Discrete-time Robust PD Controlled System with DOB/CDOB Compensation for High Speed Autonomous Vehicle Path Following

Autonomous vehicle path following performance is one of significant consideration. This paper presents discrete time design of robust PD controlled system with disturbance observer (DOB) and communication disturbance observer (CDOB) compensation to enhance autonomous vehicle path following performance. Although always implemented on digital devices, DOB and CDOB structure are usually designed in continuous time in the literature and also in our previous work. However, it requires high sampling rate for continuous-time design block diagram to automatically convert to corresponding discrete-time controller using rapid controller prototyping systems. In this paper, direct discrete time design is carried out. Digital PD feedback controller is designed based on the nominal plant using the proposed parameter space approach. Zero order hold method is applied to discretize the nominal plant, DOB and CDOB structure in continuous domain. Discrete time DOB is embedded into the steering to path following error loop for model regulation in the presence of uncertainty in vehicle parameters such as vehicle mass, vehicle speed and road-tire friction coefficient and rejecting external disturbance like crosswind force. On the other hand, time delay from CAN bus based sensor and actuator command interfaces results in degradation of system performance since large negative phase angles are added to the plant frequency response. Discrete time CDOB compensated control system can be used for time delay compensation where the accurate knowledge of delay time value is not necessary. A validated model of our lab Ford Fusion hybrid automated driving research vehicle is used for the simulation analysis while the vehicle is driving at high speed. Simulation results successfully demonstrate the improvement of autonomous vehicle path following performance with the proposed discrete time DOB and CDOB structure

Virtual and Real Data Populated Intersection Visualization and Testing Tool for V2X Application Development

The capability afforded by Vehicle-to-Vehicle communication improves situational awareness and provides advantages for many of the traffic problems caused by reduced visibility or No-Line-of-Sight situations, being useful for both autonomous and non-autonomous driving. Additionally, with the traffic light Signal Phase and Timing and Map Datainformation and other advisory information provided with Vehicle-to-Infrastructure (V2I) communication, outcomes which benefit the driver in the long run, such as reducing fuel consumption with speed regulation or decreasing traffic congestion through optimal speed advisories, providing red light violation warning messages and intersection motion assist messages for collision-free intersection maneuvering are all made possible. However, developing applications to obtain these benefits requires an intensive development process within a lengthy testing period. Understanding the intersection better is a large part of this development process. Being able to see what information is broadcasted and how this information translates into the real world would both benefit the development of these highly useful applications and also ensure faster evaluation, when presented visually, using an easy to use and interactive tool. Moreover, recordings of this broadcasted information can be modified and used for repeated testing. Modification of the data makes it flexible and allows us to use it for a variety of testing scenarios at a virtually populated intersection. Based on this premise, this paper presents and demonstrates visualization tools to project SPaT, MAP and Basic Safety Message information into easy to read real-world based graphs. Also, it provides information about the modification of the real-world data to allow creation of a virtually populated intersection, along with the capability to also inject virtual vehicles at this intersection

Holistic Vehicle Control Using Learning MPC

In recent years, learning MPC schemes have been introduced to address these challenges of traditional MPC. They typically leverage different machine learning techniques to learn the system dynamics directly from data, allowing it to handle model uncertainty more effectively. Besides, they can adapt to changes by continuously updating the learned model using real-time data, ensuring that the controller remains effective even as the system evolves. However, there are some challenges for the existing learning MPC techniques. Firstly, learning-based control approaches often lack interpretability. Understanding and interpreting the learned models and their learning and prediction processes are crucial for safety critical systems such as vehicle stability systems. Secondly, existing learning MPC techniques rely solely on learned models, which might result in poor performance or instability if the model encounters scenarios that differ significantly from the training data. Thirdly, existing learning MPC techniques typically require large amounts of high-quality data for training accurate models, which can be expensive or impractical in the vehicle stability control domain. To address these challenges, this thesis proposes a novel hybrid learning MPC approach for HVC. The main objective is to leverage the capabilities of machine learning algorithms to learn accurate and adaptive models of vehicle dynamics from data, enabling enhanced control strategies for improved stability and maneuverability. The hybrid learning MPC scheme maintains a traditional physics-based vehicle model and a data-based learning model. In the learned model, a variety of machine-learning techniques can be used to predict vehicle dynamics based on learning from collected vehicle data. The performance of the developed hybrid learning MPC controller using torque vectoring (TV) as the actuator is evaluated through the Matlab/Simulink and CarSim co-simulation with a high-fidelity Chevy Equinox vehicle model under a series of harsh maneuvers. Extensive real-world experiments using a Chevy Equinox electric testing vehicle are conducted. Both simulation results and experimental results show that the developed hybrid learning MPC approach consistently outperforms existing MPC methods with better yaw rate tracking performance and smaller vehicle sideslip under various driving conditions

Aerial Robotics for Inspection and Maintenance

Aerial robots with perception, navigation, and manipulation capabilities are extending the range of applications of drones, allowing the integration of different sensor devices and robotic manipulators to perform inspection and maintenance operations on infrastructures such as power lines, bridges, viaducts, or walls, involving typically physical interactions on flight. New research and technological challenges arise from applications demanding the benefits of aerial robots, particularly in outdoor environments. This book collects eleven papers from different research groups from Spain, Croatia, Italy, Japan, the USA, the Netherlands, and Denmark, focused on the design, development, and experimental validation of methods and technologies for inspection and maintenance using aerial robots

Data driven techniques for on-board performance estimation and prediction in vehicular applications.

L'abstract è presente nell'allegato / the abstract is in the attachmen

A matrix based integrated framework for multi disciplinary exploration of cyber-international relations

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 117-130).Cyberspace is the most pervasive and rapidly adopted communication media and the most disruptive until date. It is now indispensable for almost every facet of modern society and touches, practically, everyone by providing a powerful platform for interaction and innovation. Given the widespread availability of tools to operate in this environment, a growing array of actors are trying to benefit as they seek to control critical decision points in the real world and cyberspace. It is imperative to understand what cyberspace "is made of' - over and above the Internet and answer the question "who gets what, when, and how?" The intent of this research initiative is to contribute to the generation, management and sharing of knowledge to enhance understandings of the emerging area of cyber-international relations as a complex, flexible and adaptive domain of interactions. The first contribution of this thesis is the development of a multi-dimensional Cyber System for Strategic Decisions (CSSD) framework. This framework enables a holistic identification of the elements of a system, which are structured as set of nested and hierarchical relationships. It facilitated in mapping the entities that comprise different domains of cyberspace and the dependencies within and across those entities. The second contribution of this thesis is the development of the foundations for an internally consistent and articulate representation of cyber-international relations in terms of actors- individuals and group of individuals, layers of the Internet and the context of cyber engagement that form the basis of the CSSD framework. This approach can be applied to diverse domains to build scenarios and model different facets of both the real world and cyberspace according to the practical needs. The instruments and intensity of engagement and the extent of time of engagement are the two dependencies that map the interactions among the different entities. The third contribution of this thesis is the development of a robust, comprehensive, and coherent test use-case based on "Intellectual Property Rights (IPR)" domain. The CSSD framework is then adapted to test its applicability to the use-case. IPR has been selected as the test use-case because it provided both the legal understanding and legislative efforts at international level, in as collaborative, effective and uniform manner as possible, to protect the rights of intellectual property owners and to avoid future conflicts.by Gaurav Agarwal.S.M.in Engineering and Managemen

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Learning locomotion gait through hormone-based controller in modular robots

Modular robots are robots composed of multiple units, called 'modules'. Each module is an independent robot, with its own control electronics, actuators, sensors, communications and power. These modules can change their position and configuration in order to adapt to the requirements of the situation, making modular robot suitable for tasks that involve unknown or unstructured terrains, in which a robot cannot be designed speci cally for them. Some examples of those applications are space exploration, battlefield reconnaissance, finding victims among the debris in natural catastrophes and other similar tasks involving complicated terrains, which require a high versability. But this versability comes with several drawbacks. As modular robots are composed of several independent robots, the nature of their controller is distributed, which difficults their design and programming, requiring additionally a robust communication protocol to share information among modules. The high number of modules also results in a robot with a with number of degrees of freedom, for which achieving the coordination required for locomotion becomes increasingly difficult. Finally, as the modules are fully independent robots, the cost of researching modular robotics is usually very high, since the price of building a single robot has to be multiplied by the high number of modules. This thesis addresses those three mentioned problems: obtaining optimal locomotion gaits from a biologically inspired approach, using sinusoidal oscillators whose parameters are found through evolutionary optimization algorithms; developing a homogenous, distributed controller based on digital hormones that can recognize the current robot configuration and select the proper gait; and the development of a low-cost modular robotic platform to reseach locomotion gaits for different configurations.Ingeniería Electrónica Industrial y Automátic

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity