Advance control strategies for Maglev suspension systems

The Birmingham Maglev developed over fifteen years ago has successfully demonstrated the inherent advantages of low speed maglev over comparable wheeled systems. It remains the only commercially operational Maglev in the world today. To develop the next generation of Maglev vehicles which will overcome some of the limitations of the Birmingham system, such as chassis length and cost, the following issues are addressed in this thesis. 1) The possibility of interaction between the chassis resonant frequencies and the suspension control system causing poor ride quality and at worst instability, are formally analysed. In the Birmingham vehicle a stiff chassis (fundamental bending mode 40Hz) is used avoiding significant interaction with the suspension controller. Using advanced control strategies the low frequency chassis resonances can be controlled allowing a vehicle structure to be used with a fundamental bending mode of about 12Hz. 2) A modem control strategy is developed which delivers an improved ride quality compared with the present classical control system despite having to operate with a 'soft' chassis. Kalman filters are digitally implemented and conclusions drawn about their performance. The classical control strategy is also successfully demonstrated on a 3 m long 'flexible beam' rig. 3) An associated Maglev suspension problem for the response to ramp inputs such as the transition onto gradients which causes either a large steady state tracking error or a worsening ride quality is addressed by modern control theory using integral feedback techniques and classical theory using third order filters. These controllers are globally optimised by a multi-objective parameter optimisation system which formally considers the conflicts inherent in a suspension system between response to stochastic inputs and deterministic inputs

Uncertainty assessment of climate and land use changes scenarios for the Millbrook catchment - reservoir system simulated by the SWAT-SALMO

In this study, we analyse the uncertainty of eutrophication effects of ongoing environmental and climate changes on the Millbrook reservoir simulated by the model ensemble SWAT-SALMO. The semi-arid Millbrook catchment-reservoir system provides drinking water to the north-eastern region of Adelaide, South Australia. The Soil and Water Assessment Tool (SWAT) simulated flow as well as nitrate and phosphate loadings originating from the catchment before entering the reservoir. The lake model SALMO received the simulated nitrate and phosphate loadings as input and determined daily phosphate, nitrate, and chlorophyll -a concentrati ons in the reservoir. This integrated modelling framework was key for simulating complex scenarios on impacts of future climate and land use changes on the whole catchment -reservoir system. The uncertainty of simulation results has been taken into accoun t by complex statistical algorithms, including the Sequential Uncertainty Fitting (SUFI2) of the SWAT calibration wizard, and multi -objective parameter optimisation of SALMO by means of the Hybrid Evolutionary Algorithm (HEA). In view of the large number of data processing steps required for the integrated simulations, the uncertainty assessment focused on the five best simulations results from the SWAT to be utilised for the parameter optimisation of SALMO. The uncertainty of the model ensemble has been qu antified as envelope of the fifty best iterations of nitrate, phosphate, and chlorophyll -a concentrations based on daily time steps for a typical “dry” and a typical “wet” year. The synergized envelop was further used to compare with the results of predict ion of impacts of climate and land use changes on the Millbrook catchment - reservoir system. Overall, the estimation of uncertainty bound from the catchment -reservoir model ensemble may improve the credibility of the model predictions to be further considered in decision- making

Optimalno upravljanje automatskim mjenjačem s velikim brojem stupnjeva prijenosa

Suvremeni automatski mjenjači s planetarnim prijenosnicima uključuju velik broj stupnjeva prijenosa (i do 10), s ciljem smanjenja potrošnje goriva i emisija štetnih plinova, te poboljšanja voznih performansi. U prisustvu složene strukture mjenjača s mnogostrukim kombinacijama i profilima uključivanja spojki, potrebno je postići optimalne karakteristike upravljanja mjenjačem. U radu se prvo prikazuje modeliranje dinamike pogona vozila, s naglaskom na razvoj metoda automatskog modeliranja i automatskog reduciranja reda modela automatskog mjenjača. Automatsko generiranje modela automatskog mjenjača punog reda provodi se izravno iz veznog dijagrama mjenjača, te se taj model koristi za automatsko generiranje modela mjenjača reduciranog reda za proizvoljno, korisnički-definirano stanje spojki. U nastavku rada provodi se numeričko optimiranje upravljačkih varijabli promjene stupnja prijenosa automatskog mjenjača primjenom pseudospektralne kolokacijske metode. Temeljni cilj ove aktivnosti je dobivanje uvida u optimalno ponašanje automatskog mjenjača, posebice kod složenijih promjena stupnja prijenosa s dvostrukim prijelazom, kod kojih se istovremeno koriste četiri spojke. Zatim se predlažu praktični, po odsječcima linearni profili upravljačkih varijabli, koji se definiraju temeljem uvida dobivenih primjenom općeg pristupa optimiranja upravljačkih varijabli. Optimalne vrijednosti parametara loma tako definiranih upravljačkih profila (tj. upravljačkih strategija) određuju se primjenom više-kriterijskog optimiranja, pri čemu se rezultati optimiranja koriste za vrednovanje predloženih upravljačkih strategija uz preporuke za primjenu. S ciljem dobivanja optimalnog rješenja u prisustvu statistički poznatih varijacija temeljnih parametara odziva spojki s aktuatorom, u radu se provodi i stohastičko robusno optimiranje parametara profila upravljačkih varijabli. Konačno, dobiveni rezultati koriste se za sintezu realnog sustava upravljanja u interakciji s komandama vozača i za razne uvjete vožnje. Korištenje razvijenih metoda za modeliranje i optimiranje demonstrira se na primjeru naprednog 10-brzinskog automatskog mjenjača

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Multi-objective optimisation of process parameters for laser-based directed energy deposition of a mixture of H13 and M2 steel powders on 4Cr5Mo2SiV1 steel

In present paper, a universal method for multi-objective parameter optimisation of additive manufacturing processes was proposed and successfully applied to laser-based directed energy deposition (DED) experiments with mixtures of H13 and M2 steel powders that were deposited on 4Cr5Mo2SiV1 hot work die steel. The DED experiments were designed and completed based on the response surface method with 13 groups of laser parameters. The microstructure of the deposited alloy steel was observed and its mechanical properties were tested. The deposited steel alloy achieved an ultimate tensile strength (UTS) of 1821 ± 30 MPa with a reasonable elongation of approximately 4.5%, and the bond strength specimens achieved a bond toughness of ∼10.66% with a moderate UTS (1329 ± 28 MPa). A multi-objective optimisation method was proposed based on response surfaces which were established according to microstructural characteristics and mechanical properties data. It provided a basis for achieving high strength or high toughness DED-fabricated steel alloys.</p

Robust low-order controller design for multimodal power oscillation damping using flexible AC transmission systems devices

Abstract Damping of multi-modal oscillation through supplementary control of a single FACTS device is illustrated in this paper. This often requires multiple feedback signals in a centralised multi-input single-output (MISO) framework for which extension of the classical control design approaches is not straight forward. Past contributions have either focused on decentralised design of low order PSS in a SISO or MIMO framework; or alternatively, on robust control design techniques which of course, result in higher order controllers. This paper is an attempt to design a fixed (low) order controller which is robust and is able to damp multiple swing modes with a single FACTS device. The control design problem is formulated as a multi-objective parameter optimisation and solved using a standard evolutionary optimisation technique. Possible post-contingency operating conditions are considered explicitly during the design phase itself to reduce the conservativeness. The present exercise is a step forward towards use of Wide Area Measurement Systems (WAMS) for closed-loop supplementary control (around the primary voltage and/or power flow control loop) of the FACTS devices to improve the transfer capacity of the existing corridors