LEE-Makina Mühendisliği-Doktora
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ÖgeCollision avoidance and crash mitigation via intelligent steering intervention( 2020) Şahin, Hasan ; Akalın, Özgen ; 636992 ; Makine Mühendisliği Ana Bilim DalıThe first aim of the thesis is to reduce the collision in traffic accidents or to mitigate the collision when it can not be reduced. Collision reduction can be achieved by braking in the first place. However, a suitable distance is required to achieve braking. In this thesis, the steering escape maneuver is processed instead of braking. The distance required for steering escape maneuver is less than the distance required for braking if the relative speed between vehicles is greater than 50 km/h. Therefore, at high speeds, if the braking distance is missed, the steering escape maneuver should be considered. For the steering escape maneuver, lane identification must be made in the transition to the side lane. After the lane definition has been made, the stability limits of the vehicle should also be taken into account in order to change the lane appropriately. Stability limits vary from vehicle to vehicle. In this thesis, the stability limits of different types of vehicles are considered by using various tools in the related simulation programs. The stability to be considered in the escape maneuver is the lateral stability. Verification of lateral stability is very important. We can only do this using a nonlinear simulation model. Nonlinear conditions are also mentioned in the simulations. The next step in the escape maneuver is to properly control the escape lane. It should also be checked whether the strip is suitable for passing. There are various systems for controlling this. In the simulations, the simulations have been completed by considering some of these systems. The whole thesis consists of four separate chapters. In the first part of the thesis, an adaptive trajectory control (ATC) in case of a sudden change in μmax (maximum road friction coefficient) during an emergency lane change manoeuvre is explained. The ATC system was analysed in case of a sudden change in the maximum friction coefficient of road during an emergency lane change manoeuvre in order to maintain the driving safety. Autonomous front wheel steering (FWS) systems have been developed for emergency steering situations. The trajectory design is also a part of these systems. Moreover, in this study ATC has been designed by sensing μmax to complete the emergency steering manoeuvre successfully. Therefore, the originality of this work arises from the necessity of a trajectory change in case of a sudden change in μmax to minimize the distance between the desired and the actual path. Suitable cases were designed by using a two-track model in IPG/CarMaker (MATLAB/Simulink). Results show that ATC could be used during an emergency steering manoeuvre in case of a sudden decrease in μmax as it can be advantageous in certain critical traffic situations. Therefore, ATC could be used as an alternative system instead of Electronic Stability Program. In the second part of the thesis, a driver model supported by a rear wheel steering (RWS) assistance to minimize the distance between the desired path and actual path via steering "out-of-phase" during an emergency lane change maneuver is explained. Rear wheel steering (RWS) systems are commonly used to maintain vehicle lateral stability via steering "in-phase" at high speeds. Conventional RWS systems do not assist the driver to avoid rear-end collisions. However, in this study, a RWS assistance is proposed to avoid rear-end collisions. A driver model is supported by a RWS assistance via steering "out-of-phase" during an emergency lane change maneuver. The proposed RWS assistance uses a yaw rate feedback controller and a disturbance controller. A two-track vehicle model was used where experimental validation studies are widely available. The originality of this paper is using an intelligent RWS assistance to avoid rear-end collisions rather than improving the vehicle lateral stability. The results demonstrate that the intelligent RWS assistance supporting the driver model can both reduce rear-end collisions and also their impacts. The vehicle lateral stability can be maintained depending on the coefficient of road adhesion and distance to obstacle. In the third part of the thesis, the effectiveness of a steering warning system (SWS) for the decrease of tendency of emergency braking maneuvers is explained. The viability of an Emergency Steering Warning System was analysed to improve the safety of vehicles on highways traveling in the same direction. The proposed system evaluates the vehicle's physical limits, driver's reaction and assists in making the most logical decision to avoid a crash using a sound or a similar stimulus. Typical driving simulator events were designed in MATLAB/Simulink and IPG/CarMaker co-simulation environment. In the predetermined scenario, the leading vehicles suddenly move into the host vehicle's lane and the driver is expected to avoid crash by either steering or braking. The SWS system generates a sound stimulus when it is determined that the crash is unavoidable with the use of service brakes and the only way to avoid the obstacle is steering. The simulation events were performed by a group of participants using a driver simulator with and without the SWS system. The proposed SWS encouraged participants to do an earlier and smoother steering maneuver which can be advantageous in some certain critical traffic situations. The statistical results showed that the sound stimulus reduced the drivers' reaction time significantly and a number of accidents can be avoided by the suggested crash warning system. In the final part of the thesis, an articulated vehicle lateral stability management (AVLS) via active rear wheel steering of tractor using fuzzy logic and model predictive control is explained. In-phase rear wheel steering, where rear wheels are steered in the same direction of front wheels, has been widely investigated in the literature for vehicle stability improvements along with stability control systems. Much faster response can be achieved by steering the rear wheels automatically during an obstacle avoidance maneuver without applying the brakes where safe stopping distance is not available. Sudden lane change movements still remain challenging for heavy articulated vehicles, such as tractor and semi-trailer combinations, particularly on roads with low coefficient of adhesion. Different lateral accelerations acting on tractor and semi-trailer may cause loss of stability resulting in jackknifing, trailer-swing, roll-over or slip-off. Several attempts have been made in the literature to use active steering of semi-trailer's rear wheels to prevent jackknifing and rollover. However, loss of stability in an articulated vehicle is usually caused by an over-steered tractor, and the semitrailer's rear wheels have little effect on the tractor's directional control. In this study, viability of active rear wheel steering of tractor to maintain the stability of an articulated vehicle during a high speed obstacle avoidance maneuver is investigated. Two different controllers, fuzzy logic and linear model based predictive controllers are proposed to minimize off-tracking behavior of an articulated vehicle. The controllers were tested in IPG/TruckMaker environment with MATLAB/Simulink interface on roads with various coefficient of adhesions, performing single lane change maneuvers. The simulated results showed that jackknifing occurring right after sudden lane changes can be successfully prevented using tractor's active rear wheel steering based on model predictive control algorithm when the feedback gains are tuned correctly.
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ÖgeEvaluation of model-based predictive control methods in high-speed automated ground vehicle path following(Graduate School, 2024-06-25) Yangın, Volkan Bekir ; Akalın, Özgen ; 503182020 ; Mechanical EngineeringEnsuring safe driving in autonomous vehicles is one of the most important requirements. Providing trajectory tracking plays a key role in this issue. A number of technological instruments such as electronic anti-skid systems, electronic stability programs and electric-hydraulic-supported steering systems are used to obtain the safest trajectory for a vehicle. These systems developed by automotive manufacturers are controlled by different methods. For example, proportional-integral-derivative (PID) control, Fuzzy logic, linear-quadratic-regulator (LQR), linear-quadratic-integrator (LQI), artificial neural networks, H-infinity and model predictive control are utilized for directional control in vehicles. With the help of these control methods, parameters such as steering angle and/or wheel moments are optimized, and human intervention in driving is partially or completely prevented. Model predictive control (MPC), one of the listed methods, differs from other control methods due to its suitability for autonomous driving systems. It is a model-based control method. In this approach, the control action is provided by considering the difference between the predicted and current outputs of the system. The control signal is generated according to the selection of the MPC parameters (prediction horizon, control horizon, appropriate sampling time and weighting parameters). In addition, the MPC is capable of working with multiple system and controller outputs, multiple constraints on the control signal and multiple set-points. Furthermore, the base model of the MPC can be in linear or nonlinear formulations. In linear MPC systems, the base model formulation remains constant throughout the control action. In nonlinear MPC systems, the base model is updated at each sampling depending on the outputs of the system. With this implementation, nonlinear and complex systems can be controlled with MPC. Due to these advantages, MPC is frequently used in solving of trajectory tracking problems of the autonomous vehicles. In this Ph.D. thesis, the MPC-based control systems for a military vehicle cruising at constant speed in the NATO double lane change (DLC) maneuver are presented, considering successful trajectory tracking and fast control signal generation. This vehicle is entitled "system" and it is represented by a two-track nonlinear vehicle model. The system model has 4 degrees of freedom in the vehicle body (longitudinal motion, lateral motion, yaw motion and roll motion) and 8 degrees of freedom in each wheel (rotation). The inputs to the system are the steering angle for the front wheels and the torque outputs of each wheel. Magic Formula (2002) version is used to model the tires of the vehicle. This semi-experimental model is used to calculate the lateral and longitudinal forces that can be produced by the tire using the experimentally-determined tire parameters. The friction between the tire and ground and combined slip properties are also considered in the tire modelling. The system model has been validated using the experimental data generated by a NATO Research Task Group on Applied Vehicle Technology. The data include outputs such as steering wheel angle, wheel moments, lateral acceleration, roll angle, roll rate, yaw angle, yaw rate and global position. Six control systems based on discrete-time MPC are developed. These are "linear MPC", "adaptive nonlinear MPC" (with two tuning methods), "robust linear MPC", "classical nonlinear MPC with multiple controller outputs" and "explicit tunable nonlinear MPC". In all controllers, the vehicle model with two degrees of freedom is used as the base model. The degrees of freedom are the yaw motion of the body and the lateral motion of the center of gravity. In all controller designs, except for the "classical nonlinear MPC" and "explicit tunable nonlinear MPC" designs, the base model is configured as a single-track vehicle model. In addition, the steering angle is used as the only control signal. In the "classical nonlinear MPC" and "explicit tunable nonlinear MPC" designs, the base model is a two-track vehicle model. The steering angle and direct yaw moment are determined in coordination. In the "explicit tunable nonlinear MPC" design, the control law is not obtained using online approaches. However, in other controllers, the control signal is generated online by minimizing the cost function in the MPC structure. In all controller designs, the lateral position of the center of gravity and the yaw angle of the body are used as desired system outputs. In addition, the system model is always assumed to be at constant speed during the DLC maneuver. In the first controller design, "linear MPC", the system is expected to approach the set points at two different speeds. The base model of the controller is linear. No tuning method is used to determine the controller parameters. In the simulation studies, it is observed that the "linear MPC" can provide improved trajectory tracking performance compared to the proportional-integral-derivative (PID) control method. In the second controller design, "adaptive nonlinear MPC (version 1)", it is aimed to increase the experimentally-determined maximum NATO DLC speed of the system. The parameters of this controller are tuned by artificial neural networks (ANN) and determined online at each sampling instant according to the outputs of the system. In addition, during the control signal generation phase, the nonlinear base model is linearized at each sampling instant. This process is completed by considering the instantaneous values of the control signal and the system states. These features make the controller adaptive. Steering angle is the only control signal. Simulations show that the maximum NATO DLC speed can be increased using "adaptive nonlinear MPC (version 1)" compared to experiments and PID controller, without lateral skidding, wheel lift-off and roll-over. The third controller design, "adaptive and nonlinear MPC (version 2)", is an enhanced version of the "adaptive and nonlinear MPC (version 1)". The weights of this controller are tuned using a combined structure of ANN and the Big Bang-Big Crunch (BB-BC) algorithm. It includes 2 layers: In the first layer, controller weights are determined by ANN. These weights are then evaluated as the initial center of mass in the first iteration of the BB-BC algorithm. The BB-BC algorithm also works in two stages: In the first stage, "Big Bang", the initial population is obtained. Then the "Big Crunch" phase starts and candidate solutions are gathered around the center of mass. The BB-BC algorithm is inspired by the formation of the universe and it determines the controller weights with low computational load and fast convergence. The combined tuning mechanism is adaptive and online. The results showed that, all controller weights are tuned online and updated during the maneuvers, and path following performance of the vehicle is enhanced at different NATO DLC speeds using the developed tuning mechanism, compared to working with ANN only. The fourth design, "robust linear MPC", aims to provide a control action where controller performance is maintained at different vehicle speeds. Steering angle is the only controller output. The robustness of the controller is ensured by ANN in 2 steps. In the first step, controller weights and sampling time are determined for a given range of vehicle speeds to achieve maximum tracking performance. These parameters are determined to be same for all speeds. In addition, a smoothed control signal is obtained by adaptively selecting the prediction horizon according to the vehicle speed. In the simulation studies, it is observed that the "robust linear MPC" is more successful in trajectory following and more flexible in terms of completing the control action at three different vehicle speeds, compared to a set of linear-quadratic-integrator (LQI) and two discrete integrators. The fifth design, the "classical nonlinear MPC with multiple controller outputs", aims to optimize the steering angle and the direct yaw moment (DYM) to ensure trajectory tracking in the NATO DLC maneuver at 2 different speeds. The DYM is then determined as rear wheel torques using the wheel torque distribution algorithm and transmitted to the system model. No method is used to tune controller parameters. In order to reduce the high computational burden caused by the online control signal generation, multiple controller outputs and base model complexity, the sixth design, the "tunable explicit nonlinear MPC", is used. In this design, the behavior of the "classical nonlinear MPC" on the control action is completely modeled by ANN. Furthermore, in order to improve the trajectory tracking performance, the weights of the controller are adjusted by two mechanisms: ANN and fuzzy logic. The simulation results show that the "tunable explicit nonlinear MPC" is more successful than the "classical nonlinear MPC" in terms of trajectory tracking and computational efficiency. All simulations in this Ph.D. thesis were performed in MATLAB / SIMULINK environment. It has been determined that the controllers designed based on MPC show effective results in terms of trajectory tracking performance and computational load. They are candidates for the control of autonomous vehicles.