A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Advanced Launch System propulsion focused technology liquid methane turbopump technical implementation plan

This program will focus on the integration of all functional disciplines of the design, manufacturing, materials, fabrication and producibility to define and demonstrate a highly reliable, easily maintained, low cost liquid methane turbopump as a component for the STBE (Space Transportation Booster Engine) using the STME (main engine) oxygen turbopump. A cost model is to be developed to predict the recurring cost of production hardware and operations. A prime objective of the program is to design the liquid methane turbopump to be used in common with a LH2 turbopump optimized for the STME. Time phasing of the effort is presented and interrelationship of the tasks is defined. Major subcontractors are identified and their roles in the program are described

Methods of Technical Prognostics Applicable to Embedded Systems

Hlavní cílem dizertace je poskytnutí uceleného pohledu na problematiku technické prognostiky, která nachází uplatnění v tzv. prediktivní údržbě založené na trvalém monitorování zařízení a odhadu úrovně degradace systému či jeho zbývající životnosti a to zejména v oblasti komplexních zařízení a strojů. V současnosti je technická diagnostika poměrně dobře zmapovaná a reálně nasazená na rozdíl od technické prognostiky, která je stále rozvíjejícím se oborem, který ovšem postrádá větší množství reálných aplikaci a navíc ne všechny metody jsou dostatečně přesné a aplikovatelné pro embedded systémy. Dizertační práce přináší přehled základních metod použitelných pro účely predikce zbývající užitné životnosti, jsou zde popsány metriky pomocí, kterých je možné jednotlivé přístupy porovnávat ať už z pohledu přesnosti, ale také i z pohledu výpočetní náročnosti. Jedno z dizertačních jader tvoří doporučení a postup pro výběr vhodné prognostické metody s ohledem na prognostická kritéria. Dalším dizertačním jádrem je představení tzv. částicového filtrovaní (particle filtering) vhodné pro model-based prognostiku s ověřením jejich implementace a porovnáním. Hlavní dizertační jádro reprezentuje případovou studii pro velmi aktuální téma prognostiky Li-Ion baterii s ohledem na trvalé monitorování. Případová studie demonstruje proces prognostiky založené na modelu a srovnává možné přístupy jednak pro odhad doby před vybitím baterie, ale také sleduje možné vlivy na degradaci baterie. Součástí práce je základní ověření modelu Li-Ion baterie a návrh prognostického procesu.The main aim of the thesis is to provide a comprehensive overview of technical prognosis, which is applied in the condition based maintenance, based on continuous device monitoring and remaining useful life estimation, especially in the field of complex equipment and machinery. Nowadays technical prognosis is still evolving discipline with limited number of real applications and is not so well developed as technical diagnostics, which is fairly well mapped and deployed in real systems. Thesis provides an overview of basic methods applicable for prediction of remaining useful life, metrics, which can help to compare the different approaches both in terms of accuracy and in terms of computational/deployment cost. One of the research cores consists of recommendations and guide for selecting the appropriate forecasting method with regard to the prognostic criteria. Second thesis research core provides description and applicability of particle filtering framework suitable for model-based forecasting. Verification of their implementation and comparison is provided. The main research topic of the thesis provides a case study for a very actual Li-Ion battery health monitoring and prognostics with respect to continuous monitoring. The case study demonstrates the prognostic process based on the model and compares the possible approaches for estimating both the runtime and capacity fade. Proposed methodology is verified on real measured data.