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.

Presented in a tutorial style, this comprehensive treatment unifies, simplifies, and explains most of the techniques for designing and analyzing adaptive control systems. Numerous examples clarify procedures and methods. 1995 edition.

This thesis addresses the problem of controlling the lateral motion of an autonomous vehicle in the presence of parametric uncertainties, disturbances, and hard nonlinearities in the steering system, such as backlash in gears, stiction, hysteresis, and dead zones. The lateral motion of an autonomous vehicle is controlled by two cascaded controllers, the trajectory tracking controller and the steering angle controller. This thesis focuses on the development of both controllers using robust and adaptive control techniques. Two control strategies are developed to control the electric power steering angle, sliding mode control and adaptive backstepping control. The limitation of sliding mode control is first addressed, which is the chattering phenomena, and then a proposed methodology is presented to solve it using variable gain sliding mode control. Self-aligning moment actas as disturbance on the steering system that the controller has to compensate for. A model-based approach to estimate it is first developed and its limitations are addressed, which is tire parameters dependence. Two other approaches are then developed to overcome these limitations, the first one is a sliding mode observer, and the second one is part of a backstepping controller. Two approaches are developed to control the vehicle lateral trajectory, non-adaptive backstepping and adaptive backstepping. The extended matching design procedure is used in the adaptive backstepping controller to avoid the overestimation problem. Road curvature must be accurately know by the controller to follow the planned trajectory. It is usually measured by a camera, but the quality of the measurement is affected by environmental factors. An adaptive law is developed to estimate the road curvature online as part of an adaptive backstepping controller. Two feedforward approaches are presented to compensate for road curvature, one is derived from steady state vehicle lateral dynamics, and another is based on estimating the transfer function dynamics from road curvature to steering angle. Road bank angle is a significant disturbance in vehicle lateral control systems. A vehicle lateral state and disturbance observer is developed to estimate the road bank angle and the vehicle side slip angle, which are expensive to measure in current road vehicles, using extended Kalman filter. The observer combines a dynamical vehicle model with two measurements from inexpensive sensors.

Document from the year 2020 in the subject Engineering - Automotive Engineering, grade: 2, , language: English, abstract: Global chassis controller (GCC) design for autonomous vehicles relies on the information of the environmental factors, weather conditions, vehicle dynamics, actuation bandwidth, among others. Typically, various sensors and actuators are employed to provide such information. Challenges such as cost of sensors, actuator complexity and constraints, fail-safe operations, control authority allocation, and adaptability to a wide range of driving scenarios such as acceleration/ deceleration at set speed, double lane change, and driving on a circular path among others persist for design of such GCC architectures. Specifically for longitudinal-vertical vehicle controllers tuned to achieve safety and comfort objectives, the performance is significantly affected by the precise knowledge of road conditions i.e., tire friction and road elevation in the presence of nonlinearities such as aerodynamic drag, rolling resistance, spring and damper nonlinearities. For the longitudinal vehicle motion, tire-road friction conditions, aerodynamic forces, engine friction, and rolling nonlinearities critically affect the design of safety controllers such as traction control or active cruise control. Similarly, for vertical vehicle motion control using active suspension, the random road roughness and road defects, spring and damper nonlinearities, hydraulic actuator nonlinearities, and multi-objective design criteria, make design of controller a challenging task. With that motivation, the use cost effective virtual sensors to detect such external inputs and subsequent output feedback control solutions for the longitudinal-vertical autonomous vehicle motion is proposed in this book. The focus lies on adaptability of designed controllers and estimators to road friction conditions such as road conditions such as asphalt, snow, ice and the road elevation based on various rough roads and road defects.

The robotic mechanism and its controller make a complete system. As the robotic mechanism is reconfigured, the control system has to be adapted accordingly. The need for the reconfiguration usually arises from the changing functional requirements. This book will focus on the adaptive control of robotic manipulators to address the changed conditions. The aim of the book is to summarise and introduce the state-of-the-art technologies in the field of adaptive control of robotic manipulators in order to improve the methodologies on the adaptive control of robotic manipulators. Advances made in the past decades are described in the book, including adaptive control theories and design, and application of adaptive control to robotic manipulators.

Robust and Adaptive Control shows the reader how to produce consistent and accurate controllers that operate in the presence of uncertainties and unforeseen events. Driven by aerospace applications the focus of the book is primarily on continuous-dynamical systems. The text is a three-part treatment, beginning with robust and optimal linear control methods and moving on to a self-contained presentation of the design and analysis of model reference adaptive control (MRAC) for nonlinear uncertain dynamical systems. Recent extensions and modifications to MRAC design are included, as are guidelines for combining robust optimal and MRAC controllers. Features of the text include: · case studies that demonstrate the benefits of robust and adaptive control for piloted, autonomous and experimental aerial platforms; · detailed background material for each chapter to motivate theoretical developments; · realistic examples and simulation data illustrating key features of the methods described; and · problem solutions for instructors and MATLAB® code provided electronically. The theoretical content and practical applications reported address real-life aerospace problems, being based on numerous transitions of control-theoretic results into operational systems and airborne vehicles that are drawn from the authors’ extensive professional experience with The Boeing Company. The systems covered are challenging, often open-loop unstable, with uncertainties in their dynamics, and thus requiring both persistently reliable control and the ability to track commands either from a pilot or a guidance computer. Readers are assumed to have a basic understanding of root locus, Bode diagrams, and Nyquist plots, as well as linear algebra, ordinary differential equations, and the use of state-space methods in analysis and modeling of dynamical systems. Robust and Adaptive Control is intended to methodically teach senior undergraduate and graduate students how to construct stable and predictable control algorithms for realistic industrial applications. Practicing engineers and academic researchers will also find the book of great instructional value.