Cooperative lateral vehicle guidance control for automated vehicles with Steer-by-Wire systems

With the global trend towards automated driving, fault-tolerant onboard power supply systems are introduced into modern vehicles and the level of driving automation is continuously increasing. These advancements contribute to the applicability of Steer-by-Wire systems and the development of automated lateral vehicle guidance control functions. For the market acceptance of automated driving, the lateral vehicle guidance control function must hereby be cooperative, that is it must accept driver interventions. Existing approaches for automated lateral vehicle guidance commonly do not consider driver interventions. If unconsidered in the control loop, the driver intervention is interpreted as an external disturbance that is actively compensated by feedback. This thesis addresses the development of a cooperative lateral vehicle guidance control concept, which enables a true coexistence between manual steering control by the driver and automated steering control. To this end, the subordinate controls of the Steer-by-Wire system for the manual and automated driving mode are initially presented. These include the steering feel generation and steering torque control of the Steer-by-Wire Handwheel Actuator for the manual driving mode, which is structurally extended to a cascade steering position control for the automated driving mode. Subsequently, a superposition control is introduced, which fuses steering torque and position control. The resulting cooperative Handwheel Actuator control achieves precise tracking of the reference steering position in automated driving mode but accepts driver interventions. Thus, the driver can override the active control and experiences a natural steering feel. The transitions hereby are seamless as no blending, gain scheduling or controller output saturation is required. Subsequently, the superimposed lateral vehicle guidance controller for the automated driving mode is described, which computes the reference steering position for the respective Steer-by-Wire controls. In contrast to existing approaches, the plant model equations are rearranged to isolate the vehicle speed dependent dynamics. Thereafter, the concept of inverse nonlinearity control is employed, using a virtual control loop and feedback linearization for an online inversion of the nonlinear plant dynamics. The remaining plant is fully linear and independent of vehicle speed. Consequently, one controller can be synthesized that is valid for all vehicle speeds. The closed and open loop system thereby have the same dynamics independent of vehicle speed, which significantly simplifies control synthesis, analysis, and performance tuning in the vehicle. For considering the future reference path information and constraints on the maximum steering position within the control law, a linear Model Predictive Controller synthesis is selected. The combination of inverse nonlinearity control and linear Model Predictive Controller thus results in a Nonlinear Adaptive Model Predictive Control concept, which makes commonly applied gain scheduling fully obsolete. The controller is structurally extended by a cooperative dynamic feedforward control for considering driver interventions within the control loop. Consequently, the driver can override the active control and seamlessly modify the lateral vehicle motion. A variety of nonlinear simulation analyses and real vehicle tests demonstrate the effectiveness of the proposed control concept

Integrated Vehicle Stability Control and Power Distribution Using Model Predictive Control

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

High-Speed Obstacle Avoidance at the Dynamic Limits for Autonomous Ground Vehicles

Enabling autonomy of passenger-size and larger vehicles is becoming increasingly important in both military and commercial applications. For large autonomous ground vehicles (AGVs), the vehicle dynamics are critical to consider to ensure vehicle safety during obstacle avoidance maneuvers especially at high speeds. This research is concerned with large-size high-speed AGVs with high center of gravity that operate in unstructured environments. The term `unstructured' in this context denotes that there are no lanes or traffic rules to follow. No map of the environment is available a priori. The environment is perceived through a planar light detection and ranging sensor. The mission of the AGV is to move from its initial position to a given target position safely and as fast as possible. In this dissertation, a model predictive control (MPC)-based obstacle avoidance algorithm is developed to achieve the objectives through an iterative simultaneous optimization of the path and the corresponding control commands. MPC is chosen because it offers a rigorous and systematic approach for taking vehicle dynamics and safety constraints into account. Firstly, this thesis investigates the level of model fidelity needed for an MPC-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles even when the vehicle is close to its dynamic limits. Five different representations of vehicle dynamics models are considered. It is concluded that the two Degrees-of-Freedom (DoF) representation that accounts for tire nonlinearities and longitudinal load transfer is necessary for the MPC-based obstacle avoidance algorithm to operate the vehicle at its limits within an environment that includes large obstacles. Secondly, existing MPC formulations for passenger vehicles in structured environments do not readily apply to this context. Thus, a novel nonlinear MPC formulation is developed. First, a new cost function formulation is used that aims to find the shortest path to the target position. Second, a region partitioning approach is used in conjunction with a multi-phase optimal control formulation to accommodate the complicated forms of obstacle-free regions from an unstructured environment. Third, the no-wheel-lift-off condition is established offline using a fourteen DoF vehicle dynamics model and is included in the MPC formulation. The formulation can simultaneous optimize both steering angle and reference longitudinal speed commands. Simulation results show that the proposed algorithm is capable of safely exploiting the dynamic limits of the vehicle while navigating the vehicle through sensed obstacles of different size and number. Thirdly, in the algorithm, a model of the vehicle is used explicitly to predict and optimize future actions, but in practice, the model parameter values are not exactly known. It is demonstrated that using nominal parameter values in the algorithm leads to safety issues in about one fourth of the evaluated scenarios with the considered parametric uncertainty distributions. To improve the robustness of the algorithm, a novel double-worst-case formulation is developed. Results from simulations with stratified random scenarios and worst-case scenarios show that the double-worst-case formulation considering both the most likely and less likely worst-case scenarios renders the algorithm robust to all uncertainty realizations tested. The trade-off between the robustness and the task completion performance of the algorithm is also quantified. Finally, in addition to simulation-based validation, preliminary experimental validation is also performed. These results demonstrate that the developed algorithm is promising in terms of its capability of avoiding obstacles. Limitations and potential improvements of the algorithm are discussed.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135770/1/ljch_1.pd

Real-time Trajectory Planning to Enable Safe and Performant Automated Vehicles Operating in Unknown Dynamic Environments

Need for increased automated vehicle safety and performance will exist until control systems can fully exploit the vehicle's maneuvering capacity to avoid collisions with both static and moving obstacles in unknown environments. A safe and performance-based trajectory planning algorithm exists that can operate an automated vehicle in unknown static environments. However, this algorithm cannot be used safely in unknown dynamic environments; furthermore, it is not real-time. Accordingly, this thesis addresses two overarching research questions: * How should a trajectory planning algorithm be formulated to enable automated ground vehicle safety and performance in unknown dynamic environments? * How can such an algorithm be solved in real-time? Safe trajectory planning for high-performance automated vehicles with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time to goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This thesis presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a high mobility multipurpose wheeled vehicle (HMMWV), that includes the above set of specifications. This formulation is tested then with various sets of these specifications in a known dynamic environment. In particular, a parametric study relating execution horizon and obstacle speed reveals that the moving obstacle avoidance specification is not needed for safety when the planner has a short execution horizon (< 0.375 s), and the obstacles are slow (< 2.11 m/s). However, a moving obstacle avoidance specification is needed when the obstacles move faster, and this specification improves safety without, in most cases, increasing solve-times. Overall, results indicate that trajectory planners for high-performance automated vehicles should include the entire set of specifications mentioned above unless a static or low-speed environment permits a less comprehensive planner. Then, this thesis combines this comprehensive planning algorithm with a suitable perception algorithm to enable safe and performant control of automated ground vehicles in unknown dynamic environments. A high-fidelity, ROS-based proving ground with a 2D LiDAR model, in Gazebo, and a 145 degree of freedom model of the HMMWV, in Chrono, is developed to combine these algorithms. Six-hundred tests, realized with various obstacle speeds and sizes, are performed in this proving ground in both known and unknown dynamic environments. Results from this comparison demonstrate that operating in an unknown environment, as opposed to a known environment, significantly increases collisions, steering effort, throttle effort, braking effort, orientation and tracking error, time to goal, and planner solve times. To avoid this deterioration of safety and performance factors in unknown environments, the use of more accurate perception systems should be explored. Ultimately, however, these results demonstrate that the comprehensive trajectory planning formulation developed in this thesis enables safe and performant control of automated vehicles in unknown dynamic environments among small (< 2 m) obstacles traveling at speeds up to high (20 m/s). To solve this formulation in real-time, an open-source, direct-collocation-based optimal control problem modeling language, called NLOptControl, is established in this thesis. Results demonstrate that NLOptControl can solve the formulation in real-time in both known and unknown environments. NLOptControl holds great potential for not only improving existing off-line and on-line control systems but also engendering a wide variety of new ones.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149859/1/febbo_1.pd