Controladores borrosos para la dirección de vehículos autónomos en maniobras dentro de entornos urbanos

National audienceHasta la fecha, los sistemas de ayuda a la conducción desarrollados en el sector de la automoción se centran especialmente en el control de velocidad del vehículo. Sin embargo, sistemas que involucren el control (ya sea parcial o total) sobre la dirección del vehículo se encuentran todavía en fase experimental. Este trabajo está centrado en el diseño, desarrollo e implementación de un sistema de control lateral en cascada para vehículos autónomos reales, basado en controladores borrosos de alto nivel para maniobras en circuitos urbanos. Diferentes experimentos se han llevado a cabo en curvas de distinto radio y a diferentes velocidades (dentro de entornos urbanos), además, se han implementado dos nuevas maniobras: la marcha atrás y conducción en rotondas, mostrando un buen funcionamiento

LMI-based H∞ controller of vehicle roll stability control systems with input and output delays

This article belongs to the Section Physical Sensors.Many of the current research works are focused on the development of different control systems for commercial vehicles in order to reduce the incidence of risky driving situations, while also improving stability and comfort. Some works are focused on developing low-cost embedded systems with enough accuracy, reliability, and processing time. Previous research works have analyzed the integration of low-cost sensors in vehicles. These works demonstrated the feasibility of using these systems, although they indicate that this type of low-cost kit could present relevant delays and noise that must be compensated to improve the performance of the device. For this purpose, it is necessary design controllers for systems with input and output delays. The novelty of this work is the development of an LMI-Based H∞ output-feedback controller that takes into account the effect of delays in the network, both on the sensor side and the actuator side, on RSC (Roll Stability Control) systems. The controller is based on an active suspension with input and output delays, where the anti-roll moment is used as a control input and the roll rate as measured data, both with delays. This controller was compared with a controller system with a no-delay consideration that was experiencing similar delays. The comparison was made through simulation tests with a validated vehicle on the TruckSim® software.This work was supported by the FEDER/Ministry of Science and Innovation-Agencia Estatal de Investigacion (AEI) of the Government of Spain through the project [RTI2018-095143-B-C21]

An Intelligent Predictive Algorithm for the Anti-Rollover Prevention of Heavy Vehicles for Off-Road Applications

Rollover detection and prevention are among the most critical aspects affecting the stability and safety assessment of heavy vehicles, especially for off-road driving applications. This topic has been studied in the past and analyzed in depth in terms of vehicle modelling and control algorithms design able to prevent the rollover risk. However, it still represents a serious problem for automotive carmakers due to the huge counts among the main causes for traffic accidents. The risk also becomes more challenging to predict for off-road heavy vehicles, for which the incipient rollover might be triggered by external factors, i.e., road irregularities, bank angles as well as by aggressive input from the driver. The recent advances in road profile measurement and estimation systems make road-preview-based algorithms a viable solution for the rollover detection. This paper describes a model-based formulation to analytically evaluate the load transfer dynamics and its variation due to the presence of road perturbations, i.e., road bank angle and irregularities. An algorithm to detect and predict the rollover risk for heavy vehicles is also presented, even in presence of irregular road profiles, with the calculation of the ISO-LTR Predictive Time through the Phase-Plane analysis. Furthermore, the artificial intelligence techniques, based on the recurrent neural network approach, is also presented as a preliminary solution for a realistic implementation of the methodology. The paper finally assess the efficacy of the proposed rollover predictive algorithm by providing numerical results from the simulation of the most severe maneuvers in realistic off-road driving scenarios, also demonstrating its promising predictive capabilities

Real-time lateral stability and steering characteristic control using non-linear model predictive control

This paper presents a non-linear integrated control strategy that primarily focuses maintaining vehicle lateral stability using active front steering and differential braking. The proposed control strategy utilises a non-linear model predictive controller to improve lateral stability. A stable linear reference model is used for reference generation. By including the understeer gradient in the reference model, different kinematic responses are obtained from the controlled vehicle. The prediction model utilises the road friction estimate to create dynamic stability constraints that include rollover and sliding of the vehicle. The design of the model predictive controller allows easy activation of different control actuators and dynamic modification to the control behaviour. The control methodology is validated using MATLAB/Simulink and a validated MSC ADAMS model. A sensitivity analysis is conducted to identify the susceptibility of the control strategy to various parameters and states.https://www.tandfonline.com/loi/nvsd20hj2023Mechanical and Aeronautical Engineerin

A Model-free Approach to Vehicle Stability Control

This project explored the feasibility of using measured responses of a passenger car together with a fuzzy logic based control algorithm to sense the onset of under-steer (or loss of steering control) and mitigate or eliminate it. The controller is simple and robust and, unlike existing controllers, instead of comparing the vehicle response to that of an idealized model it makes decisions based solely upon the measured response of the car. Simulations were conducted (using CarSim) of various vehicles executing the skid pad and the double lane change tests to characterize the vehicle behavior. Consistent and qualitatively similar patterns in vehicle response during the inception of and at limit under-steer were observed. A fuzzy logic routine was developed that analyzes real-time measurements of steering wheel angle (SWA) and lateral acceleration (Ay). Based on the relative `trends\u27 of the signals, the control algorithm decides upon the presence and extent of under-steer in the vehicle. The degree of under-steer then defines the corrective action. The fundamental concept is to measure a drop in the instantaneous lateral acceleration gain, i.e., Ay/SWA, indicating a lack of response. It is quantified as a normalized error and transformed into an under-steer number between 0 and 10 using a pair of fuzzy inference systems. Once incipient under-steer is detected, the brakes and engine throttle are managed to limit the lateral deviation from the travel lane. The controller also senses vehicle velocity, master cylinder brake pressure and normalized throttle input to improve controllability. This approach eliminates the need for either a simple or a complex vehicle model and the associated dependence on the model parameters. Controller performance was validated using a braking-in-turn maneuver developed by the author and the standard double lane change maneuver. The results have shown clear improvement in the tracking ability of a vehicle. The simulations were conducted at different speeds with each of several vehicles and with different tire-to-ground friction values without any changes to the control algorithm. This has shown that the controller is robust across different conditions. The controller is successful in increasing the maximum safe speed for a negotiating a curve for all vehicles on various road conditions. The last part of the controller was to combine it with an existing over-steer controller, developed at Clemson University, which also uses fuzzy logic. This was successfully completed to obtain a fully functional ESC system, independent of a vehicle model. Future work will include tuning the controller based on track data from real vehicles

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

직렬형 하이브리드 기반 6륜 인휠 차량의 최적 주행성, 안정성 및 에너지 효율을 위한 주행제어 알고리즘 개발

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 자동차공학전공, 2012. 8. 이경수.본 논문은 직렬형 하이브리드 기반 6륜 인휠차량의 최적 주행성, 안정성 및 에너지 효율을 위한 주행제어 알고리즘 개발에 대하여 서술하였다. 대상 차량은 구동, 제동 및 조향이 독립적으로 가능한 시스템으로 구성되어 있다. 통합 주행제어 알고리즘은 6WD/6WS 차량의 최적 안정성, 주행성 및 에너지 효율을 위해 개발되었습니다. 제안된 알고리즘은 목표 동역학, 상위 제어, 하위 제어, 동력관리 계층을 포함하여 크게 4부분으로 구성되어 있습니다. 목표 동역학 계층은 운전자의 조향, 구동 및 제동 입력을 통해 각 휠의 조향각과 목표 속도 및 제동량을 결정합니다. 안정성 판단/제어, 요 모멘트 제어 및 속도 제어는 상위 제어기에 포함되어 있다. 안정성 판단/제어는 차량의 안정성을 판단하여 횡안정성 및 전복 안정성을 확보하기 위하여 G-vectoring과 요 모멘트 제어를 실시한다. 요 모멘트 제어는 요 안정성을 확보하기 위해 목표 요 속도를 만족시키는 목표 요 모멘트를 결정한다. G-vectoring 제어는 과도한 횡 가속도를 줄이기 위하여 종방향 가속도를 차량에 작용하게 하여 전복 안정성을 확보 하도록 설계하였다. 속도 제어는 운전자의 의도를 만족하기 위하여 슬라이딩 제어 기법을 기반으로 설계되었다. 하위 제어기는 각 휠의 슬립 상황, 인휠 모터의 토크 제한등을 고려하여 각 휠에 분배된다. 이를 위하여 Control Allocation 기법이 사용되었으며, 실시간 구현을 위하여 4가지 해석 기법을 개발하고 적용하여 적합한 알고리즘을 적용하였다. 동력관리 제어는 차량 구동에 있어서 연료소모량을 최소로 하기 위한 전략을 기반으로 설계되었다. 등가 연료 소모량 최소 전략 (ECMS)이 사용되어 최적의 연료 효율을 확보하였다. 제어기 성능 검증을 위하여 컴퓨터 시뮬레이션을 수행하였다. 시뮬레이션 결과를 통해 일반 차량의 성능과 비교하여, 크게 향상된 안정성, 주행성 및 에너지 효율을 확인 하였다.This paper describes an integrated driving control algorithm for optimized maneuverability and stability of a six-wheeled driving/brake and six-wheeled steering (6WD/6WS) electric combat vehicle which is equipped with drive/brake-by-wire and steer-by-wire modules. This integrated driving control algorithm is developed to obtain optimized stability, maneuverability and energy efficiency of a 6WD/6WS vehicle. The proposed control algorithm consists of four parts: desired dynamics, upper level control, lower level control and power management algorithm. The desired dynamics determines the steering angle of each wheel and the desired acceleration according to drivers steering, throttle, and braking inputs. Stability decision/control, yaw moment control, and speed control algorithms are included in the upper level control layer in order to track the desired dynamics and guarantee yaw and roll stability. The lower level control layer which is based on a control allocation method computes actuator commands, such as independent driving and regenerative braking torques. In the upper level control layer, the stability decision algorithm defines stability regions on a g-g diagram and calculates the desired longitudinal acceleration based on a G-vectoring control method and the desired yaw rate for lateral and yaw stability, and rollover prevention. The G-vectoring control algorithm determines the longitudinal acceleration required to keep the vehicle stable. The speed control calculates the desired longitudinal net force, and the desired net yaw moment is determined to track the desired yaw rate. Control allocation method is used to design the lower level control layer. Limitations related to the physical maximum output torque and prevention of excessive wheel slip are defined as control input constraints of control allocation, which takes friction circle information into account. For real-time implementation, four candidate methods have been designed and developed to solve the control allocation problem. Feasible method has been adopted, taking execution time into account in order to obtain optimized solutions. In the power management layer, from the determined input torque, the required power can be calculated. The required engine/generator and battery power are determined to minimize energy consumption. Fuel consumption minimization strategy (ECMS) is useful for on-line optimization and adopted to implement real-time applications. Computer simulations have been conducted to evaluate the proposed integrated driving control algorithm. It has been shown from simulation results that, compared to conventional drive systems, significantly improved vehicle maneuverability and stability can be obtained by the proposed integrated control algorithm.Abstract i List of Tables viii List of Figures ix Nomenclature xiii Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Previous Researches 3 1.2.1 Lateral Stability Control System 3 1.2.2 Torque Vectoring Control System 5 1.2.3 G-Vectoring Control System 7 1.2.4 Control Allocation 8 1.2.5 Power Management Control System 9 1.3 Thesis Objectives 11 1.4 Thesis Outline 13 Chapter 2. Control System Modeling 15 2.1 Control System Overview 15 2.2 Control System Architecture 18 2.3 Vehicle Dynamic, Actuators and Power System Model 20 2.3.1 Vehicle dynamic model 20 Body dynamics 21 Tire dynamics 22 2.3.2 Motor Dynamic model 24 2.3.3 Power System Model 25 2.3.4 Plannar Model for Control System Design 28 Stability analysis of the proposed 6WD/6WS platform 32 2.3.5 Bicycle model for Direct Yaw Moment Control Design 36 Chapter 3. Integrated Driving Control Algorithm 37 3.1 Desired Dynamics Layer 38 3.1.1 Desired steering angle determination 38 3.1.2 Desired velocity determination 40 3.2 Upper Level Control Layer 44 3.2.1 Stability decision algorithm 44 3.2.2 G-vectoring control algorithm 49 Accessibility of the G-vectoring control algorithm 50 Controllability of the G-vectoring control algorithm 53 Design of G-vectoring control algorithm 55 3.2.3 Yaw moment control algorithm 59 Performance verification based on frequency analysis 64 3.2.4 Speed control algorithm 70 Velocity tracking algorithm 71 Acceleration tracking algorithm 72 Switching algorithm 72 3.2.5 Stability analysis of the proposed control system 73 3.3 Lower Level Control Layer 79 3.3.1 Control Allocation Formulation 79 Cost function and constraints definition for control allocation problem formulation 81 Actuator Limitation Algorithm 91 Slip Limitation Algorithm 93 3.3.2 Fixed-point (FXP) control allocation method 97 3.3.3 Cascaded Generalized pseudo-inverse (CGI) method 99 3.3.4 Interior point (IP) method 101 3.3.5 Weighted least square method (WLS) 107 3.3.6 Implementation of control allocation 112 Unsaturated condition of control inputs 113 Saturated condition of control inputs 116 3.4 Power Management Layer 121 3.4.1 Equivalent fuel consumption minimization stratery(ECMS) 121 3.4.2 Design of engine/generator control algorithm 130 Chapter 4. Estimator Design 132 4.1 Longitudinal tire force estimation 133 4.2 Friction circle estimation 136 Chapter 5. Simulation Results 143 5.1 Turning Performance Verification – Open loop 151 5.2 Turning Performance Verification with Braking 156 5.3 Turning Performance Verification – Closed- loop 160 5.4 Lateral Stability Verification 162 5.5 Rollover Stability Verification 170 5.6 Driving Performance Verification for Gradient Road 173 5.7 Performance Verification of Energy Efficiency 175 5.8 Integrated Performance Verification using Test Track 186 5.9 Integrated Performance Verification using Test Track (DLC included) 195 Bibliography 202 국문초록 208Docto

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs