The primary objective of fusion research is to realize a thermonuclear fusion power plant. The main method to confine the hot plasma is by using a magnetic field. The reversed-field pinch is a type of magnetic confinement device which suffers from variety of magnetohy- drodynamic (MHD) instabilities. A particular unstable mode that is treated in this work is the resistive wall mode (RWM), which occurs due to the current gradient in the RFP and has growth rates of the order of the magnetic diffusion time of the wall. Application of control engineering tools appears to allow a robust and stable RFP operation.A model-based approach to stabilize the RWMs is pursued in this thesis. The approach consists of empirical modeling of RWMs using a class of subspace identification methodology. The obtained model is then used as a basis for a model based controller. In particular the first experimental results of using a predictive control for RWM stabilization are obtained. It is shown that the formulation of the model based controller allows the user to incorporate several physics relevant phenomena along with the stabilization of RWM. Another use of the model is shown to estimate and compensate the inherent error field. The results are encouraging, and the methods appear to be generically useful as research tools in controlled magnetic confinement fusion. / Fusionsforskningens primära mål är att förverkliga en ny typ av kraftverk baserade på termonukleär fusion. Den viktigaste metoden för att innesluta det heta plasmat är användandet av  magnetfält. ”Reverserat-fält pinch” (RFP) är en typ av anläggning för magnetisk inneslutning av fusionsplasma som uppvisar ett flertal magneto-hydrodynamiska instabiliteter. En specifik instabil mod som behandlas i detta arbete är”resistiv-vägg” moden (RWM). Den orsakas av strömgradienten i RFPn och tillväxer med en tidskonstant som är av samma storleksordning som magnetfältets diffusionstid i det omgivande metallskalet.  Tillämpning av verktyg från reglerteknikområdet förefaller tillåta en robust och stabil RFP drift. I detta arbete används ett modell-baserat tillvägagångssätt för kompensering av RWM. Det innefattar empirisk modellering av RWM med användning av ”subspace” system-identifieringsmetoder. Den erhållna modellen används sedan som grund för en modell-baserad regulator. De första experimentella resultaten från modell-prediktiv kompensering av RWM har erhållits.  I detta arbete har också visats att formuleringen av den modellbaserade regulatorn tillåter användaren att integrera flera relevanta fysikaliska aspekter förutom RWM. Ytterligare en användning av modellen är för att göra uppskattning och kompensering av avvikelser i anläggningens magnetfält, så kallade fält-fel. Resultaten är uppmuntrande, och det förefaller som om de undersökta metoderna är allmänt användbara som verktyg för forskning om magnetisk inneslutning av fusionsplasma. / <p>QC 20170202</p>

Retrofits of hydraulic and mechanical deep drawing presses often stop with the exchange of the electrical and the hydraulic parts. But that is only half the job. The use of high definition control electronics, faster CPUs and more dynamic hydraulic actuators, offers the opportunity of redesigning the already existing control concepts of the press. In this paper we present how the performance of the press, i.e. the control quality, can be increased for hydraulic ram and cushion axes. The improvement in control quality is achieved through the use of intelligent closed-loop and open-loopcontrol algorithms. Therefore, creasing and crack formation can be reduced, since enhancements in control quality have direct influence on the quality of the forming process. Results will be shown for hydraulic drawing cushion control, i.e. pressure control, as well as for hydraulic ram control, i.e. position, velocity and parallelism control. We present findings for hydraulic cushion control of a mechanical press type Arisa S-4-1600-470-230-LDE (link-drive press with 10 hydraulic cushions) and for ram- / cushion-control of hydraulic press type Müller-Weingarten ZE2100 (multi-curve press with 8-point cushion).

