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The motivation for the development of vehicle stability control systems comes from the fact that vehicle dynamic behavior in unfavorable driving conditions such as low road-tire adhesion and high speed differs greatly from its nominal behavior. Due to this unexpected behavior, a driver may not be successful in controlling the vehicle in challenging driving situations based only on her/his everyday driving experience. Several noteworthy research works have been conducted on stability control systems over the last two decades to prevent car accidents due to human error. Most of the resultant stability controllers contain individual modules, where each perform a particular task such as yaw tracking, sideslip control, or wheel slip control. These design requirements may contradict each other in some driving scenarios. In such situations, inconsistent control actions can be generated with individual modules. The development of a stability controller that can satisfy diverse and often contradictory requirements is a great challenge. In general, transferring a control structure from one vehicle to another with a different drivetrain layout and actuation system configuration requires remarkable rectifications and repetition of tuning processes from the beginning to achieve a similar performance. This can be considered to be a serious drawback for car manufacturing companies since it results in extra effort, time, and expenses in redesigning and retuning the controller. In this thesis, an integrated controller with a modular structure has been designed to concurrently provide control of the vehicle chassis (yaw rate and sideslip control) and wheel stability (wheel slip ratio control). The proposed control structure incorporates longitudinal and lateral vehicle dynamics to decide on a unified control action. This control action is an outcome of solving an optimization problem that considers all the control objectives in a single cost function, so integrated wheel and vehicle stability is guaranteed. Moreover, according to the particular modular design of the proposed control structure, it can be easily reconfigured to work with different drivetrain layouts such as all-wheel-drive, front-wheel-drive, and rear-wheel-drive, as well as various actuators such as torque vectoring, differential braking, and active steering systems. The high-level control module provides a Center of Gravity (CG) based error analysis and determines the required longitudinal forces and yaw moment adjustments. The low-level control module utilizes this information to allocate control actions optimally at each vehicle corner (wheel) through a single or multi-actuator regime. In order to consider the effect of the actuator dynamics, a mathematical description of the auction system is included in distribution objective function. Therefore, a legitimate control performance is promised in situations requiring shifting from one configuration to another with minimal modifications. The performance of the proposed modular control structure is examined in simulations with a high-fidelity model of an electric GM Equinox vehicle. The high-fidelity model has been developed and provided by GM and the use of the model is to reduce the number of labor-intensive vehicle test and is to test extreme and dangerous driving conditions. Several driving scenarios with severe steering and throttle commands, then, are designed to evaluate the capability of the proposed control structure in integrated longitudinal and lateral vehicle stabilization on slippery road condition. Experimental tests also have been performed with two different electric vehicles for real-time implementation as well as validation purposes. The observations verified the performance qualifications of the proposed control structure to preserve integrated wheel and vehicle chassis stability in all track tests.
A comprehensive overview of integrated vehicle system dynamics exploring the fundamentals and new and emerging developments This book provides a comprehensive coverage of vehicle system dynamics and control, particularly in the area of integrated vehicle dynamics control. The book consists of two parts, (1) development of individual vehicle system dynamic model and control methodology; and (2) development of integrated vehicle dynamic model and control methodology. The first part focuses on investigating vehicle system dynamics and control according to the three directions of vehicle motions, including longitudinal, vertical, and lateral. Corresponding individual control systems, e.g. Anti-lock Brake System (ABS), Active Suspension, Electric Power Steering System (EPS), are introduced and developed respectively. Particular attention is paid in the second part of the book to develop integrated vehicle dynamic control system. Integrated vehicle dynamics control system is an advanced system that coordinates all the chassis control systems and components to improve the overall vehicle performance including safety, comfort, and economy. Integrated vehicle dynamics control has been an important research topic in the area of vehicle dynamics and control over the past two decades. The research topic on integrated vehicle dynamics control is investigated comprehensively and intensively in the book through both theoretical analysis and experimental study. In this part, two types of control architectures, i.e. centralized and multi-layer, have been developed and compared to demonstrate their advantages and disadvantages. Integrated vehicle dynamics control is a hot topic in automotive research; this is one of the few books to address both theory and practice of integrated systems Comprehensively explores the research area of integrated vehicle dynamics and control through both theoretical analysis and experimental study Addresses a full range of vehicle system topics including tyre dynamics, chassis systems, control architecture, 4 wheel steering system and design of control systems using Linear Matrix Inequality (LMI) Method
To resolve the urban transportation challenges like congestion, parking, fuel consumption, and pollution, narrow urban vehicles which are small in footprint and light in their gross weight are proposed. Apart from the narrow cabin design, these vehicles are featured by their active tilting system, which automatically tilts the cabin like a motorcycle during the cornering for comfort and safety improvements. Such vehicles have been manufactured and utilized in city commuter programs. However, there is no book that systematically discusses the mechanism, dynamics, and control of narrow tilting vehicles (NTVs). In this book, motivations for building NTVs and various tilting mechanisms designs are reviewed, followed by the study of their dynamics. Finally, control algorithms designed to fully utilize the potential of tilting mechanisms in narrow vehicles are discussed. Special attention is paid to an efficient use of the control energy for rollover mitigation, which greatly enhance the stability of NTVs with optimized operational costs.
This thesis reports on novel methods for gain-scheduling and fault tolerant control (FTC). It begins by analyzing the connection between the linear parameter varying (LPV) and Takagi-Sugeno (TS) paradigms. This is then followed by a detailed description of the design of robust and shifting state-feedback controllers for these systems. Furthermore, it presents two approaches to fault-tolerant control: the first is based on a robust polytopic controller design, while the second involves a reconfiguration of the reference model and the addition of virtual actuators into the loop. Inaddition the thesis offers a thorough review of the state-of-the art in gain scheduling and fault-tolerant control, with a special emphasis on LPV and TS systems.
There is a growing need for active safety systems to assist drivers in unfavorable driving conditions. In these conditions, the behavior of the vehicle is different than the linear response during everyday driving. Even experienced drivers usually lose control of the vehicle in such situations and that often results in a car accident. Stability control systems have been developed over the past few decades to assist drivers in keeping the vehicle under control. Most of these control systems are comprised of separate modules, each responsible for one task such as yaw rate tracking, sideslip control, traction control or power distribution. These objectives may be in conflict in some driving situations. In such cases, individual controllers fight over priority and produce conflicting control commands, to the detriment of the vehicle performance. In addition, in most stability control systems, transferring the controller from one vehicle to another with a different driveline and actuator configuration requires significant modifications in the controller and major re-tuning to obtain a similar performance. This is a major disadvantage for auto companies and increases the controller design and tuning costs. In this thesis, an integrated control system has been designed to address vehicle stability, traction control and power distribution objectives at the same time. The proposed controller casts all of these objectives in a single objective function and chooses control actions to optimize this objective function. Therefore, the output of the integrated controller is not altered by another module and the optimality of the solution is not compromised. Furthermore, the designed controller can be easily reconfigured to work with various driveline configurations such as all-wheel drive, front or rear-wheel drive. In addition, it can also work with various actuator configurations such as torque vectoring, differential braking or any combination of them on the front or rear axles. Moving from one configuration to another does not change the stability control performance and major re-tuning can be avoided. The performance of the designed model predictive controller is evaluated in software simulations with a high fidelity model of an electric Equinox vehicle. The stability and wheel slip control performance of the controller is evaluated in various driving and road conditions. In addition, the effect of integrated power distribution is studied. Experimental tests with two different electric vehicles are also carried out to evaluate the real-time performance of the MPC controller. It is observed that the controller is able to maintain vehicle and wheel stability in all of the driving scenarios considered. The power distribution system is able to improve vehicle efficiency by approximately 1.5% and acts in cooperation with the stability control objectives.
Motion control as well as control of vehicle's dynamic performance represents a very delicate and challenging task from the point of view of control system design. Namely, it is necessary to ensure that the control simultaneously satisfies several requirements such as: global stability, ride quality, ride comfort, minimal dynamic loads of the mechanical subsystems, low energy consumption, etc. The choice of the appropriate control strategy and the way of its realization represent a crucial problem the solution of which demands sufficiently deep knowledge of dynamic behaviour of road vehicle in various motion conditions. There are several books dealing with modelling and automotive control. However, the control algorithms described in them are prevailingly based on the so-called decentralized principle, using most often simplified, planar vehicle models and the common control techniques - robust local controllers. Thus, up to now, there has been no text considering the centralized approach to practical vehicle stability analysis. Because of that, the goal of this book is to stress out the importance of knowledge of vehicle dynamics, the benefits of implementation of the integrated control in advanced vehicle controllers, as well as the importance of system's stability analysis in the synthesis of dynamic control laws. Based on the entire vehicle dynamics, two novelties are introduced: (i) integrated dynamic control of road vehicles based on a centralized control approach and (ii) practical stability analysis of the vehicle system. The author: Dr. Aleksandar D. Rodic was born in Belgrade, Yugoslavia, in 1960. His main interest is in modelling, system identification, simulation and control of large-scale dynamic systems. His special interest includes design of integrated and intelligent control algorithms of road vehicles operating with driver assisted control systems. He is the author of more then 40 scientific papers in leading international journals and proceedings of scientific meetings. He is scientific consultant of several international journals. He is winner of the UNIDO/UNDP and of the Alexander von Humboldt Research Fellowship. Prof. Dr. Miomir K. Vukobratovic was born in Zrenjanin, Yugoslavia, 1931. His main interest is the development of efficient modelling of robotic systems' dynamics. Special interest is in modelling and control of legged locomotion robots and active systems. He is the author of more than 20 scientific books and monographs as well as more than 500 papers in world-recognized international journals or conference proceedings. He is a member of many international scientific committees. He is the President of the Yugoslav Engineering Academy, member of Serbian and foreign member of Russian Academy of Sciences. He is an honoured Professor and Doctor Honoris Causa of several universities. He is holder of several international awards for professional activities.
This monograph focuses on control methods that influence vehicle dynamics to assist the driver in enhancing passenger comfort, road holding, efficiency and safety of transport, etc., while maintaining the driver’s ability to override that assistance. On individual-vehicle-component level the control problem is formulated and solved by a unified modelling and design method provided by the linear parameter varying (LPV) framework. The global behaviour desired is achieved by a judicious interplay between the individual components, guaranteed by an integrated control mechanism. The integrated control problem is also formalized and solved in the LPV framework. Most important among the ideas expounded in the book are: application of the LPV paradigm in the modelling and control design methodology; application of the robust LPV design as a unified framework for setting control tasks related to active driver assistance; formulation and solution proposals for the integrated vehicle control problem; proposal for a reconfigurable and fault-tolerant control architecture; formulation and solution proposals for the plug-and-play concept; detailed case studies. Robust Control Design for Active Vehicle Assistance Systems will be of interest to academic researchers and graduate students interested in automotive control and to control and mechanical engineers working in the automotive industry. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Multi-axle vehicles, such as trucks and buses, have been playing a vital role in trucking industry, public transportation system, and long-distance transport services. However, at the same time, statistics suggest more than one million lives are lost in road accidents each year over the world. The high adoption and utilization of multi-axle vehicles hold a significant portion of road accidents and death. To improve the active safety of vehicles, active systems have been developed and commercialized over the last decades to augment the driver's actions. However, unlike two-axle vehicles (e.g., passenger cars), multi-axle vehicles come in a rich diversity and variety to meet with many different transportation needs. Specifically, vehicle configurations are seen in different numbers of axles, numbers of articulations, powertrain modes, and active actuation systems. In addition, multi-axle vehicles are usually articulated, which makes the dynamics and control more complex and challenging as more instability modes appear, such as, trailer sway and jackknife. This research is hence motivated by an essential question: how can a universal and reconfigurable control system be developed for any multi-axle/articulated vehicle with any configuration? Leveraging the matrix approach and optimization-based techniques, this thesis developed a reconfigurable and universal modeling and control framework to this aim. Specifically, a general dynamics modeling that unifies any multi-axle and articulated vehicles in one formulation is developed in an intuitive manner. It defines the `Boolean Matrices' to determine any configuration of the articulation, the number of axles, and the active actuation systems. In this way, the corresponding dynamics model can be easily and quickly formulated when axles, articulations or actuators are added or removed. The general modeling serves to achieve the universality and reconfigurability in controller design. Therefore, a hierarchical, i.e., two-layer, control system is proposed. In the high layer, the optimization process of a model predictive control (MPC) calculates corrective Center of Gravity (CG) forces/moments, which are universal to any vehicle. The lower-level controller is achieved by a Control Allocation (CA) algorithm. It aims to realize the MPC commands by regulating the steering or torque (driving or braking) at each wheel optimally. In addition, the optimization takes into account real-time constraints, such as actuator limits, tire capacity, wheel slips, and actuators failure. Simulations are conducted on different vehicle configurations to evaluate control performance, reconfigurability, and robustness of the system. Additionally, to evaluate the real-time performance of the developed controller, experimental validation is carried out on an articulated vehicle with multiple configurations of differential braking systems. It is observed that the controller is very effective in dynamics control and has a promising reconfigurability when moving from one configuration to another.