Download or read book Model Predictive Control for Autonomous and Semiautonomous Vehicles written by Yiqi Gao and published by . This book was released on 2014 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we consider the problem of designing and implementing Model Predictive Controllers (MPC) for lane keeping and obstacle avoidance of autonomous or semi-autonomous ground vehicles. Vehicle nonlinear dynamics, fast sampling time and limited computational resources of embedded automotive hardware make it a challenging control design problem. MPC is chosen because of its capability of systematically taking into account nonlinearities, future predictions and operating constraints during the control design stage. We start from comparing two different MPC based control architectures. With a given trajectory representing the driver intent, the controller has to autonomously avoid obstacles on the road while trying to track the desired trajectory by controlling front steering angle and differential braking. The first approach solves a single nonlinear MPC problem for both replanning and following of the obstacle free trajectories. While the second approach uses a hierarchical scheme. At the high-level, new trajectories are computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model in order to avoid the obstacle. At the low-level an MPC controller computes the vehicle inputs in order to best follow the high level trajectory based on a higher fidelity nonlinear vehicle model. Experimental results of both approaches on icy roads are shown. The experimental as well as simulation results are used to compare the two approaches. We conclude that the hierarchical approach is more promising for real-time implementation and yields better performance due to its ability of having longer prediction horizon and faster sampling time at the same time. Based on the hierarchical approach for autonomous drive, we propose a hierarchical MPC framework for semi-autonomous obstacle avoidance, which decides the necessity of control intervention based on the aggressiveness of the evasive maneuver necessary to avoid collisions. The high level path planner plans obstacle avoiding maneuvers using a special kind of curve, the clothoid. The usage of clothoids have a long history in highway design and robotics control. By optimizing over a small number of parameters, the optimal clothoids satisfying the safety constraints can be determined. The same parameters also indicate the aggressiveness of the avoiding maneuver and thus can be used to decide whether a control intervention is needed before its too late to avoid the obstacle. In the case of control intervention, the low level MPC with a nonlinear vehicle model will follow the planned avoiding maneuver by taking over control of the steering and braking. The controller is validated by both simulations and experimental tests on an icy track. In the proposed autonomous hierarchical MPC where the point mass vehicle model is used for high level path replanning, despite of its successful avoidance of the obstacle, the controller's performance can be largely improved. In the test, we observed deviations of the actual vehicle trajectory from the high level planned path. This is because the point mass model is overly simplified and results in planned paths that are infeasible for the real vehicle to track. To address this problem, we propose an improved hierarchical MPC framework based on a special coordinate transformation in the high level MPC. The high level uses a nonlinear bicycle vehicle model and utilizes a coordinate transformation which uses vehicle position along a path as the independent variable. That produces high level planned paths with smaller tracking error for the real vehicle while maintaining real-time feasibility. The low level still uses an MPC with higher fidelity model to track the planned path. Simulations show the method's ability to safely avoid multiple obstacles while tracking the lane centerline. Experimental tests on an autonomous passenger vehicle driving at high speed on an icy track show the effectiveness of the approach. In the last part, we propose a robust control framework which systematically handles the system uncertainties, including the model mismatch, state estimation error, external disturbances and etc. The framework enforces robust constraint satisfaction under the presence of the aforementioned uncertainties. The actual system is modeled by a nominal system with an additive disturbance term which includes all the uncertainties. A "Tube-MPC" approach is used, where a robust control invariant set is used to contain all the possible tracking errors of the real system to the planned path (called the "nominal path"). Thus all the possible actual state trajectories in time lie in a tube centered at the nominal path. A nominal NMPC controls the tube center to ensure constraint satisfaction for the whole tube. A force-input nonlinear bicycle vehicle model is developed and used in the RNMPC control design. The robust invariant set of the error system (nominal system vs. real system) is computed based on the developed model, the associated uncertainties and a predefined disturbance feedback gain. The computed invariant set is used to tighten the constraints in the nominal NMPC to ensure robust constraint satisfaction. Simulations and experiments on a test vehicle show the effectiveness of the proposed framework.

Download or read book Passivity Based Model Predictive Control for Mobile Vehicle Motion Planning written by Adnan Tahirovic and published by Springer Science & Business Media. This book was released on 2013-04-18 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: • how to use an MPC optimization framework for the mobile vehicle navigation approach; • how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and • what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful.

Download or read book Autonomous Ground Vehicles written by Ümit Özgüner and published by Artech House. This book was released on 2011 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the near future, we will witness vehicles with the ability to provide drivers with several advanced safety and performance assistance features. Autonomous technology in ground vehicles will afford us capabilities like intersection collision warning, lane change warning, backup parking, parallel parking aids, and bus precision parking. Providing you with a practical understanding of this technology area, this innovative resource focuses on basic autonomous control and feedback for stopping and steering ground vehicles.Covering sensors, estimation, and sensor fusion to percept the vehicle motion and surrounding objects, this unique book explains the key aspects that makes autonomous vehicle behavior possible. Moreover, you find detailed examples of fusion and Kalman filtering. From maps, path planning, and obstacle avoidance scenarios...to cooperative mobility among autonomous vehicles, vehicle-to-vehicle communication, and vehicle-to-infrastructure communication, this forward-looking book presents the most critical topics in the field today.

Download or read book Non linear Model Predictive Control for Autonomous Vehicles written by Muhammad Awais Abbas and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Marine Craft Hydrodynamics and Motion Control written by Thor I. Fossen and published by John Wiley & Sons. This book was released on 2021-04-16 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of MARINE CRAFT HYDRODYNAMICS AND MOTION CONTROL The latest tools for analysis and design of advanced GNC systems Handbook of Marine Craft Hydrodynamics and Motion Control is an extensive study of the latest research in hydrodynamics, guidance, navigation, and control systems for marine craft. The text establishes how the implementation of mathematical models and modern control theory can be used for simulation and verification of control systems, decision-support systems, and situational awareness systems. Coverage includes hydrodynamic models for marine craft, models for wind, waves and ocean currents, dynamics and stability of marine craft, advanced guidance principles, sensor fusion, and inertial navigation. This important book includes the latest tools for analysis and design of advanced GNC systems and presents new material on unmanned underwater vehicles, surface craft, and autonomous vehicles. References and examples are included to enable engineers to analyze existing projects before making their own designs, as well as MATLAB scripts for hands-on software development and testing. Highlights of this Second Edition include: Topical case studies and worked examples demonstrating how you can apply modeling and control design techniques to your own designs A Github repository with MATLAB scripts (MSS toolbox) compatible with the latest software releases from Mathworks New content on mathematical modeling, including models for ships and underwater vehicles, hydrostatics, and control forces and moments New methods for guidance and navigation, including line-of-sight (LOS) guidance laws for path following, sensory systems, model-based navigation systems, and inertial navigation systems This fully revised Second Edition includes innovative research in hydrodynamics and GNC systems for marine craft, from ships to autonomous vehicles operating on the surface and under water. Handbook of Marine Craft Hydrodynamics and Motion Control is a must-have for students and engineers working with unmanned systems, field robots, autonomous vehicles, and ships. MSS toolbox: https://github.com/cybergalactic/mss Lecture notes: https://www.fossen.biz/wiley Author’s home page: https://www.fossen.biz

Download or read book An Integrated Framework for Planning and Control of Semi Autonomous Vehicles written by Andrew Jacob Gray and published by . This book was released on 2013 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents the design of a novel active safety system preventing unintended roadway departures. The proposed framework unifies threat assessment, stability, and control of passenger vehicles into a single combined optimization problem. A nonlinear Model Predictive Control (NMPC) problem is formulated where the nonlinear vehicle dynamics, in closed- loop with a driver model, is used to optimize the steering and braking actions needed to keep the driver safe. A model of the driver's nominal behavior is estimated based on his observed behavior. The driver commands the vehicle while the safety system corrects the driver's steering and braking action in case there's a risk that the vehicle will unintentionally depart the road. The resulting predictive controller is always active and mode switching is not necessary. We show simulation results detailing the behavior of the proposed controller as well as experimental results obtained by implementing the proposed framework on embedded hardware in a passenger vehicle. The results demonstrate the capability of the proposed controller to detect and avoid roadway departures while avoiding unnecessary interventions.

Download or read book Adaptive Lateral Model Predictive Control for Autonomous Driving of Heavy Duty Vehicles written by Goncalo Collares Pereira and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predictive Control Under Uncertainty for Safe Autonomous Driving written by Ashwin Mark Carvalho and published by . This book was released on 2016 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving vehicles have attracted a lot of interest due to their potential to significantly reduce traffic fatalities and transform people's lives. The reducing costs of advanced sensing technologies and the increasing capabilities of embedded computing hardware have enabled the commercialization of highly automated driving features. However, the reliable operation of autonomous vehicles is still a challenge and a major barrier in the large scale acceptance and deployment of the technology. This dissertation focuses on the challenges of designing safe control strategies for self-driving vehicles due to the presence of uncertainty arising from the non-deterministic forecasts of the driving scene. The overall goal is to unify elements from the fields of vehicle dynamics modeling, machine learning, real-time optimization and control design under uncertainty to enable the safe operation of self-driving vehicles. We propose a systematic framework based on Model Predictive Control (MPC) for the controller design, the effectiveness of which is demonstrated via applications such as lateral stability control, autonomous cruise control and autonomous overtaking on highways. Data collected from our experimental vehicles is used to build predictive models of the vehicle and the environment, and characterize the uncertainty therein. Several approaches for the control design are presented based on a worst-case or probabilistic view of the uncertain forecasts, depending on the application. The proposed control methodologies are validated by experiments performed on prototype passenger vehicles and are executed in real-time on embedded hardware with limited computational power. The experiments show the ability of the proposed framework to handle a variety of driving scenarios including aggressive maneuvers on low-friction surfaces such as snow and navigation in the presence of multiple vehicles.

Download or read book Study and Programming a State Estimator and a Model Predictive Control for an Autonomous Formula Student Car written by Pau Ortega Gómez and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The main aim of this dissertation is to study and program a state estimator and a Model Predictive Control for an autonomous driving application. The problem statement relies on developing an autonomous vehicle to compete in Formula Student Driverless. To achieve that, two of the main pillars are the state estimation, to know the vehicle state for location and mapping the environment; and the vehicle controller, in this case, a Model Predictive Controller. Kinematic and Dynamic vehicle models using the bicycle model are studied and tested to validate the real-world correlation. These will be used for both state estimation and controller. Different approaches to Kalman Filters are studied in Sec. 3 State Estimator to analyse which is the best solution. Kalman Filter needs different sensors installed on the vehicle. For this reason, a study about the sensors has been done to find the most appropriate sensor given the system and algorithm requirements. At the end of the section, the experimental results are discussed. A cascade controller using MPC and PID controller or State Feedback for fast dynamics is discussed in the controller chapter. The results from the fast dynamics controller are exposed, discussing which fits better in the control scheme. Finally, the Model Predictive Control is explained. Both MPC controller and state estimator have been programmed in ROS and simulated using a Lap Time Simulator which simulation-reality error is less than a 1% in lap time. To obtain a full understanding of how both codes are implemented, the simulator structure is presented. Both EKF and MPC results are also exposed in the last chapter.

Download or read book Model Predictive Control Schemes For Autonomous Ground Vehicle written by Solomon S. Oyelere and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Model Predictive Control (MPC) to fast systems like autonomous ground vehicles (AGV) or mobile robots is thoroughly investigated in this book. The control of Autonomous ground vehicles (AGV) is challenging because of nonholonomic constraints, uncertainties, speed and accuracy of controls and the vehicle's terrain of operation. This work develops a Model Predictive Control (MPC) algorithm for the autonomous ground vehicle (AGV).

Download or read book Advanced Model Predictive Control for Autonomous Marine Vehicles written by Yang Shi and published by Springer Nature. This book was released on 2023-02-13 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of marine control system design related to underwater robotics applications. In particular, it presents novel optimization-based model predictive control strategies to solve control problems appearing in autonomous underwater vehicle applications. These novel approaches bring unique features, such as constraint handling, prioritization between multiple design objectives, optimal control performance, and robustness against disturbances and uncertainties, into the control system design. They therefore form a more general framework to design marine control systems and can be widely applied. Advanced Model Predictive Control for Autonomous Marine Vehicles balances theoretical rigor – providing thorough analysis and developing provably-correct design conditions – and application perspectives – addressing practical system constraints and implementation issues. Starting with a fixed-point positioning problem for a single vehicle and progressing to the trajectory-tracking and path-following problem of the vehicle, and then to the coordination control of a large-scale multi-robot team, this book addresses the motion control problems, increasing their level of challenge step-by-step. At each step, related subproblems such as path planning, thrust allocation, collision avoidance, and time constraints for real-time implementation are also discussed with solutions. In each chapter of this book, compact and illustrative examples are provided to demonstrate the design and implementation procedures. As a result, this book is useful for both theoretical study and practical engineering design, and the tools provided in the book are readily applicable for real-world implementation.

Download or read book Evaluation of Model Predictive Control Method for Collision Avoidance of Automated Vehicles written by Hikmet D. Ozdemir and published by . This book was released on 2020 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collision avoidance design plays an essential role in autonomous vehicle technology. It's an attractive research area that will need much experimentation in the future. This research area is very important for providing the maximum safety to automated vehicles, which have to be tested several times under diFFerent circumstances for safety before use in real life. This thesis proposes a method for designing and presenting a collision avoidance maneuver by using a model predictive controller with a moving obstacle for automated vehicles. It consists of a plant model, an adaptive MPC controller, and a reference trajectory. The proposed strategy applies a dynamic bicycle model as the plant model, adaptive model predictive controller for the lateral control, and a custom reference trajectory for the scenario design. The model was developed using the Model Predictive Control Toolbox and Automated Driving Toolbox in Matlab. Builtin tools available in Matlab/Simulink were used to verify the modeling approach and analyze the performance of the system. The major contribution of this thesis work was implementing a novel dynamic obstacle avoidance control method for automated vehicles. The study used validated parameters obtained from previous research. The novelty of this research was performing the studies using a MPC based controller instead of a sliding mode controller, that was primarily used in other studies. The results obtained from the study are compared with the validated models. The comparisons consisted of the lateral overlap, lateral error, and steering angle simulation results between the models. Additionally, this study also included outcomes for the yaw angle. The comparisons and other outcomes obtained in this study indicated that the developed control model produced reasonably acceptable results and recommendations for future studies.

Download or read book Advances in Engineering Research and Application written by 藤田ハミド and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The International Conference on Engineering Research and Applications (ICERA 2018), which took place at Thai Nguyen University of Technology, Thai Nguyen, Vietnam on December 1-2, 2018, provided an international forum to disseminate information on latest theories and practices in engineering research and applications. The conference focused on original research work in areas including Mechanical Engineering, Materials and Mechanics of Materials, Mechatronics and Micro Mechatronics, Automotive Engineering, Electrical and Electronics Engineering, Information and Communication Technology. By disseminating the latest advances in the field, The Proceedings of ICERA 2018, Advances in Engineering Research and Application, helps academics and professionals alike to reshape their thinking on sustainable development."--

Download or read book DEVELOPMENT OF AUTONOMOUS VEHICLE MOTION PLANNING AND CONTROL ALGORITHM WITH D PLANNER AND MODEL PREDICTIVE CONTROL IN A DYNAMIC ENVIRONMENT written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : The research in this report incorporates the improvement in the autonomous driving capability of self-driving cars in a dynamic environment. Global and local path planning are implemented using the D* path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm is tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Different traffic scenarios are built, and a camera sensor is configured to simulate the sensory data and feed it to the controller for further processing and vehicle motion planning. Simulation results of vehicle motion control with global and local path planning for dynamic obstacle avoidance are presented. The simulation results show that an autonomous vehicle follows a commanded velocity when the relative distance between the ego vehicle and an obstacle is greater than a calculated safe distance. When the relative distance is close to the safe distance, the ego vehicle maintains the headway. When an obstacle is detected by the ego vehicle and the ego vehicle wants to pass the obstacle, the ego vehicle performs obstacle avoidance maneuver by tracking desired lateral positions.

Download or read book Human Like Decision Making and Control for Autonomous Driving written by Peng Hang and published by CRC Press. This book was released on 2022-07-25 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.

Download or read book Trajectory Generation for Autonomous Highway Driving Using Model Predictive Control written by Gerard Ferrer Usieto and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Predictive Control (MPC) has had an increasing role in autonomous driving applications over the last decade, enabled by the continuous rising of the computational power in microcontrollers. In this thesis a collision avoidance trajectory generation algorithm based in MPC formulation is developed. The operating environment consists in a one-way highway with two lanes. The overall system is equipped with a low-level controller capable of tracking the trajectory generated by the MPC planner. In the path towards this goal, a MPC based lane changing application in an obstacle-free highway environment has been developed. A point-mass kinematic vehicle model is used as the MPC plant model for its simplicity and enabled by the usage of a low-level controller. This thesis studies several obstacle representation approaches and then, explains in detail the development process of the collision avoidance trajectory generation application, defining and discussing simulation results for each intermediate approach obtained. Both applications have been implemented in a BeagleBone Black online board situated in small-scale trucks (1:12) for testing purpose. The experimental results have been studied and discussed to prove the algorithms functionalities, as well as to check the board capabilities to run online MPC applications in comparison with polynomials based approaches.

Download or read book Safe Control and Coordination of Multiple Autonomous Vehicles written by and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: