Development of Biomimetic-Based Controller Design Methods for Advanced Energy Systems

A biologically inspired optimal control strategy, denoted as BIO-CS, is proposed for advanced energy systems applications. This strategy combines the ant\u27s rule of pursuit idea with multi-agent and optimal control concepts. The BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents\u27 local interactions. The developed BIO-CS is integrated with an Artificial Neural Network (ANN)-based adaptive component for further improvement of the overall framework. In particular, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIO-CS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems.;The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. Specifically, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIMRTM software platform is addressed. The proposed control laws are derived in MATLAB RTM environment, while the plant models are built in DYNSIM RTM, and a previously developed MATLABRTM-DYNSIM RTM link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIO-CS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking.;Other applications of BIO-CS addressed include: i) a nonlinear fermentation process to produce ethanol; and ii) a transfer function model derived from the cyber-physical fuel cell-gas turbine hybrid power system that is part of the Hybrid Performance (HYPER) project at the National Energy Technology Laboratory (NETL). Other theoretical developments in this work correspond to the integration of the BIO-CS approach with Multi-Agent Optimization (MAO) techniques and casting BIO-CS as a Model Predictive Controller (MPC). These developments are demonstrated by revisiting the fermentation process example. The proposed biologically-inspired approaches provide a promising alternative for advanced control of energy systems of the future

Application of auto-ID in agent-based manufacturing control

Conference Theme: Soft Computing Techniques for Advanced Manufacturing and Service SystemsSession - MA-Ha Manufacturing Technologies 1: cie177hk-1A feasibility study has been established to integrate agent and auto-ID technologies in manufacturing control applications. A multi-agent system (MAS) framework for intelligent manufacturing has been established. The intelligent MAS environment attempts to exploit the potential of Auto-ID (RFID in particular) technology in manufacturing applications. The aim is to evaluate the applications of Auto-ID, especially with RFID technology, in manufacturing control. This involves the establishment of the hardware and software interfaces to enable production and process data to be recorded and written in the Auto-ID devices. Experiments are being conducted to study the working requirements and parameters of the Auto-ID devices in the shopfloor environments. Subsequently, the RFID technology is adopted in a flexible assembly cell (FAC) to evaluate the feasibility of integrating the RFID devices in a multi-agent based manufacturing control system. A MAS infrastructure for FAC control has been developed to incorporate the coordination of the RFID devices.published_or_final_versionThe 40th International Conference on Computers & Industrial Engineering (CIE40), Awaji City, Japan, 25-28 July 2010. In Proceedings of the International Conference on Computers and Industrial Engineering, 2010, p. 1-

An Automated Continuous Integration Multitest Platform for Automotive Systems

Testing has always been a crucial part of application development. It involves different techniques for verifying and validating the features of the target systems. For a complicated and/or complex system, tests are preferred to be carried out in different stages of the development process and as early as possible to avoid extra costs due to the errors caught at later stages. With the increasing system complexity, the cost of testing is also increasing in terms of resources and time, which introduce further impact against development constraints such as time-to-market. On the other hand, more and more associated electronic components lead to an ever-increasing system complexity in high reliable applications such as automotive ones different from heterogeneous systems such as advanced driver assistance systems, sensor fusion systems, etc. In this article, we present a testing framework utilizing the continuous integration (CI) solution from software engineering, a commercial virtual platform, and a hardware field programmable gate array based verification platform focusing on the engine control unit to demonstrate the feasibility of the proposed method. The efficiency and viability of the CI method have been demonstrated on a real heterogeneous automotive system

Koneoppimiskehys petrokemianteollisuuden sovelluksille

Machine learning has many potentially useful applications in process industry, for example in process monitoring and control. Continuously accumulating process data and the recent development in software and hardware that enable more advanced machine learning, are fulfilling the prerequisites of developing and deploying process automation integrated machine learning applications which improve existing functionalities or even implement artificial intelligence. In this master's thesis, a framework is designed and implemented on a proof-of-concept level, to enable easy acquisition of process data to be used with modern machine learning libraries, and to also enable scalable online deployment of the trained models. The literature part of the thesis concentrates on studying the current state and approaches for digital advisory systems for process operators, as a potential application to be developed on the machine learning framework. The literature study shows that the approaches for process operators' decision support tools have shifted from rule-based and knowledge-based methods to machine learning. However, no standard methods can be concluded, and most of the use cases are quite application-specific. In the developed machine learning framework, both commercial software and open source components with permissive licenses are used. Data is acquired over OPC UA and then processed in Python, which is currently almost the de facto standard language in data analytics. Microservice architecture with containerization is used in the online deployment, and in a qualitative evaluation, it proved to be a versatile and functional solution.Koneoppimisella voidaan osoittaa olevan useita hyödyllisiä käyttökohteita prosessiteollisuudessa, esimerkiksi prosessinohjaukseen liittyvissä sovelluksissa. Jatkuvasti kerääntyvä prosessidata ja toisaalta koneoppimiseen soveltuvien ohjelmistojen sekä myös laitteistojen viimeaikainen kehitys johtavat tilanteeseen, jossa prosessiautomaatioon liitettyjen koneoppimissovellusten avulla on mahdollista parantaa nykyisiä toiminnallisuuksia tai jopa toteuttaa tekoälysovelluksia. Tässä diplomityössä suunniteltiin ja toteutettiin prototyypin tasolla koneoppimiskehys, jonka avulla on helppo käyttää prosessidataa yhdessä nykyaikaisten koneoppimiskirjastojen kanssa. Kehys mahdollistaa myös koneopittujen mallien skaalautuvan käyttöönoton. Diplomityön kirjallisuusosa keskittyy prosessioperaattoreille tarkoitettujen digitaalisten avustajajärjestelmien nykytilaan ja toteutustapoihin, avustajajärjestelmän tai sen päätöstukijärjestelmän ollessa yksi mahdollinen koneoppimiskehyksen päälle rakennettava ohjelma. Kirjallisuustutkimuksen mukaan prosessioperaattorin päätöstukijärjestelmien taustalla olevat menetelmät ovat yhä useammin koneoppimiseen perustuvia, aiempien sääntö- ja tietämyskantoihin perustuvien menetelmien sijasta. Selkeitä yhdenmukaisia lähestymistapoja ei kuitenkaan ole helposti pääteltävissä kirjallisuuden perusteella. Lisäksi useimmat tapausesimerkit ovat sovellettavissa vain kyseisissä erikoistapauksissa. Kehitetyssä koneoppimiskehyksessä on käytetty sekä kaupallisia että avoimen lähdekoodin komponentteja. Prosessidata haetaan OPC UA -protokollan avulla, ja sitä on mahdollista käsitellä Python-kielellä, josta on muodostunut lähes de facto -standardi data-analytiikassa. Kehyksen käyttöönottokomponentit perustuvat mikropalveluarkkitehtuuriin ja konttiteknologiaan, jotka osoittautuivat laadullisessa testauksessa monipuoliseksi ja toimivaksi toteutustavaksi

The GRAVITY instrument software / High-level software

GRAVITY is the four-beam, near- infrared, AO-assisted, fringe tracking, astrometric and imaging instrument for the Very Large Telescope Interferometer (VLTI). It is requiring the development of one of the most complex instrument software systems ever built for an ESO instrument. Apart from its many interfaces and interdependencies, one of the most challenging aspects is the overall performance and stability of this complex system. The three infrared detectors and the fast reflective memory network (RMN) recorder contribute a total data rate of up to 20 MiB/s accumulating to a maximum of 250 GiB of data per night. The detectors, the two instrument Local Control Units (LCUs) as well as the five LCUs running applications under TAC (Tools for Advanced Control) architecture, are interconnected with fast Ethernet, RMN fibers and dedicated fiber connections as well as signals for the time synchronization. Here we give a simplified overview of all subsystems of GRAVITY and their interfaces and discuss two examples of high-level applications during observations: the acquisition procedure and the gathering and merging of data to the final FITS file.Comment: 8 pages, 7 figures, published in Proc. SPIE 9146, Optical and Infrared Interferometry IV, 91462