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

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

Survey of the Application Fields and Modeling Methods of Automotive Vehicle Dynamics Models

In this paper, a review is presented on automotive vehicle dynamics modeling. Applied vehicle dynamics models from various application fields are analyzed and classified in the first section. Vehicle dynamics models may be simplified because of different reasons: several control/estimation/analysis methods are suitable only for simplified models (e.g. using control-oriented models), or because of the computational cost. Detailed/truth models of vehicle dynamics represent another field of vehicle dynamics modeling, these models play an important role in the virtual prototyping of vehicles. In the second section, the main modeling considerations of vehicle dynamics are presented in longitudinal, lateral and vertical directions. Various physical effects must be considered in the case of vehicle dynamics modeling, a lot of these effects are significant only in a specific direction of the vehicle body, which is the main potential of model simplification. The section presents vehicle modeling considerations in all of the three translational directions of the vehicle body

Adaptive Sliding Mode Fault Tolerant Control for Autonomous Vehicle With Unknown Actuator Parameters and Saturated Tire Force Based on the Center of Percussion

With consideration of tire force saturation in vehicle motions, a novel path-following controller is developed for autonomous vehicles with unknown-bound disturbances and unknown actuator parameters. An adaptive sliding-mode fault-tolerant control (ASM-FTC) strategy is designed to stabilize the path-following errors without any information of disturbance boundaries, actuator fault boundaries and steering ratio from the steering wheel to the front wheels. By selecting the distance from the center of gravity to the center of percussion as the preview length, the effects of the lateral rear-tire force are decoupled and cancelled out, and then the preview error, which represents the path-following performance, can be only commanded by the front-tire force. To further address the issue of unknown tire-road friction limits, a modified ASM-FTC strategy is presented to improve the path-following performance as the lateral tire force is saturated. Simulation results show that the modified ASM-FTC controller demonstrates superior tracking performance over the normal ASM-FTC while the autonomous vehicle follows desired paths

Controller with Vehicular Communication Design for Vehicular Platoon System

PhD ThesisTracked Electric Vehicles (TEV) which is a new mass-transport system. It aims to provide a safe, efficient and coordinated traffic system. In TEV, the inter-vehicular distance is reduced to only a quarter of the regular car length and where drive at 200km/h enabling mass transport at uniform speed. Under this requirement, the design of the controller is particularly important. This thesis first developed an innovative approach using adaptive Proportion, integral and derivation (PID) controller using fuzzy logic theory to keep variable time-gap between dynamic cars for platooning system with communication delay. The simulation results presented show a significant improvement in keeping time-gap variable between the cars enabling a safe and efficient flow of the platooning system. Secondly, this thesis investigates the use of Slide Mode Control (SMC) for TEV. It studies different V2V communication topology structures using graph theory and proposes a novel SMC design with and without global dynamic information. The Lyapunov candidate function was chosen to study the impact which forms an integral part for current and future research. The simulation results show that this novel SMC has a tolerance ability for communication delay. In order to present the real time TEV platoon system, a similar PI controller has been utilized in a novel automated vehicle, based on Raspberry Pi, multi-sensors and the designed Remote Control (RC) car. Thirdly, in order to obtain precise positioning information for vehicles in platoon system, this thesis describes Inertial Measurement Unit (IMU)/Global Navigation Satellite System (GNSS) data fusion to achieve a highly precise positioning solution. The results show that the following vehicles can reach the same velocity and acceleration as the leading vehicle in 5 seconds and the spacing error is less than 0.1m. The practical results are in line with those from the simulated experiment

