State Estimation Filtering using Recent Finite Measurements and Inputs for Active Suspension System with Temporary Uncertainties

In this paper, the finite memory structure(FMS) filter using most recent finite measured outputs and control inputs is applied for the state estimation filtering of automotive suspension systems to verify intrinsic robustness of FMS filter. Firstly, the single-corner model for the automotive suspension system and its state-space model are described. Secondly, FMS as well as infinite memory structure(IMS) filters are briefly introduced and represented by the summation form. Thirdly, a couple of temporary uncertainties, model uncertainty and unknown input, are discussed. Finally, extensive computer simulations are performed for both nominal system and temporarily uncertain system. It is shown that the FMS filter can be better than the IMS filter for both temporary uncertainties. In addition, the FMS filter can be shown to be comparable to the IMS filter after the effects of a couple of temporary uncertainties have completely disappeared

Preview-based techniques for vehicle suspension control: a state-of-the-art review

Abstract Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution for production vehicles. Despite the availability of a significant body of papers on the topic, the literature lacks a comprehensive and up-to-date survey on the variety of proposed techniques for suspension control with road preview, and the comparison of their effectiveness. To cover the gap, this literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview. The main formulations are reported for each control category, and the respective features are critically analysed, together with the most relevant performance indicators. The paper also discusses the effect of the road preview time on the resulting system performance, and identifies control development trends

A literature review on connected vehicle use cases

Abstract. Explosive growth of Internet of Things and cloud computing technologies has enabled the creation of more connected vehicles. As vehicles are becoming more connected the use cases of surrounding them are ambiguous. The goal of this thesis is to perform a literature review to see what use cases there are for connected vehicles, and if it is possible to categorize these use case scenarios to clear categories. The results of this thesis indicate that there are three main use case categories for connected vehicles. First category was vehicle state monitoring which was used to monitor vehicle’s diagnostic data, to monitor emission control tampering and to determine if the vehicle has had a collision. Second category were about vehicle management where connected vehicle technologies were used to deliver smart vehicle information services and to perform remote updating of a vehicle. Third category was vehicle state controlling, where use case scenarios of this category created an adaptive suspension control application and battery usage optimization application by utilizing cloud backend. Safe route assisting application also utilized cloud technologies. As well as remote control of a vehicle and digital twin based driving assist system. These use case categories could also be differentiated by their communication method between the vehicle and remote backend. There are three main communication methods; active communication where remote backend only sent data to vehicle, passive communication in which the remote backend was passive receiver of the vehicle data and hybrid two-way communication between vehicle and remote backend. Primary contribution of this thesis was that framework for defining different connected vehicle use case scenarios was established. Primary limitation of this thesis was that low amount of publications were considered for review, and for future research it is recommended to expand this topic to include more publications for review and to also consider what are the common characteristics in each use case scenario

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

AI-For-Mobility—A New Research Platform for AI-Based Control Methods

AI-For-Mobility (AFM) is the new research platform to investigate and implement novel control methods based on Artificial Intelligence (AI) within the Department of Vehicle System Dynamics at the German Aerospace Center (DLR). A production hybrid vehicle serves as a base platform. Since AI-based methods are data-driven, the vehicle is equipped with manifold sensors to provide the required data. They measure the vehicle’s state holistically and perceive the surrounding environment, while high performance on-board CPUs and GPUs handle the sensor data. A full by-wire control system enables the vehicle to be used for applications in the field of automated driving. Despite all modifications, it is approved for public road use and meets the driving dynamics properties of a standard road vehicle. This makes it an attractive research and test platform, both for automotive applications and technology demonstrations in other scientific fields (e.g., robotics, aviation, etc.). This paper presents the vehicle’s design and architecture in a detailed manner and shows a promising application potential of AFM in the context of AI-based control methods

Optimisation of racing car suspensions featuring inerters

Racing car suspensions are a critical system in the overall performance of the vehicle. They must be able to accurately control ride dynamics as well as influencing the handling characteristics of the vehicle and providing stability under the action of external forces. This work is a research study on the design and optimisation of high performance vehicle suspensions using inerters. The starting point is a theoretical investigation of the dynamics of a system fitted with an ideal inerter. This sets the foundation for developing a more complex and novel vehicle suspension model incorporating real inerters. The accuracy and predictability of this model has been assessed and validated against experimental data from 4- post rig testing. In order to maximise overall vehicle performance, a race car suspension must meet a large number of conflicting objectives. Hence, suspension design and optimisation is a complex task where a compromised solution among a set of objectives needs to be adopted. The first task in this process is to define a set of performance based objective functions. The approach taken was to relate the ride dynamic behaviour of the suspension to the overall performance of the race car. The second task of the optimisation process is to develop an efficient and robust optimisation methodology. To address this, a multi-stage optimisation algorithm has been developed. The algorithm is based on two stages, a hybrid surrogate model based multiobjective evolutionary algorithm to obtain a set of non-dominated optimal suspension solutions and a transient lap-time simulation tool to incorporate external factors to the decision process and provide a final optimal solution. A transient lap-time simulation tool has been developed. The minimum time manoeuvring problem has been defined as an Optimal Control problem. A novel solution method based on a multi-level algorithm and a closed-loop driver steering control has been proposed to find the optimal lap time. The results obtained suggest that performance gains can be obtained by incorporating inerters into the suspension system. The work suggests that the use of inerters provides the car with an optimised aerodynamic platform and the overall stability of the vehicle is improved

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Extending Cyber-Physical Systems to Support Stakeholder Decisions Under Resource and User Constraints: Applications to Intelligent Infrastructure and Social Urban Systems

In recent years, rapid urbanization has imposed greater load demands on physical infrastructure while placing stressors (e.g., pollution, congestion, social inequity) on social systems. Despite these challenges, opportunities are emerging from the unprecedented proliferation of information technologies enabling ubiquitous sensing, cloud computing, and full-scale automation. Together, these advancements enable “intelligent” systems that promise to enhance the operation of the built environment. Even with these advancements, the ability of professionals to “sense for decisions” —data-driven decision processes based on sensed data that have quantifiable returns on investment—remains unrealized for an entire class of problems. In response, this dissertation builds a rigorous foundation enabling stakeholders to use sensor data to inform decisions in two applications: infrastructure asset management and community-engaged decision making. This dissertation aligns sensing strategies with decisions governing infrastructure management by extending the role of reliability methods to quantify system performance. First, the reliability index is used as a scalar measure of the safety (i.e., failure probability) that is extracted from monitoring data to assess structural condition relative to a failure limit state. As an example, long-term data collected from a wireless sensing network (WSN) installed on the Harahan Bridge (Memphis, TN) is used in a reliability framework to track the fatigue life of critical eyebar assemblies. The proposed reliability-based SHM framework is then generalized to formally and more broadly link SHM data with condition ratings (CRs) because inspector-assigned CRs remain the primary starting point for asset management decisions made in practice today. While reliability methods historically quantify safety with respect to a single failure limit state, this work demonstrates that there exist measurable reliability index values associated with “lower” limit states below failure that more richly characterize structural performance and rationally map to CR scales. Consequently, monitoring data can be used to assign CRs based on quantitative information encompassing the measurable damage domain, as opposed to relying on visual inspection. This work reflects the first-ever SHM framework to explicitly map monitoring data to actionable decisions and is validated using a WSN on the Telegraph Road Bridge (TRB) (Monroe, MI). A primary challenge faced by solar-powered WSNs is their stringent energy constraints. For decision-making processes relying on statistical estimation of performance, the utility of data should be considered to optimize the data collection process given these constraints. This dissertation proposes a novel stochastic data collection and transmission policy for WSNs that minimizes the variance of a measured process’ estimated parameters subject to constraints imposed by energy and data buffer sizes, stochastic models of energy and event arrivals, the value of measured data, and temporal death. Numerical results based on one-year of data collected from the TRB illustrate the gains achieved by implementing the optimal policy to obtain response data used to estimate the reliability index. Finally, this dissertation extends the work performed in WSN and sense-for-decision frameworks by exploring their role in community-based decision making. This work poses societal engagement as a necessary entry point to urban sensing efforts because members of under-resourced communities are vulnerable to lack of access to data and information. A novel, low-power WSN architecture is presented that functions as a user-friendly sensing solution that communities can rapidly deploy. Applying this platform, transformative work to “democratize” data is proposed in which members of vulnerable communities collect data and generate insights that inform their decision-making strategies.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162898/1/kaflanig_1.pd

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin