Smart System Side Slip Tester with Exponential Filter

According to Article 6, Paragraph 1, of Law No. 55 of 2012 Concerning Cars, cars that are not roadworthy are particularly harmful for the safety of passengers and other road users. The front wheel ring, which has a significant impact on the safety of the motorized vehicle, is one of the technical requirements for roadworthiness. The front wheel pins make sure the car can go straight, which is related to the steering system's safety and has an impact on fuel economy. Through routine testing at the motor vehicle testing facility owned by the Transportation Service, the front wheel valve examination is performed using a front wheel blade test tool known as the Side Slip Tester. Previously, a lot of the automobile test equipment used at various test facilities was impractical and inaccurate. The construction of a smart system for evaluating wheel blades on cars is covered in this study, along with the implementation of an exponential filter to improve and lower the noise in sensor readings of ADC signals. By comparing the readings of the manufactured tool with a calibrated dial indicator, tests and calibrations are performed. The graph shows that the response to the input signal is quick and excellent for noise filtering, so based on the results of the exponential filter test, 0.2 is the ideal weight for the ADC reading filter. The 9 mm side slip bench shear test yields a maximum error result of 3% following tool calibration

A diagnosis-based approach for tire-road forces and maximum friction estimation

International audienceA new approach to estimate vehicle tire forces and road maximum adherence is presented. Contrarily to most of previous works on this subject, it is not an asymptotic observer based estimation, but a combination of elementary diagnosis tools and new algebraic techniques for filtering and estimating derivatives of noisy signals. In a first step, instantaneous friction and lateral forces will be computed within this framework. Then, extended braking stiffness concept is exploited to detect which braking efforts allow to distinguish a road type from another. A weighted Dugoff model is used during these “distinguishable” intervals to estimate the maximum friction coefficient. Very promising results have been obtained in noisy simulations and real experimentations for most of driving situations

Fixed-Order Robust H

We present a novel linear observer with an extension dealing with polytopic uncertainties in a vehicle dynamic system to identify the side-slip angle. The performance optimization issue is addressed by the minimization of H∞ norm of the system considering the estimation error as an output and the steer angle as an input. Contrary to the standard robust optimal design approaches, we use a convex inner approximation technique to reduce the order of the observer and this enables us to derive suboptimal, fixed-order, and efficiently practicable estimators. Moreover, the numerical examples performed on two-track nonlinear model of the system are provided to illustrate the impacts of design parameters on the optimization results and the efficiency of the technique

Corner-based estimation of tire forces and vehicle velocities robust to road conditions

The final publication is available at Elsevier via https://doi.org/10.1016/j.conengprac.2017.01.009 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recent developments in vehicle stability control and active safety systems have led to an interest in reliable vehicle state estimation on various road conditions. This paper presents a novel method for tire force and velocity estimation at each corner to monitor tire capacities individually. This is entailed for more demanding advanced vehicle stability systems and especially in full autonomous driving in harsh maneuvers. By integrating the lumped LuGre tire model and the vehicle kinematics, it is shown that the proposed corner-based estimator does not require knowledge of the road friction and is robust to model uncertainties. The stability of the time-varying longitudinal and lateral velocity estimators is explored. The proposed method is experimentally validated in several maneuvers on different road surface frictions. The experimental results confirm the accuracy and robustness of the state estimators.Automotive Partnership Canada, Ontario Research Fund, General Motors Co

Nonlinear Control and Estimation Techniques with Applications to Vision-based and Biomedical Systems