State and Parameter Estimation of Vehicle-Trailer Systems

Vehicle-trailer systems have different unstable modes that should be considered in their stability control, including trailer snaking, jack-knifing, and roll-over. In general, vehicle control systems require vehicle parameters and states, including geometric parameters, mass, tire forces, and side slip angles which some are not constant or can be measured economically. In a vehicle-trailer system, the trailer states and parameters such as articulation angle, trailer geometric parameters, trailer mass, trailer tire forces, and yaw rate need to be measured or identified/estimated, in addition to the unknown vehicle states/parameters. The trailer states and parameters can be measured by sensors such as Inertial Measurement Unit (IMU), wheel torque sensors, and force measurement units. However, most of these sensors are not commercially viable to be used in a vehicle or trailer due to significant extra costs. Estimation algorithms are the other tools to identify the parameters and states of the system without imposing extra costs. Accurate state and parameter estimators are needed for the development and implementation of a stability control system for a vehicle-trailer system. The main purpose of this research is to design real-time state and parameter estimation algorithms for vehicle-trailer systems. Correspondingly, a comprehensive overview of different model-based and non-model-based techniques/algorithms used for estimating vehicle-trailer states and parameters are provided. The vehicle-trailer system equations of motion are then presented and based on the presented vehicle-trailer model, the possibility of the trailer states and parameters estimation are investigated for different possible vehicle-trailer on-board sensor settings. Two different methods are proposed to estimate trailer mass for arbitrary vehicle-trailer configurations: model-based and Machine Learning (ML). The stability of the model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a deep neural network is designed to estimate trailer mass. The inputs of the ML-based method are selected based on the vehicle-trailer model and are normalized by the vehicle mass, tire sizes, and geometry so that retraining of the network is not needed for different towing vehicles. The simulation and experimental results demonstrate that the trailer mass can be estimated with with acceptable computational costs. In this thesis, ultrasonic sensors along with kinematics and dynamics equations of a towing vehicle are used to develop three approaches for hitch angle estimation. The first approach is based on direct calculation of hitch angle using certain a priori geometric information and distance measurements of four Ultra sonic sensors. As the second and third approaches, kinematic and dynamic models of the vehicle-trailer system are used to develop least-square and Kalman filter based recursive hitch angle estimations. A more reliable hitch angle estimation scheme is then proposed as the integration of the algorithms developed following each of the three approaches via a switching data fusion logic. It is shown that the proposed integrated hitch angle estimation scheme can be used for any ball type trailer with a flat or symmetric V-nose frontal face without any priori information on the trailer parameters. Additionally, a new approach in estimating the lateral tire forces and hitch-forces of a vehicle-trailer system is introduced. It is shown that the proposed hitch-force estimation is independent of trailer mass and geometry. The designed lateral tire forces and hitch-force estimation algorithms can be used for any ball type trailer without any priori information on the trailer parameters. A vehicle-trailer model is proposed to design an observer for the estimation of the hitch-forces and lateral tire forces. Simulations studies in CarSim along with experimental tests are used to validate the presented method to confirm the accuracy of the developed observer. Moreover, using the vehicle-trailer lateral dynamics along with the LuGre tire model, an estimation system for the lateral velocity of a vehicle-trailer is proposed. It is shown that the proposed estimation is robust to the road conditions. An affine quadratic stability approach is used to analyze the stability of the proposed estimation. The test results confirm the accuracy of the developed estimation and convergence of the vehicle-trailer lateral velocity estimation to the actual value. Model-based and ML-based estimators are developed for estimating road angles for arbitrary vehicle-trailer configurations. The estimators are shown to be independent from road friction conditions. The model-based method employs unknown input observers on the vehicle-trailer roll and pitch dynamic models. In the proposed ML-based estimator, a recurrent neural network with Long-short-term-memory gates is designed to estimate the road angles. The inputs to the ML-based method are normalized by the vehicle wheel-base, mass, and CG height to make it applicable to any towing vehicle with the need of retraining. The simulation and experimental results justify the convergence of the road angle estimation error

