Adaptive Yaw Control Of Three-Axle Road Vehicles Based On Mass, Yaw Inertia And Cg Position Identification

This paper introduces an adaptive yaw control scheme based on the estimation of the vehicle mass, yaw inertia and center of gravity (CG) position. The control deigns for three-axle road vehicles, which can be trucks, buses, or even three-axle passenger cars. System parameters of these vehicles vary significantly due to varying conditions, such as unloading and fully-loading of payloads. As a result, control references and fixed-model-based controller lose efficacy. The proposed adaptive yaw control compensates these issues, utilizing the integration of a least-square based parameter identification algorithm and a Model Reference Adaptive Control (MRAC) law. Simulation test results verify the effectiveness of the proposed adaptive control scheme

Estimation of longitudinal speed robust to road conditions for ground vehicles

This is an Accepted Manuscript of an article published by Taylor & Francis in Vehicle System Dynamics on June 14 2016 available online: http://dx.doi.org/10.1080/00423114.2016.1178391This article seeks to develop a longitudinal vehicle velocity estimator robust to road conditions by employing a tyre model at each corner. Combining the lumped LuGre tyre model and the vehicle kinematics, the tyres internal deflection state is used to gain an accurate estimation. Conventional kinematic-based velocity estimators use acceleration measurements, without correction with the tyre forces. However, this results in inaccurate velocity estimation because of sensor uncertainties which should be handled with another measurement such as tyre forces that depend on unknown road friction. The new Kalman-based observer in this paper addresses this issue by considering tyre nonlinearities with a minimum number of required tyre parameters and the road condition as uncertainty. Longitudinal forces obtained by the unscented Kalman filter on the wheel dynamics is employed as an observation for the Kalman-based velocity estimator at each corner. The stability of the proposed time-varying estimator is investigated and its performance is examined experimentally in several tests and on different road surface frictions. Road experiments and simulation results show the accuracy and robustness of the proposed approach in estimating longitudinal speed for ground vehicles

Longitudinal vehicle state estimation using nonlinear and parameter-varying observers

The final publication is available at Elsevier via https://doi.org/10.1016/j.mechatronics.2017.02.004 © 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/A corner-based velocity estimation approach is proposed which is used for vehicle’s traction and stability control systems. This approach incorporates internal tire states within the vehicle kinematics and enables the velocity estimator to work for a wide range of maneuvers without road friction information. Tire models have not been widely implemented in velocity estimators because of uncertain road friction and varying tire parameters, but the current study utilizes a simplified LuGre model with the minimum number of tire parameters and estimates velocity robust to model uncertainties. The proposed observer uses longitudinal forces, updates the states by minimizing the longitudinal force estimation error, and provides accurate outcomes at each tire. The estimator structure is shown to be robust to road conditions and rejects disturbances and model uncertainties effectively. Taking into account the vehicle dynamics is time-varying, the stability of the observer for the linear parameter varying model is proved, time-varying observer gains are designed, and the performance is studied. Road test experiments have been conducted and the results are used to validate the proposed approach.Automotive Partnership Canada [APCPJ 395996-09], Ontario Research Fund [ORF-RE-04-039], General Motors Co

Online and Offline Identification of Tyre Model Parameters

The accelerating development of active safety system and autonomous vehicles put higher requirements on both environmental sensing and vehicle state estimation as well as virtual verification of these systems. The tyres are relevant in this context due to the considerable influence of the tyres on the vehicle motion and the performance boundaries set by the tyres. All forces that the driver use to control the vehicle are generated in the contact patch between the tyre and the road on a normal passenger car. Hence, the performance limits imposed by the tyres should ideally be considered in the active safety systems and in self-driving vehicles. Due to tyres influence on the vehicle motions, they are some of the key components that must be accurately modelled to correlate complete vehicle simulations models with physical testing.This thesis investigates the possibility to estimate the tyre-road friction coefficient during normal driving using active tyre force excitation, i.e. online identification of tyre model parameters. The thesis also investigates the possibility to scale tyre Force and Moment (F&M) models for complete vehicle simulations from indoor tests to real road surfaces using vehicle-based tyre testing, i.e. offline identification of tyre model parameters.For online identification of tyre model parameters, the focus has been on how to perform tyre force excitation to maximize the information about the tyre-road friction coefficient. Furthermore, the required excitation level, as a ratio of the maximum tyre-road friction coefficient, for different road surfaces and tyre models have been evaluated for a larger number of passenger car tyres. The thesis shows the feasibility and benefits of using active tyre force excitations and illustrates its benefits when estimating the tyre-road friction coefficient by identifying nonlinear tyre model parameters. The method shows promising results by offering tyre-road friction estimates when demanded by the driver or an on-board system. This system can also be combined with other tyre-road friction estimates to offer a continuous tyre-road friction estimate, e.g. through car-to-car communication.For offline identification of tyre model parameters, the focus was put on rescaling tyre models from indoor testing to a real-world road surface using vehicle-based tyre testing. Sensors were fitted to the vehicle to measure all inputs and outputs of the Pacejka 2002 tyre model. Furthermore, testing was performed on both different road surfaces and using different manoeuvres for tyre model identification. The effect on the complete vehicle behaviour in simulation when using tyre models based on different manoeuvres and road surfaces was investigated. The results show the importance of using a road surface and manoeuvre that are representative for the road surface and manoeuvre in which the vehicle will be evaluated. The sensitivity to different manoeuvres are mainly related to the changes in tyre properties with tyre surface temperature and the lack of temperature effects in the tyre model. The method shows promising results as an efficient way to rescale tyre models to a new road surface

Development of an Integrated Estimation Method for Vehicle States, Parameters and Tire Forces

Stability and desirable performance of vehicle control systems are directly dependent on the quality and accuracy of sensory and estimated data provided to the controllers. Tire forces and vehicle states such as lateral and longitudinal velocities are required information for most vehicle control systems. However, there are challenges associated with efficient estimation of tire forces and vehicle states. Furthermore, changes in vehicle inertial parameters, road grade, and bank angle all have major influences on both tire forces and vehicle states. Efficient identification of these parameters requires sufficient information about a set of vehicle states and tire forces. This duality relationship mandates the development of efficient methods for simultaneous estimation of tire forces, vehicle states, and vehicle and road parameters. This research proposes the design of an integrated estimation structure that can simultaneously estimate tire forces, vehicle velocity, vehicle inertial parameters, and road angles. The proposed structure is robust against variations in tire parameters because of tire brand, wear, and road friction coefficient. The methods developed in this thesis are all validated experimentally on multiple vehicle platform.4 month

State Estimation and Control of Active Systems for High Performance Vehicles

In recent days, mechatronic systems are getting integrated in vehicles ever more. While stability and safety systems such as ABS, ESP have pioneered the introduction of such systems in the modern day car, the lowered cost and increased computational power of electronics along with electrification of the various components has fuelled an increase in this trend. The availability of chassis control systems onboard vehicles has been widely studied and exploited for augmenting vehicle stability. At the same time, for the context of high performance and luxury vehicles, chassis control systems offer a vast and untapped potential to improve vehicle handling and the driveability experience. As performance objectives have not been studied very well in the literature, this thesis deals with the problem of control system design for various active chassis control systems with performance as the main objective. A precursor to the control system design is having complete knowledge of the vehicle states, including those such as the vehicle sideslip angle and the vehicle mass, that cannot be measured directly. The first half of the thesis is dedicated to the development of algorithms for the estimation of these variables in a robust manner. While several estimation methods do exist in the literature, there is still some scope of research in terms of the development of estimation algorithms that have been validated on a test track with extensive experimental testing without using research grade sensors. The advantage of the presented algorithms is that they work only with CAN-BUS data coming from the standard vehicle ESP sensor cluster. The algorithms are tested rigorously under all possible conditions to guarantee robustness. The second half of the thesis deals with the design of the control objectives and controllers for the control of an active rear wheel steering system for a high performance supercar and a torque vectoring algorithm for an electric racing vehicle. With the use of an active rear wheel steering, the driver’s confidence in the vehicle improves due a reduction in the lag between the lateral acceleration and the yaw rate, which allows drivers to push the vehicle harder on a racetrack without losing confidence in it. The torque vectoring algorithm controls the motor torques to improve the tire utilisation and increases the net lateral force, which allows professional drivers to set faster lap times

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

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018