This dissertation is divided into four self-contained chapters. In Chapter 1, a new estimator using a single calibrated camera mounted on a moving platform is developed to asymptotically recover the range and the three-dimensional (3D) Euclidean position of a static object feature. The estimator also recovers the constant 3D Euclidean coordinates of the feature relative to the world frame as a byproduct. The position and orientation of the camera is assumed to be measurable unlike existing observers where velocity measurements are assumed to be known. To estimate the unknown range variable, an adaptive least squares estimation strategy is employed based on a novel prediction error formulation. A Lyapunov stability analysis is used to prove the convergence properties of the estimator. The developed estimator has a simple mathematical structure and can be used to identify range and 3D Euclidean coordinates of multiple features. These properties of the estimator make it suitable for use with robot navigation algorithms where position measurements are readily available. Numerical simulation results along with experimental results are presented to illustrate the effectiveness of the proposed algorithm. In Chapter 2, a novel Euclidean position estimation technique using a single uncalibrated camera mounted on a moving platform is developed to asymptotically recover the three-dimensional (3D) Euclidean position of static object features. The position of the moving platform is assumed to be measurable, and a second object with known 3D Euclidean coordinates relative to the world frame is considered to be available a priori. To account for the unknown camera calibration parameters and to estimate the unknown 3D Euclidean coordinates, an adaptive least squares estimation strategy is employed based on prediction error formulations and a Lyapunov-type stability analysis. The developed estimator is shown to recover the 3D Euclidean position of the unknown object features despite the lack of knowledge of the camera calibration parameters. Numerical simulation results along with experimental results are presented to illustrate the effectiveness of the proposed algorithm. In Chapter 3, a new range identification technique for a calibrated paracatadioptric system mounted on a moving platform is developed to recover the range information and the three-dimensional (3D) Euclidean coordinates of a static object feature. The position of the moving platform is assumed to be measurable. To identify the unknown range, first, a function of the projected pixel coordinates is related to the unknown 3D Euclidean coordinates of an object feature. This function is nonlinearly parameterized (i.e., the unknown parameters appear nonlinearly in the parameterized model). An adaptive estimator based on a min-max algorithm is then designed to estimate the unknown 3D Euclidean coordinates of an object feature relative to a fixed reference frame which facilitates the identification of range. A Lyapunov-type stability analysis is used to show that the developed estimator provides an estimation of the unknown parameters within a desired precision. Numerical simulation results are presented to illustrate the effectiveness of the proposed range estimation technique. In Chapter 4, optimization of antiangiogenic therapy for tumor management is considered as a nonlinear control problem. A new technique is developed to optimize antiangiogenic therapy which minimizes the volume of a tumor and prevents it from growing using an optimum drug dose. To this end, an optimum desired trajectory is designed to minimize a performance index. Two controllers are then presented that drive the tumor volume to its optimum value. The first controller is proven to yield exponential results given exact model knowledge. The second controller is developed under the assumption of parameteric uncertainties in the system model. A least-squares estimation strategy based on a prediction error formulation and a Lyapunov-type stability analysis is developed to estimate the unknown parameters of the performance index. An adaptive controller is then designed to track the desired optimum trajectory. The proposed tumor minimization scheme is shown to minimize the tumor volume with an optimum drug dose despite the lack of knowledge of system parameters. Numerical simulation results are presented to illustrate the effectiveness of the proposed technique. An extension of the developed technique for a mathematical model which accounts for pharmacodynamics and pharmacokinetics is also presented. Futhermore, a technique for the estimation of the carrying capacity of endothelial cells is also presented

Reliable Vehicle State and Parameter Estimation

Diverse vehicle active safety systems including vehicle electronic stability control (ESC) system, anti-lock braking system (ABS), and traction control system (TCS) are significantly relying on information about the vehicle's states and parameters, as well as the vehicle's surroundings. However, many important states or parameters, such as sideslip angle, tire-road friction coefficient, road gradient and vehicle mass are hard to directly measure, and hence advanced estimation algorithms are needed. Furthermore, enhancements of sensor technologies and the emergence of new concepts such as {\it Internet of Things} and their automotive version, {\it Internet of Vehicles}, facilitate reliable and resilient estimation of vehicle states and road conditions. Consequently, developing a resilient estimation structure to operate with the available sensor data in commercial vehicles and be flexible enough to incorporate new information in future cars is the main objective of this thesis. This thesis presents a reliable corner-based vehicle velocity estimation and a road condition classification algorithm. For vehicle velocity estimation, a combination of vehicle kinematics and the LuGre tire model is introduced in the design of a corner-based velocity observer. Moreover, the observability condition for both cases of time-invariant and parameter varying is studied. The effect of suspension compliance on enhancing the accuracy of the vehicle corner velocity estimation is also investigated and the results are verified via several experimental tests. The performance and the robustness of the proposed corner-based vehicle velocity estimation to model and road condition uncertainties is analyzed. The stability of the observer is discussed, and analytical expressions for the boundedness of the estimation error in the presence of system uncertainties for the case of fixed observer gains are derived. Furthermore, the stability of the observer under arbitrary and stochastic observer gain switching is studied and the performances of the observer for these two switching scenarios are compared. At the end, the sensitivity of the proposed observer to tire parameter variations is analyzed. These analyses are referred to as offline reliability methods. In addition to the off-line reliability analysis, an online reliability measure of the proposed velocity estimation is introduced, using vehicle kinematic relations. Moreover, methods to distinguish measurement faults from estimation faults are presented. Several experimental results are provided to verify the approach. An algorithm for identifying (classifying) road friction is proposed in this thesis. The analytical foundation of this algorithm, which is based on vehicle response to lateral excitation, is introduced and its performance is discussed and compared to previous approaches. The sensitivity of this algorithm to vehicle/tire parameter variations is also studied. At the end, various experimental results consisting of several maneuvers on different road conditions are presented to verify the performance of the algorithm

Estimación del ángulo de deriva de un vehículo mediante redes neuronales

Los objetivos que se pretenden cumplir en el presente Proyecto Fin de Carrera son los siguientes: - Tratar, de forma breve, aquellos aspectos más importantes e interesantes que están relacionados con la dinámica lateral del vehículo, así como los sistemas de seguridad incorporados en los vehículos para negociar con las inestabilidades que se puedan producir en el automóvil durante su marcha. - Profundizar en el conocimiento del ángulo de deriva de un automóvil, uno de los parámetros claves en la dinámica lateral. - Realizar un repaso sobre las técnicas y estrategias seguidas por diferentes autores en sus estudios con el objetivo de medir o estimar el ángulo de deriva. En especial, se profundizará sobre las Redes Neuronales Artificiales. - Estimación del ángulo de deriva de un vehículo mediante la técnica de Redes Neuronales Artificiales, haciendo uso del Software Java-NNS, introduciendo como parámetros de entrada al sistema: la velocidad longitudinal, la aceleración lateral, la velocidad de guiñada y el ángulo de dirección obtenidos del software CarSim.Ingeniería Industria

Full Vehicle State Estimation Using a Holistic Corner-based Approach

Vehicles' active safety systems use different sensors, vehicle states, and actuators, along with an advanced control algorithm, to assist drivers and to maintain the dynamics of a vehicle within a desired safe range in case of instability in vehicle motion. Therefore, recent developments in such vehicle stability control and autonomous driving systems have led to substantial interest in reliable road angle and vehicle states (tire forces and vehicle velocities) estimation. Advances in applications of sensor technologies, sensor fusion, and cooperative estimation in intelligent transportation systems facilitate reliable and robust estimation of vehicle states and road angles. In this direction, developing a flexible and reliable estimation structure at a reasonable cost to operate the available sensor data for the proper functioning of active safety systems in current vehicles is a preeminent objective of the car manufacturers in dealing with the technological changes in the automotive industry. This thesis presents a novel generic integrated tire force and velocity estimation system at each corner to monitor tire capacities and slip condition individually and to address road uncertainty issues in the current model-based vehicle state estimators. Tire force estimators are developed using computationally efficient nonlinear and Kalman-based observers and common measurements in production vehicles. The stability and performance of the time-varying estimators are explored and it is shown that the developed integrated structure is robust to model uncertainties including tire properties, inflation pressure, and effective rolling radius, does not need tire parameters and road friction information, and can transfer from one car to another. The main challenges for velocity estimation are the lack of knowledge of road friction in the model-based methods and accumulated error in kinematic-based approaches. To tackle these issues, the lumped LuGre tire model is integrated with the vehicle kinematics in this research. It is shown that the proposed generic corner-based estimator reduces the number of required tire parameters significantly and does not require knowledge of the road friction. The stability and performance of the time-varying velocity estimators are studied and the sensitivity of the observers' stability to the model parameter changes is discussed. The proposed velocity estimators are validated in simulations and road experiments with two vehicles in several maneuvers with various driveline configurations on roads with different friction conditions. The simulation and experimental results substantiate the accuracy and robustness of the state estimators for even harsh maneuvers on surfaces with varying friction. A corner-based lateral state estimation is also developed for conventional cars application independent of the wheel torques. This approach utilizes variable weighted axles' estimates and high slip detection modules to deal with uncertainties associated with longitudinal forces in large steering. Therefore, the output of the lateral estimator is not altered by the longitudinal force effect and its performance is not compromised. A method for road classification is also investigated utilizing the vehicle lateral response in diverse maneuvers. Moreover, the designed estimation structure is shown to work with various driveline configurations such as front, rear, or all-wheel drive and can be easily reconfigured to operate with different vehicles and control systems' actuator configurations such as differential braking, torque vectoring, or their combinations on the front or rear axles. This research has resulted in two US pending patents on vehicle speed estimation and sensor fault diagnosis and successful transfer of these patents to industry

Road vehicle state estimation using low-cost GPS/INS

Due to noise and bias in the Inertial Navigation System (INS), vehicle dynamics measurements using the INS are inaccurate. Although alternative methods involving the integration of INS with accurate Global Positioning System (GPS) exist and are accurate, this kind of system is far too expensive to become value-adding to production vehicles. This thesis therefore considers two aspects: 1) the possibility of estimating vehicle dynamics using low-cost INS and GPS, and 2) the importance of vehicle dynamics in terms of handling in the eyes of customers upon vehicle purchase. The former aspect is considered from an engineering perspective and the latter is studied in a marketing context. From an engineering point of view, knowledge of vehicle dynamics not only improves existing safety control systems, such the Anti-lock Braking System (ABS) and Electronic Stabilising Program (ESP), but also allows the development of new systems. Based on modelling and simulation in MATLAB/Simulink, low-cost GPS and in-car INS (such as accelerometers, gyroscopes and wheel speed sensors) measurements are fused using Kalman Filters (KFs) to estimate the vehicle dynamics. These estimations are then compared with the simulation results from IPG Car- Maker. For most simulations, the speed of the vehicle is kept between 15 to 55kph. It is found that while triple KF designs are able to estimate the tyre radius, the longitudinal velocity and the heading angle accurately, an integrated KF design with known vehicle parameters is also able to estimate the lateral velocity precisely. Apart from studying and comparing different KF designs with restricted sensors quality, the effects and benefits of different sensor qualities in dynamic estimations are also studied via the variation of sensor sampling rates and accuracies. This investigation produces a design procedure and estimation error analyses (theoretical and graphical) which may help future engineers in designing their KFs. From a marketing perspective, it is important to understand customers’ purchase reasons in order to allocate resources more efficiently and effectively. As GPS/INS KF designs are able to enhance vehicle handling, it is vital to understand the relative importance of vehicle handling as a consumer purchase choice criterion. Based on two surveys, namely the New Vehicle Experience Survey in the US (NVES US) and the New Car Buyer Survey in the UK (NCBS UK), analyses are performed in a computer program called the Predictive Analytics SoftWare (PASW), which is formerly known as the Statistical Package for the Social Sciences (SPSS). The number of purchase reasons are first reduced with factor analysis, the latent factors produced are then used in the SPSS Two Step Cluster analysis for customer segmentation. With the customer segments and the latent factors defined, a discriminant analysis is carried out to determine customer type in the automobile sector, in particular for Jaguar Cars. It is found that customers in general take vehicle handling for granted and often underrate its importance in their purchase. New vehicle handling-aided systems therefore need to be marketed in terms of the value they add to other benefits such as reliability and performance in order to increase sales and stakeholder value

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