Integrated vehicle dynamics control using active steering, driveline and braking

This thesis investigates the principle of integrated vehicle dynamics control through proposing a new control configuration to coordinate active steering subsystems and dynamic stability control (DSC) subsystems. The active steering subsystems include Active Front Steering (AFS) and Active Rear Steering (ARS); the dynamic stability control subsystems include driveline based, brake based and driveline plus brake based DSC subsystems. A nonlinear vehicle handling model is developed for this study, incorporating the load transfer effects and nonlinear tyre characteristics. This model consists of 8 degrees of freedom that include longitudinal, lateral and yaw motions of the vehicle and body roll motion relative to the chassis about the roll axis as well as the rotational dynamics of four wheels. The lateral vehicle dynamics are analysed for the entire handling region and two distinct control objectives are defined, i.e. steerability and stability which correspond to yaw rate tracking and sideslip motion bounding, respectively. Active steering subsystem controllers and dynamic stability subsystem controller are designed by using the Sliding Mode Control (SMC) technique and phase-plane method, respectively. The former is used as the steerability controller to track the reference yaw rate and the latter serves as the stability controller to bound the sideslip motion of the vehicle. Both stand-alone controllers are evaluated over a range of different handling regimes. The stand-alone steerability controllers are found to be very effective in improving vehicle steering response up to the handling limit and the stand-alone stability controller is found to be capable of performing the task of maintaining vehicle stability at the operating points where the active steering subsystems cannot. Based on the two independently developed stand-alone controllers, a novel rule based integration scheme for AFS and driveline plus brake based DSC is proposed to optimise the overall vehicle performance by minimising interactions between the two subsystems and extending functionalities of individual subsystems. The proposed integrated control system is assessed by comparing it to corresponding combined control. Through the simulation work conducted under critical driving conditions, the proposed integrated control system is found to lead to a trade-off between stability and limit steerability, improved vehicle stability and reduced influence on the longitudinal vehicle dynamics

Robust H

To shorten the steer diameter and to improve the maneuverability flexibility of a construction vehicle, four wheels’ steering system is presented. This steering system consists of mechanical-electrical-hydraulic assemblies. Its diagram and principle are depicted in detail. Then the mathematical models are derived step by step, including the whole vehicle model and the hydraulic route model. Considering the nonlinear and time-varying uncertainty of the steering system, robust H2/H∞ controller is put forward to guarantee both the system performance and the robust stability. The H∞ norm of the sensitive function from the parameter perturbation of the hydraulic system to the yaw velocity of the vehicle is taken as the evaluating index of the robustness and the H2 norm of the transfer function from the external disturbance to the steering angle of the wheel as the index of linear quadratic Gaussian. The experimental results showed that the proposed scheme was superior to classical PID controller and can guarantee both the control performance and the robustness of the steering system

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

Synthesis and Analysis of an Active Independent Front Steering (AIFS) System

Technological developments in road vehicles over the last two decades have received considerable attention towards pushing the safe performance limits to their ultimate levels. Towards this goal, Active Front Steering (AFS) and Direct Yaw-moment Control (DYC) systems have been widely investigated. AFS systems introduce corrective steering angles to the conventional system in order to realize a target handling response for a given speed and steering input. An AFS system, however, may yield limited performance under severe steering maneuvers involving substantial lateral load shift and saturation of the inside tire-road adhesion. The adhesion available at the outer tire, on the other hand, would remain under-utilized. This dissertation explores effectiveness of an Active Independent Front Steering (AIFS) system that could introduce a corrective measure at each wheel in an independent manner. The effectiveness of the AIFS system was investigated firstly through simulation of a yaw-plane model of a passenger car. The preliminary simulation results with AIFS system revealed superior potential compared to the AFS particularly in the presence of greater lateral load shift during a high-g maneuver. The proposed concept was thus expected to be far more beneficial for enhancement of handling properties of heavy vehicles, which invariably undergo large lateral load shift due to their high center of mass and roll motion. A nonlinear yaw-plane model of a two-axle single-unit truck, fully and partially loaded with solid and liquid cargo, with limited roll degree-of-freedom (DOF) was thus developed to study the performance potentials of AIFS under a range of steering maneuvers. A simple PI controller was synthesized to track the reference yaw rate response of a neutral steer vehicle. The steering corrections, however, were limited such that none of the tires approach saturation. For this purpose, a tire saturation zone was identified considering the normalized cornering stiffness property of the tire. The controller strategy was formulated so as to limit the work-load magnitude at a pre-determined level to ensure sufficient tire-road adhesion reserve to meet the braking demand, when exists. Simulation results were obtained for a truck model integrating AFS and AIFS systems subjected to a range of steering maneuvers, namely: a J-turn maneuver on uniform as well as split-μ road conditions, and path change and obstacle avoidance maneuvers. The simulation results showed that both AFS and AIFS can effectively track the target yaw rate of the vehicle, while the AIFS helped limit saturation of the inside tire and permitted maximum utilization of the available tire-road adhesion of the outside tire. The results thus suggested that the performance of an AIFS system would be promising under severe maneuvers involving simultaneous braking and steering, since it permitted a desired adhesion reserve at each wheel to meet a braking demand during the steering maneuver. Accordingly, the vehicle model was extended to study the dynamic braking characteristics under braking-in-turn maneuvers. The simulation results revealed the most meritorious feature of the AIFS in enhancing the braking characteristics of the vehicle and reducing the stopping time during such maneuvers. The robustness of the proposed control synthesis was subsequently studied with respect to parameter variations and external disturbance. This investigation also explores designs of fail-safe independently controllable front wheels steering system for implementation of the AIFS concept