Integration of Active Systems for a Global Chassis Control Design

Vehicle chassis control active systems (braking, suspension, steering and driveline), from the first ABS/ESC control unit to the current advanced driver assistance systems (ADAS), are progressively revolutionizing the way of thinking and designing the vehicle, improving its interaction with the surrounding world (V2V and V2X) and have led to excellent results in terms of safety and performances (dynamic behavior and drivability). They are usually referred as intelligent vehicles due to a software/hardware architecture able to assist the driver for achieving specific safety margin and/or optimal vehicle dynamic behavior. Moreover, industrial and academic communities agree that these technologies will progress till the diffusion of the so called autonomous cars which are able to drive robustly in a wide range of traffic scenarios. Different autonomous vehicles are already available in Europe, Japan and United States and several solutions have been proposed for smart cities and/or small public area like university campus. In this context, the present research activity aims at improving safety, comfort and performances through the integration of global active chassis control: the purposes are to study, design and implement control strategies to support the driver for achieving one or more final target among safety, comfort and performance. Specifically, the vehicle subsystems that are involved in the present research for active systems development are the steering system, the propulsion system, the transmission and the braking system. The thesis is divided into three sections related to different applications of active systems that, starting from a robust theoretical design procedure, are strongly supported by objective experimental results obtained fromHardware In the Loop (HIL) test rigs and/or proving ground testing sessions. The first chapter is dedicated to one of the most discussed topic about autonomous driving due to its impact from the social point of view and in terms of human error mitigation when the driver is not prompt enough. In particular, it is here analyzed the automated steering control which is already implemented for automatic parking and that could represent also a key element for conventional passenger car in emergency situation where a braking intervention is not enough for avoiding an imminent collision. The activity is focused on different steering controllers design and their implementation for an autonomous vehicle; an obstacle collision avoidance adaptation is introduced for future implementations. Three different controllers, Proportional Derivative (PD), PD+Feedforward (FF) e PD+Integral Sliding Mode (ISM), are designed for tracking a reference trajectory that can be modified in real-time for obstacle avoidance purposes. Furthermore, PD+FF and PD+ISM logic are able to improve the tracking performances of automated steering during cornering maneuvers, relevant fromthe collision avoidance point of view. Path tracking control and its obstacle avoidance enhancement is also shown during experimental tests executed in a proving ground through its implementation for an autonomous vehicle demonstrator. Even if the activity is presented for an autonomous vehicle, the active control can be developed also for a conventional vehicle equipped with an Electronic Power Steering (EPS) or Steer-by-wire architectures. The second chapter describes a Torque Vectoring (TV) control strategy, applied to a Fully Electric Vehicle (FEV) with four independent electric motor (one for each wheel), that aims to optimize the lateral vehicle behavior by a proper electric motor torque regulation. A yaw rate controller is presented and designed in order to achieve a desired steady-state lateral behaviour of the car (handling task). Furthermore, a sideslip angle controller is also integrated to preserve vehicle stability during emergency situations (safety task). LQR, LQR+FF and ISM strategies are formulated and explained for yaw rate and concurrent yaw rate/sideslip angle control techniques also comparing their advantages and weakness points. The TV strategy is implemented and calibrated on a FEV demonstrator by executing experimental maneuvers (step steer, skid pad, lane change and sequence of step steers) thus proving the efficacy of the proposed controller and the safety contribution guaranteed by the sideslip control. The TV could be also applied for internal combustion engine driven vehicles by installing specific torque vectoring differentials, able to distribute the torque generated by the engine to each wheel independently. The TV strategy evaluated in the second chapter can be influenced by the presence of a transmission between themotor (or the engine) and wheels (where the torque control is supposed to be designed): in addition to the mechanical delay introduced by transmission components, the presence of gears backlashes can provoke undesired noises and vibrations in presence of torque sign inversion. The last chapter is thus related to a new method for noises and vibration attenuation for a Dual Clutch Transmission (DCT). This is achieved in a new way by integrating the powertrain control with the braking system control, which are historically and conventionally analyzed and designed separately. It is showed that a torsional preload effect can be obtained on transmission components by increasing the wheel torque and concurrently applying a braking wheel torque. For this reason, a pressure following controller is presented and validated through a Hardware In the Loop (HIL) test rig in order to track a reference value of braking torque thus ensuring the desired preload effect and noises reduction. Experimental results demonstrates the efficacy of the controller, also opening new scenario for global chassis control design. Finally, some general conclusions are drawn and possible future activities and recommendations are proposed for further investigations or improvements with respect to the results shown in the present work