driver–steering interaction is independent from the actual environment. Whereas, for the driver–environment transparency, the environment interaction is estimated using an observer; and then the estimated signal is fed back to the reference model. Furthermore, an optimization-based transparency algorithm is proposed. This renders the closed-loop system transparent in case of environmental uncertainty, even if the initial condition is non-transparent.The steering related active safety functions can be directly realized using the closed-loop steering feedback controller. This implies, but is not limited to, an angle overlay from the vehicle motion control functions and a torque overlay from the haptic support functions.Throughout the thesis, both experimental and the theoretical findings are corroborated. This includes a real-time implementation of the torque and position control strategies. In general, it can be concluded that position control lacks performance and robustness due to high and/or varying system inertia. Though the problem is somewhat mitigated by a robust H-infinity controller, the high frequency haptic performance remains compromised. Whereas, the required objectives are simultaneously achieved using a torque controller

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies

New trends in electrical vehicle powertrains

The electric vehicle and plug-in hybrid electric vehicle play a fundamental role in the forthcoming new paradigms of mobility and energy models. The electrification of the transport sector would lead to advantages in terms of energy efficiency and reduction of greenhouse gas emissions, but would also be a great opportunity for the introduction of renewable sources in the electricity sector. The chapters in this book show a diversity of current and new developments in the electrification of the transport sector seen from the electric vehicle point of view: first, the related technologies with design, control and supervision, second, the powertrain electric motor efficiency and reliability and, third, the deployment issues regarding renewable sources integration and charging facilities. This is precisely the purpose of this book, that is, to contribute to the literature about current research and development activities related to new trends in electric vehicle power trains.Peer ReviewedPostprint (author's final draft

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Nonlinear Modeling and Control of Driving Interfaces and Continuum Robots for System Performance Gains

With the rise of (semi)autonomous vehicles and continuum robotics technology and applications, there has been an increasing interest in controller and haptic interface designs. The presence of nonlinearities in the vehicle dynamics is the main challenge in the selection of control algorithms for real-time regulation and tracking of (semi)autonomous vehicles. Moreover, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics plus the soft and flexible nature of the manipulator body. The trajectory tracking and control of automobile and robotic systems requires control algorithms that can effectively deal with the nonlinearities of the system without the need for approximation, modeling uncertainties, and input disturbances. Control strategies based on a linearized model are often inadequate in meeting precise performance requirements. To cope with these challenges, one must consider nonlinear techniques. Nonlinear control systems provide tools and methodologies for enabling the design and realization of (semi)autonomous vehicle and continuum robots with extended specifications based on the operational mission profiles. This dissertation provides an insight into various nonlinear controllers developed for (semi)autonomous vehicles and continuum robots as a guideline for future applications in the automobile and soft robotics field. A comprehensive assessment of the approaches and control strategies, as well as insight into the future areas of research in this field, are presented.First, two vehicle haptic interfaces, including a robotic grip and a joystick, both of which are accompanied by nonlinear sliding mode control, have been developed and studied on a steer-by-wire platform integrated with a virtual reality driving environment. An operator-in-the-loop evaluation that included 30 human test subjects was used to investigate these haptic steering interfaces over a prescribed series of driving maneuvers through real time data logging and post-test questionnaires. A conventional steering wheel with a robust sliding mode controller was used for all the driving events for comparison. Test subjects operated these interfaces for a given track comprised of a double lane-change maneuver and a country road driving event. Subjective and objective results demonstrate that the driver’s experience can be enhanced up to 75.3% with a robotic steering input when compared to the traditional steering wheel during extreme maneuvers such as high-speed driving and sharp turn (e.g., hairpin turn) passing. Second, a cellphone-inspired portable human-machine-interface (HMI) that incorporated the directional control of the vehicle as well as the brake and throttle functionality into a single holistic device will be presented. A nonlinear adaptive control technique and an optimal control approach based on driver intent were also proposed to accompany the mechatronic system for combined longitudinal and lateral vehicle guidance. Assisting the disabled drivers by excluding extensive arm and leg movements ergonomically, the device has been tested in a driving simulator platform. Human test subjects evaluated the mechatronic system with various control configurations through obstacle avoidance and city road driving test, and a conventional set of steering wheel and pedals were also utilized for comparison. Subjective and objective results from the tests demonstrate that the mobile driving interface with the proposed control scheme can enhance the driver’s performance by up to 55.8% when compared to the traditional driving system during aggressive maneuvers. The system’s superior performance during certain vehicle maneuvers and approval received from the participants demonstrated its potential as an alternative driving adaptation for disabled drivers. Third, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which inverse kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is validated in a realistic simulation and implemented on a six degree-of-freedom two-section OctArm continuum manipulator. Both simulation and experimental results show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent tracking performance (e.g. steady state arc length and curvature tracking error of 3.3mm and 130mm-1, respectively). Last, semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. The proposed holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer vehicle operations