Control of vehicle lateral dynamics based on longitudinal wheel forces

Trends show that on board vehicle technology is becoming increasingly complex and that this will continue to be the case. This complexity has enabled both driver assistance systems and fully automatic systems to be introduced. Driver assistance systems include anti-lock braking and yaw rate control, and these differ from fully automatic systems which include collision avoidance systems, where control of the car may be taken away from the driver. With this distinct difference in mind, this work will focus on driver assist based systems, where emerging technology has created an opportunity to try and improve upon the systems which are currently available. This work investigates the ability to simultaneously control a set of two lateral dynamics using primarily the longitudinal wheels forces. This approach will then be integrated with front wheel steering control to assess if any benefits can be obtained. To aid this work, three different vehicle models are available. A linear model is derived for the controller design stage, and a highly nonlinear validated model from an industrial partner is available for simulation and evaluation purposes. A third model, which is also nonlinear, is used to integrate the control structures with a human interface test rig in a Hardware in the Loop (HiL) environment, which operates in real-time. Frequency based analysis and design techniques are used for the feedback controller design, and a feedforward based approach is used to apply a steering angle to the vehicle model. Computer simulations are initially used to evaluate the controllers, followed by evaluation via a HiL setup using a test rig. Using a visualisation environment in Matlab, this interface device allows driver interaction with the controllers to be analysed. It also enables driver reaction without any controllers present to be compared directly with the controller performance whilst completing the test manoeuvres. Results show that during certain manoeuvres, large variations in vehicle velocity are required to complete the control objective. However, it can be concluded from both the computer simulation and HiL results that simultaneous control of the lateral dynamics, based on the longitudinal wheel forces can be achieved using linear control methods

Path tracking control of a multi-actuated autonomous vehicle at the limits of handling.

Longo, Stefano - Associate Supervisor Siampis, Efstathios - Associate SupervisorOver the past few decades, autonomous vehicles have been widely considered as the next generation of road transportation. As a result, relevant technology has been rapidly developed, and one specific topic is enabling autonomous vehicles to operate under demanding conditions. This requires the autonomous driving controller to have a good understanding of the vehicle dynamics at the limits of handling, and is expected to improve the performance as well as safety of autonomous vehicles especially in extreme situations. Furthermore, there has been application of techniques such as torque vectoring and four- wheel steering on modern vehicles as part of the driver assistance system, while such multi-actuation can be deployed on an autonomous vehicle in order to further enhance its performance in response to challenging manoeuvres and scenarios. This thesis aims to develop a real-time path tracking control strategy for an autonomous electric vehicle at the limits of handling, taking advantage of torque vectoring and four- wheel steering techniques for the enhanced control of vehicle dynamics. A nonlinear model predictive control formulation based on a three degree-of-freedom vehicle model is proposed for control design, which takes into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. In addition, steady-state references of steering inputs as well as vehicle states are generated based on a bicycle model and included in the control formulation to improve the performance. Two path tracking models with different coordinate systems are introduced to the control formulation, and compared to understand the more suitable one for the proposed path tracking purpose. Then the path tracking performance with different levels of actuation is investigated. According to the high-fidelity simulation results, the vehicle achieves the minimum lateral deviation with the over-actuation topology including both torque vectoring and four-wheel steering, which illustrates that the over-actuation formulation can enhance the path tracking performance by enduing the vehicle with the best flexibility as well as stability during operation at the limits of handling. Before being implemented on the vehicle, the performance of the proposed control strategy is further assessed with regards to real-time operation. After evaluating the control performance with different prediction horizons and sampling time, the most suitable setup is identified which compromises between the control performance and the capability of real-time execution. Finally, the control algorithm is implemented on a real vehicle for practical testing. The controller is tested in four different scenarios, and the results demonstrate that the proposed controller is capable of path tracking control and vehicle stabilisation for multi-actuated autonomous vehicles at the limits of handling. In general, this thesis has proposed a path tracking controller for autonomous vehicles which takes into account nonlinear vehicle dynamics at the limits of handling. Following some necessary simplification, the developed controller has been successfully deployed on a real vehicle in real time, and the control performance has been validated in several challenging scenarios. The controller proves itself to be able to improve the vehicle’s flexibility as well as to stabilise the vehicle at the limits of handling, and furthermore, it is able to accommodate relatively large side slip angles during the demanding manoeuvres as well.PhD in Transport System

Robust Discrete-Time Lateral Control of Racecars by Unknown Input Observers

This brief addresses the robust lateral control problem for self-driving racecars. It proposes a discrete-time estimation and control solution consisting of a delayed unknown input-state observer (UIO) and a robust tracking controller. Based on a nominal vehicle model, describing its motion with respect to a generic desired trajectory and requiring no information about the surrounding environment, the observer reconstructs the total force disturbance signal, resulting from imperfect knowledge of the time-varying tire-road interface characteristics, presence of other vehicles nearby, wind gusts, and other model uncertainty. Then, the controller actively compensates the estimated force and asymptotically steers the tracking error to zero. The brief also presents a closed-loop stability proof of the method, ensuring perfect asymptotic estimation and tracking by the controlled vehicle. The proposed solution advantageously needs no a-priori information about the total disturbance boundedness, additional variables to model uncertainty, or observer parameters to be tuned. Its effectiveness and superiority to existing methods are studied in theory and shown in simulations where a full racecar model, based on the vehicle dynamics blockset, is required to track aggressive maneuvers. Through a faster and more accurate disturbance estimation, the solution robustly ensures better dynamic responses even with measurement noise

H2 optimal control algorithms for vehicle control

Vzestup autonomních vozidel a e-mobility umožňuje nasazení pokročilých řídicích systémů. Řízení na úrovni dynamiky vozidla poskytuje vyšší bezpečnost a lepší odezvu speciálně při velmi rychlých manévrech. Tato práce bere v potaz fyzikální limity dané cestou, pneumatikami a dynamikou vozidla a navrhuje řešení pro řízení podélné dynamiky. Cílem je maximalizovat podélné zrychlení vozidla. V této práci je použit jako nelineární verifikační a validační model jednostopý model vozidla, který zahrnuje Pacejkovu magickou rovnici pro modelování pneumatik. Jsou zde navrženy 2 možné přístupy k řešení. V první části je prezentováno řízení podélného skluzu lambda. Stavový model pro návrh řídicího systému je odvozen z nelineárního modelu v pracovním bodě s konstantním zrychlením. Protože rychlost je stav systému nelze zde použít běžné linearizační metody - nejedná se o linearizaci v ekvilibriu. Místo toho je použita linearizace podél trajektorie. Toto výustí v použití LPV technik. Dále je navržen řídicí algoritmus založený na použití LQ metodologie, který řídí podélný skluz. V druhé části je představen řídicí systém založený na sledování úhlové rychlosti kol. Jádro tohoto systému tvoří zpětnovazební LQ řídicí smyčka pro řízení úhlové rychlosti omega kola. Referenční signál úhlové rychlosti kol je vypočítáván na základě požadavku na lambdu. Jako nejvyšší v hierarchii je zpětnovazební smyčka řízení zrychlení. Nakonec jsou provedeny virtuální jízdní testy, které porovnávají řídicí sytém založený na sledování úhlové rychlosti kol a systém bez regulace na klouzavém povrchu.Trend of autonomous vehicles and e-mobility is in favor of an advanced control system development and deployment. Vehicle dynamics level control systems providing safety limits and high performance response, especially during high dynamics maneuvers, are necessary. This work provides solution for vehicle longitudinal dynamics (vehicle acceleration) considering physical limits given by road, tire and vehicle dynamics respectively. The goal is to maximize vehicle longitudinal acceleration. Considered mathematical model is nonlinear single-track model incorporating nonlinear Pacejka magic formula as a tire model. This work proposes two possible control approaches. In first part the direct longitudinal slip ratio lambda control is presented. Design model for control system is derived as a linearized state-space model at constant acceleration operation point. Therefore, the common linearization approach, at system equilibrium, is not possible and the linearization along system trajectory is used. Such solution results in involvement of LPV techniques, as vehicle velocity is state variable. Next, the LQ optimal control framework is employed to deliver control algorithms providing constant longitudinal slip ratio trajectory tracing. Augmented direct slip ratio lambda control based on wheel angular velocity tracking is proposed in second part. The core of suggested hierarchical control system is the LQ-based closed loop for single wheel angular velocity omega tracking. The omega set-point signal is computed based on lambda demand. Finally, the vehicle longitudinal acceleration controller is designed. Virtual riding tests comparing the omega tracking based control system and open loop behavior on slippery surface are provided at the end of thesis