In the intensive care units, hyperglycaemia among the critically ill is associated with poor outcomes. Many studies have been done on managing hyperglycaemia in the critically ill. Patients in the ICU continue to benefit from the outcome of extensive studies including several randomized clinical trials on glycaemic control with intensive insulin therapy. Tight glycaemic control has now emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and cost. Although the debate on tight glycaemic control is on going, managing glycaemic level in ICUs is gaining widespread acceptance as the adverse effects are well known. However, in the less acute wards, to date there have only been a single randomized, controlled study to examine the benefit of glycaemic control. Patients in the less acute wards do not receive the same level of care, as glycaemic control is not regarded as important and not a priority. Glycaemic goals in the less acute wards are often judged based on clinical experience rather than adhering to a standard protocol or a treatment guideline. It is important that patients in the less acute wards received the level of care as practised in the ICU. If hyperglycaemia worsens outcome in the ICU, a similar effect is seen within less acute wards. Hence, tight glycaemic control needs to be extended in the less critical setting as well. To support the establishment of a control protocol for patients in less acute wards, a method that has been successful in the critical care and can be adapted to the less acute wards, is the model based or model-derived control protocol. Model-based protocol can deliver a safe and effective patient-specific control, which means the glycaemic control protocol can be devised to each individual patient. Hence, a physiological model that represents the glucose-insulin regulatory system is presented in this thesis. The developed model, Intensive Control Insulin-Nutrition-Glucose (ICING) is based on the best aspects of two previous clinically-validated glucose-insulin models.

In the wake of rising energy costs, there is a critical need for sustainable energy management of commercial and residential buildings. Buildings consume approximately 40% of total energy consumed in the US, and current methods to reduce this level of consumption include energy monitoring, smart sensing, and advanced integrated building control. However, the building industry has been slow to replace current PID and rule-based control strategies with more advanced strategies such as model-based building control. This is largely due to the additional cost of accurately modeling the dynamics of the building and the general uncertainty that model-based controllers can be reliably used in real conditions. The first half of this thesis addresses the challenge of constructing accurate grey-box building models for control using model identification. Current identification methods poorly estimate building model parameters because of the complexity of the building model structure, and fail to do so quickly because these methods are not scalable for large buildings. Therefore, we introduce the notion of parameter identifiability to determine those parameters in the building model that may not be accurately estimated and we use this information to strategically improve the identifiability of the building model. Finally, we present a decentralized identification scheme to reduce the computational effort and time needed to identify large buildings. The second half of this thesis discusses the challenge of using uncertain building models to reliably control building temperature. Under real conditions, building models may not match the dynamics of the building, which directly causes increased building energy consumption and poor thermal comfort. To reduce the impact of model uncertainty on building control, we pose the model-based building control problem as a robust control problem using well-known H1 control methods. Furthermore, we introduce a tuning law to reduce the conservativeness of a robust building control strategy in the presence of high model uncertainty, both in a centralized and decentralized building control framework.

No description available.

A measurement-based reduced-order heat transfer modeling framework is developed to optimize cooling costs of dynamic and virtualized data centers. The reduced-order model is based on a proper orthogonal decomposition-based model order reduction technique. For data center heat transfer modeling, the framework simulates air temperatures and CPU temperatures as a parametric response surface with different cooling infrastructure design variables as the input parameters. The parametric framework enables an efficient design optimization tool and is used to solve several important problems related to energy-efficient thermal design of data centers. The first of these problems is about determining optimal response time during emergencies such as power outages in data centers. To solve this problem, transient air temperatures are modeled with time as a parameter. This parametric prediction framework is useful as a near-real-time thermal prognostic tool. The second problem pertains to reducing temperature monitoring cost in data centers. To solve this problem, transient air temperatures are modeled with spatial location as the parameter. This parametric model improves spatial resolution of measured temperature data and thereby reduces sensor requisition for transient temperature monitoring in data centers. The third problem is related to determining optimal cooling set points in response to dynamically-evolving heat loads in a data center. To solve this problem, transient air temperatures are modeled with heat load and time as the parameters. This modeling framework is particularly suitable for life-cycle design of data center cooling infrastructure. The last problem is related to determining optimal cooling set points in response to dynamically-evolving computing workload in a virtualized data center. To solve this problem, transient CPU temperatures under a given computing load profile are modeled with cooling resource set-points as the parameters.

Autogenous (AG) milling is utilised around the world for rst stage particle size reduction. The system exhibits highly non-linear behaviour in addition to being subject to unmeasured variability associated with most ore bodies. Anglo American Platinum aimed at improving online optimisation of the circuit by implementing industrial model predictive control to reduce system variability and continuously drive towards the optimal operating point within system constraints. A dimensional analysis of the circuit was conducted to explain the relationships between the various milling parameters discussed in the literature survey. The measured variables used in the analysis satis ed Buckingham's theorem, indicating that a complete subset of dimensionless groups were present and suitably able to describe process movement. These relationships were used as a reference point in determining the dynamic step response models between these variables necessary for model based control. The industrial dynamic matrix controller commissioned on the AG mill resulted in a 66 % reduction in power and a 40 % reduction in load. These are the main controlled variables of the mill. The controller also managed to reduce its objective function, e ective power utilisation, by 11 %. This stability improvement enabled a test campaign where the mill was controlled at various operating regions in order to establish the conditions conducive to the nest product size at a given mill feed rate. Moving the mill's operating region from the benchmarked plant to this optimal grind environment (at benchmarked variability) provided an estimated potential recovery increase of 0.27 % (absolute) due to better precious metal liberation. Stabilising the mill at this point with the model predictive controller resulted in a further 0.04 % potential recovery increase (absolute). The 0.31 % potential recovery increase is estimated at a monetary value of $93.1 million per annum. Copyright / Dissertation (MEng)--University of Pretoria, 2013. / Chemical Engineering / unrestricted

Retrofits of hydraulic and mechanical deep drawing presses often stop with the exchange of the electrical and the hydraulic parts. But that is only half the job. The use of high definition control electronics, faster CPUs and more dynamic hydraulic actuators, offers the opportunity of redesigning the already existing control concepts of the press. In this paper we present how the performance of the press, i.e. the control quality, can be increased for hydraulic ram and cushion axes. The improvement in control quality is achieved through the use of intelligent closed-loop and open-loopcontrol algorithms. Therefore, creasing and crack formation can be reduced, since enhancements in control quality have direct influence on the quality of the forming process. Results will be shown for hydraulic drawing cushion control, i.e. pressure control, as well as for hydraulic ram control, i.e. position, velocity and parallelism control. We present findings for hydraulic cushion control of a mechanical press type Arisa S-4-1600-470-230-LDE (link-drive press with 10 hydraulic cushions) and for ram- / cushion-control of hydraulic press type Müller-Weingarten ZE2100 (multi-curve press with 8-point cushion).

The control of batch pulp digesters is hampered by insufficient measurements as well as nonlinearity and weak correlation between consecutive cooks. This makes a model-based approach to control attractive. Due to the age of the industry, many legacy controllers are in place on digesters around the world. The theoretical variance obtained by Monte Carlo modelling of a new controller is used as a benchmark for performance comparison between an old control system (S-factor) and a new model based controller developed by the University of Pretoria (the UP controller). This study covers the development of the controller, Monte Carlo modelling of the old and new controllers and in-situ testing of the UP controller on an operating digester. During Monte Carlo simulation, the UP controller outperformed the legacy controller, obtaining a theoretical overall variance of 3,07 (which will be used as the baseline for performance measurement) while also showing larger responses to tuning factors. The S-factor performed at 6,8 times the theoretical optimum variance during in situ testing, while the UP controller performed at 3,9 times the theoretical optimum (43% better than the S-factor controller). An average error 90% lower than that of the S-factor controller was obtained when using the UP controller. Additional benefits of the new controller include easy inclusion of new measurements and clear relations between the tuning parameters used and the conditions in the digester. / Dissertation (MEng (Control))--University of Pretoria, 2003. / Chemical Engineering / unrestricted

No description available.