Engineering problems have characteristics such as a large number of variables, non-linear, computationally expensive, complex and black-box (i.e. unknown internal structure). These characteristics prompt difficulties for existing optimisation techniques. A consequence of this is that the required optimisation time rapidly increases beyond what is practical. There is a needfor dedicated techniques to exploit the power of mathematical optimisation tosolve engineering problems. The objective of this thesis is to investigate thisneed within the field of automation, specifically for control optimisation ofautomated systems.The thesis proposes an optimisation algorithm for optimising the controlof automated interacting production stations (i.e. independent stations thatinteract by for example material handling robots). The objective of the optimisation is to increase the production rate of such systems. The non-separable nature of these problems due to the interactions, makes them hard to optimise.The proposed algorithm is called the Constructive Cooperative CoevolutionAlgorithm (C3). The thesis presents the experimental evaluation of C3, bothon theoretical and real-world problems. For the theoretical problems, C3 istested on a set of standard benchmark functions. The performance, robustness and convergence speed of C3 is compared with the algorithms. This shows that C3 is a competitive optimisation algorithm for large-scale non-separable problems.C3 is also evaluated on real-world industrial problems, concerning thecontrol of interacting production stations, and compared with other optimisation algorithms on these problems. This shows that C3 is very well-suited for these problems. The importance of considering the energy consumption and equipment wear, next to the production rate, in the objective function is also investigated. This shows that it is crucial that these are considered to optimise the overall performance of interacting production stations.

The Cal Poly CubeSat Laboratory (CPCL) is currently facing unprecedented engineering challenges—both technically and programmatically—due to the increasing cost and complexity of CubeSat flight missions. In responding to recent RFPs, the CPCL has been forced to find commercially available solutions to entire mission critical spacecraft subsystems such as propulsion and attitude determination & control, because currently no in-house options exist for consideration. The commercially available solutions for these subsystems are often extremely expensive and sometimes provide excessively good performance with respect to mission requirements. Furthermore, use of entire commercial subsystems detracts from the hands-on learning objectives of the CPCL by removing engineering responsibility from students. Therefore, if these particular subsystems can be designed, tested, and integrated in-house at Cal Poly, the result would be twofold: 1) the space of missions supportable by the CPCL under tight budget constraints will grow, and 2) students will be provided with unique, hands-on guidance, navigation, and control learning opportunities. In this thesis, the CPCL’s attitude determination and control system design and analysis toolkit is significantly improved to support in-house ADCS development. The toolkit—including the improvements presented in this work—is then used to complete the existing, partially complete CPCL ADCS design. To fill in missing gaps, particular emphasis is placed on guidance and control algorithm design and selection of attitude actuators. Simulation results show that the completed design is competitive for use in a large class of small satellite missions for which pointing accuracy requirements are on the order of a few degrees.

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Power limits on future high-performance computing (HPC) systems will constrain applications. However, HPC applications do not consume constant power over their lifetimes. Thus, applications assigned a fixed power bound may be forced to slow down during high-power computation phases, but may not consume their full power allocation during low-power I/O phases. This paper explores algorithms that leverage application semantics-phase frequency, duration and power needs-to shift unused power from applications in I/O phases to applications in computation phases, thus improving system-wide performance. We design novel techniques that include explicit staggering of applications to improve power shifting. Compared to executing without power shifting, our algorithms can improve average performance by up to 8% or improve performance of a single, high-priority application by up to 32%.

Short Large-Amplitude Magnetic Structures (SLAMS) have been observedby spacecraft near Earth’s quasi-parallel bow shock. They arecharacterized by a short and sudden increase of the magnetic field,usually by a factor of 2 or more. SLAMS studies have previously beenlimited to small sample sizes because SLAMS were identified throughmanual inspection of the spacecraft data. This makes it difficult to drawgeneral conclusions and the subjective element complicates collaborationbetween researchers. A solution is presented in this thesis; anautomatic SLAMS detection algorithm. We investigate several movingwindowmethods and measure their performance on a set of manuallyidentified SLAMS. The best algorithm is then used to identify 98406SLAMS in data from the Magnetospheric Multiscale (MMS) mission. Ofthose, 66210 SLAMS were detected when the Fast Plasma Investigation(FPI) instrument was active. Additionally, we are interested in knowingwhether a detected SLAMS is located in the foreshock or magnetosheath.Therefore, we implement a Gaussian mixture model classifier,based on hierarchical clustering of the FPI data, that can separatebetween the four distinct regions of the magnetosphere that MMSencounters; magnetosphere, magnetosheath, solar wind and (ion) foreshock.The identified SLAMS are compiled into a database which holdstheir start and stop dates, positional coordinates, B-field informationand information from the magnetospheric classifier to allow for easyfiltering to a specific SLAMS population. To showcase the potentialof the database we use it to perform preliminary statistical analysison how the properties of SLAMS are affected by its spatial and/ormagnetospheric location. The database and Matlab implementationare available on github: https://github.com/cfognom/MMS_SLAMS_detection_and_magnetospheric_classification. / Korta magnetiska strukturer med hög amplitud (SLAMS) har observeratsav satelliter nära jordens kvasi-parallella bogchock. En kortoch plötslig höjning av magnetfältsstyrkan är ett typiskt drag förSLAMS, vanligtvis med en faktor 2 eller mer. Forskning om SLAMShar tidigare varit begränsad till mindre fallstudier eftersom SLAMSidentifierats genom manuell inspektion av satellitdata. Detta gör detsvårt att dra generella slutsatser och det subjektiva elementet försvårarsamarbetet mellan forskare. En lösning till detta problem presenteras idenna avhandling; en automatisk identifieringsalgoritm för SLAMS. Viundersöker flera metoder och mäter deras prestanda på en uppsättningmanuellt identifierade SLAMS. Den bästa algoritmen används sedan föratt identifiera 98406 SLAMS i data från MMS-uppdraget. Av dessa upptäcktes66210 SLAMS när FPI-instrumentet var aktivt. Vi är dessutomintresserade av att veta om en upptäckt SLAMS finns i förshocken ellermagnetoskiktet. Därför implementerar vi en Gaussisk klassificeraresom bygger på hierarkisk klustring av FPI-data. Den kan separerade fyra distinkta regionerna av magnetosfären som MMS observerar;magnetosfär, magnetoskikt, solvind och (jon) förchock. De identifieradeSLAMS:en sammanställs till en databas som innehåller deras start- ochstoppdatum, positionskoordinater, B-fältsinformation och informationfrån magnetosfärsklassificeraren för att möjliggöra enkel filtrering tillen specifik SLAMS-population. För att visa potentialen av databasenutför vi en preliminär statistisk undersökning av hur egenskapernaav SLAMS påverkas av deras rumsliga och/eller magnetosfäriska position.Databasen och Matlab-implementationen är tillgängliga på Github:https://github.com/cfognom/MMS_SLAMS_detection_and_magnetospheric_classification.

Two dimensional magnetic recording (TDMR) achieves high areal densities by reducing the size of a bit comparable to the size of the magnetic grains resulting in two dimensional (2D) inter symbol interference (ISI) and very high media noise. Therefore, it is critical to handle the media noise along with the 2D ISI detection. In this paper, we tune the generalized belief propagation (GBP) algorithm to handle the media noise seen in TDMR. We also provide an intuition into the nature of hard decisions provided by the GBP algorithm. The performance of the GBP algorithm is evaluated over a Voronoi based TDMR channel model where the soft outputs from the GBP algorithm are used by a belief propagation (BP) algorithm to decode low-density parity check (LDPC) codes.