Vehicle Stability Control Considering the Driver-in-the-Loop

A driver‐in‐the‐loop modeling framework is essential for a full analysis of vehicle stability systems. In theory, knowing the vehicle’s desired path (driver’s intention), the problem is reduced to a standard control system in which one can use different methods to produce a (sub) optimal solution. In practice, however, estimation of a driver’s desired path is a challenging – if not impossible – task. In this thesis, a new formulation of the problem that integrates the driver and the vehicle model is proposed to improve vehicle performance without using additional information from the future intention of the driver. The driver’s handling technique is modeled as a general function of the road preview information as well as the dynamic states of the vehicle. In order to cover a variety of driving styles, the time‐ varying cumulative driver's delay and model uncertainties are included in the formulation. Given that for practical implementations, the driver’s future road preview data is not accessible, this information is modeled as bounded uncertainties. Subsequently, a state feedback controller is designed to counteract the negative effects of a driver’s lag while makes the system robust to modeling and process uncertainties. The vehicle’s performance is improved by redesigning the controller to consider a parameter varying model of the driver‐vehicle system. An LPV controller robust to unknown time‐varying delay is designed and the disturbance attenuation of the closed loop system is estimated. An approach is constructed to identify the time‐varying parameters of the driver model using past driving information. The obtained gains are clustered into several modes and the transition probability of switching between different driving‐styles (modes) is calculated. Based on this analysis, the driver‐vehicle system is modeled as a Markovian jump dynamical system. Moreover, a complementary analysis is performed on the convergence properties of the mode‐dependent controller and a tighter estimation for the maximum level of disturbance rejection of the LPV controller is obtained. In addition, the effect of a driver’s skills in controlling the vehicle while the tires are saturated is analyzed. A guideline for analysis of the nonlinear system performance with consideration to the driver’s skills is suggested. Nonlinear controller design techniques are employed to attenuate the undesirable effects of both model uncertainties and tire saturation

Multiple-Model Robust Adaptive Vehicle Motion Control

An improvement in active safety control systems has become necessary to assist drivers in unfavorable driving conditions. In these conditions, the dynamic of the vehicle shows rather different respond to driver command. Since available sensor technologies and estimation methods are insufficient, uncertain nonlinear tire characteristics and road condition may not be correctly figured out. Thus, the controller cannot provide the appropriate feedback input to vehicle, which may result in deterioration of controller performance and even in loss of vehicle control. These problems have led many researchers to new active vehicle stability controllers which make vehicle robust against critical driving conditions like harsh maneuvers in which tires show uncertain nonlinear behaviour and/or the tire-road friction coefficient is uncertain and low. In this research, the studied vehicle has active front steering system for driver steer correction and in-wheel electric motors in all wheels to generate torque vector at vehicle center of gravity. To address robustness against uncertain nonlinear characteristics of tire and road condition, new blending based multiple-model adaptive schemes utilizing gradient and recursive least squares (RLS) methods are proposed for a faster system identification. To this end, the uncertain nonlinear dynamics of vehicle motion is addressed as a multiple-input multiple-output (MIMO) linear system with polytopic parameter uncertainties. These polytopic uncertainties denote uncertain variation in tire longitudinal and lateral force capacity due to nonlinear tire characteristics and road condition. In the proposed multiple-model approach, a set of fixed linear parametric identifi cation models are designed in advance, based on the known bounds of polytopic parameter set. The proposed adaptive schemes continuously generates a weighting vector for blending the identifi cation model to achieve the true model (operation condition) of the vehicle. Furthermore, the proposed adaptive schemes are generalized for MIMO systems with polytopic parameter uncertainties. The asymptotic stability of the proposed adaptive identifi cation schemes for linear MIMO systems is studied in detail. Later, the proposed blending based adaptive identi fication schemes are used to develop Linear Quadratic (LQ) based multiple-model adaptive control (MMAC) scheme for MIMO systems with polytopic parameter uncertainties. To this end, for each identi fication model, an optimal LQ controller is computed on-line for the corresponding model in advance, which saves computation power during operation. The generated control inputs from the set of LQ controllers is being blended on-line using weighting vector continuously updated by the proposed adaptive identifi cation schemes. The stability analysis of the proposed LQ based optimal MMAC scheme is provided. The developed LQ based optimal MMAC scheme has been applied to motion control of the vehicle. The simulation application to uncertain lateral single-track vehicle dynamics is presented in Simulink environment. The performances of the proposed LQ based MMAC utilizing RLS and gradient based methods have been compared to each other and an LQ controller which is designed using the same performance matrices and fixed nominal values of the uncertain parameters. The results validated the stability and effectiveness of the proposed LQ based MMAC algorithm and demonstrate that the proposed adaptive LQ control schemes outperform over the LQ control scheme for tracking tasks. In the next step, we addressed the constraints on actuation systems for a model predictive control (MPC) based MMAC design. To determine the constraints on torque vectoring at vehicle center of gravity (CG), we have used the min/max values of torque and torque rate at each corner, and the vehicle kinematic structure information. The MPC problem has been redefi ned as a constrained quadratic programming (QP) problem which is solved in real-time via interior-point algorithm by an embedded QP solver using MATLAB each time step. The solution of the designed MPC based MMAC provides total steering angle and desired torque vector at vehicle CG which is optimally distributed to each corner based on holistic corner control (HCC) principle. For validation of the designed MPC based MMAC scheme, several critical driving scenarios has been simulated using a high- fidelity vehicle simulation environment CarSim/Simulink. The performance of the proposed MPC based MMAC has been compared to an MPC controller which is designed for a wet road condition using the same tuning parameters in objective function design. The results validated the stability and effectiveness of the proposed MPC based MMAC algorithm and demonstrate that the proposed adaptive control scheme outperform over an MPC controller with fixed parameter values for tracking tasks

Optimal torque vectoring control strategies for stabilisation of electric vehicles at the limits of handling

The study of chassis control has been a major research area in the automotive industry and academia for more than fifty years now. Among the popular methods used to actively control the dynamics of a vehicle, torque vectoring, the method of controlling both the direction and the magnitude of the torque on the wheels, is of particular interest. Such a method can alter the vehicle’s behaviour in a positive way under both sub-limit and limit handling conditions and has become even more relevant in the case of an electric vehicle equipped with multiple electric motors. Torque vectoring has been so far employed mainly in lateral vehicle dynamics control applications, with the longitudinal dynamics of the vehicle remaining under the full authority of the driver. Nevertheless, it has been also recognised that active control of the longitudinal dynamics of the vehicle can improve vehicle stability in limit handling situations. A characteristic example of this is the case where the driver misjudges the entry speed into a corner and the vehicle starts to deviate from its path, a situation commonly referred to as a ‘terminal understeer’ condition. Use of combined longitudinal and lateral control in such scenarios have been already proposed in the literature, but these solutions are mainly based on heuristic approaches that also neglect the strong coupling of longitudinal and lateral dynamics in limit handling situations. The main aim of this project is to develop a real-time implementable multivariable control strategy to stabilise the vehicle at the limits of handling in an optimal way using torque vectoring via the two independently controlled electric motors on the rear axle of an electric vehicle. To this end, after reviewing the most important contributions in the control of lateral and/or longitudinal vehicle dynamics with a particular focus on the limit handling solutions, a realistic vehicle reference behaviour near the limit of lateral acceleration is derived. An unconstrained optimal control strategy is then developed for terminal understeer mitigation. The importance of constraining both the vehicle state and the control inputs when the vehicle operates at the limits of handling is shown by developing a constrained linear optimal control framework, while the effect of using a constrained nonlinear optimal control framework instead is subsequently examined next. Finally an optimal estimation strategy for providing the necessary vehicle state information to the proposed optimal control strategies is constructed, assuming that only common vehicle sensors are available. All the developed optimal control strategies are assessed not only in terms of performance but also execution time, so to make sure they are implementable in real time on a typical Electronic Control Unit

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