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Zero energy houses and (near) zero energy buildings are among the most ambitious targets of society moving towards an energy efficient built environment. The "zero" energy consumption is most often judged on a yearly basis and should thus be interpreted as yearly net zero energy. The fully self sustainable, i.e. off-grid, home poses a major challenge due to the dynamic nature of building load profiles, ambient weather condition and occupant needs. In current practice, the off-grid status is accomplishable only by relying on backup generators or utilizing a large energy storage system. The research develops a risk based holistic system design method to guarantee a match between onsite sustainable energy generation and energy demand of systems and occupants. Energy self-sufficiency is the essential constraint that drives the design process. It starts with information collection of occupants' need in terms of life style, risk perception, and budget planning. These inputs are stated as probabilistic risk constraints that are applied during design evolution. Risk expressions are developed based on the relationships between power unavailability criteria and "damages" as perceived by occupants. A power reliability assessment algorithm is developed to aggregate the system underperformance causes and estimate all possible power availability outcomes of an off-grid house design. Based on these foundations, the design problem of an off-grid house is formulated as a stochastic programming problem with probabilistic constraints. The results show that inherent risks in weather patterns dominate the risk level of off-grid houses if current power unavailability criteria are used. It is concluded that a realistic and economic design of an off-grid house can only be achieved after an appropriate design weather file is developed for risk conscious design methods. The second stage of the research deals with the potential risk mitigation when an intelligent energy management system is installed. A stochastic model based predictive controller is implemented to manage energy allocation to sub individual functions in the off-grid house during operation. The controller determines in real time the priority of energy consuming activities and functions. The re-evaluation of the risk indices show that the proposed controller helps occupants to reduce damages related to power unavailability, and increase thermal comfort performance of the house. The research provides a risk oriented view on the energy self-sufficiency of off-grid solar houses. Uncertainty analysis is used to verify the match between onsite sustainable energy supply and demand under dynamic ambient conditions in a manner that reveals the risks induced by the fact that new technologies may not perform as well as expected. Furthermore, taking occupants' needs based on their risk perception as constraints in design evolution provides better guarantees for right sized system design.

<div>Efficient engine operation is a fundamental control problem in automotive applications. Robust control algorithms are necessary to achieve satisfactory, safe engine performance</div><div>at all operating conditions while reducing emissions. This thesis develops a framework for control architecture design to enable robust air handling system management.</div><div><br></div><div>The first work in the thesis derives a simple physics-based, control-oriented model for turbocharged lean burn engines which is able to capture the critical engine dynamics that are</div><div>needed to design the controller. The control-oriented model is amenable for control algorithm development and includes the impacts of modulation to any combination of four actuators: throttle valve, bypass valve, fuel rate, and wastegate valve. The controlled outputs: engine speed, differential pressure across throttle and air-to-fuel ratio are modeled as functions of selected states and inputs. Two validation strategies, open-loop and closed-loop are used to validate the accuracy of both nonlinear and linear versions of the control-oriented model. The relative gain array is applied to the linearized engine model to understand the degree of interactions between plant inputs and outputs as well as the best input-output pairing as a function of frequency. With strong evidence of high degree of coupling between inputs and outputs, a coordinated multiple-input multiple-output (MIMO) controller is hypothesized to perform better than a single-input single-output (SISO) controller. A framework to design robust model-based H1 MIMO controllers for any given linear plant, while considering state and output multiplicative uncertainties as well as actuator bandwidths is developed. The framework also computes the singular structure value, μ for the uncertain closed-loop system to quantify robustness, both in terms of stability and performance. The multi-tracking control problem targets engine speed, differential pressure across throttle as well as air-to-fuel ratio to achieve satisfactory engine performance while also preventing compressor surge and reducing engine emissions. A controller switching methodology using slow-fast controller decomposition and hysteresis at switching points is proposed to smoothly switch control authority between several MIMO controllers. The control design approach is applied to a truth-reference GT-Power engine model to evaluate the closed-loop controller performance. The engine response obtained using the robust MIMO controller is compared with that obtained using a state-of-the-art benchmark controller to evaluate the additional benefits of the MIMO controller.</div><div><br></div><div><div>In the second study, a robust 2-degree of freedom controller that commands eBooster speed to control air-to-fuel ratio, and a robust MIMO coordinated controller to control gas</div><div>exchange process in a diesel engine with electrified air handling architecture are developed. The MIMO controller simultaneously controls engine speed, mass fraction of the recirculated exhaust gas as well as air-to-fuel ratio. The actuators available for control in the engine include the exhaust gas recirculation valve, exhaust throttle valve, fuel injection rate, eBooster speed, eBooster bypass valve. To design the robust eBooster controller, the input-output relationship between eBooster speed and air-to-fuel ratio is estimated using system identification techniques. The robust MIMO controller is synthesized using a physics-based mean value control-oriented engine model that accurately represents the high-fidelity GT-Power model. In the first control strategy, the robust eBooster controller is added to an already existing stock engine control unit while in the second control strategy, the stock engine control unit is replaced with the multiple-input multiple-output controller. The two control architectures are tested under different operating conditions to evaluate the controller performance. Simulation results with the control architectures developed in the thesis are compared to a baseline engine configuration, where the engine operates without eBooster. Although it is observed that both these control algorithms significantly improve engine performance as compared to the baseline configuration, MIMO controller provides the best engine performance overall.</div></div>

Driven by intense competition, increasing operational cost and strict environmental regulations, the modern process and energy industry needs to find the best possible way to adapt to maintain profitability. Optimization of control and operation of the industrial systems is essential to satisfy the contradicting objectives of improving product quality and process efficiency while reducing production cost and plant downtime. Use of optimization not only improves the control and monitoring of assets but also offers better coordination among different assets. Thus, it can lead to considerable savings in energy and resource consumption, and consequently offer a reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks that can be integrated with the existing industrial automation platforms to benefit from optimal control and operation. The main focus of this dissertation is the use of different process models, soft sensors and optimization techniques to improve the control, diagnostics and decision support for the process and energy industry. A generic architecture for an optimal control, diagnostics and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies within the energy-intensive pulp and paper industry and the promising micro-combined heat and power (CHP) industry are selected to demonstrate the learning system. One of the main challenges in this research arises from the marked differences between the case studies in terms of size, functions, quantity and structure of the existing automation systems. Typically, only a few pulp digesters are found in a Kraft pulping mill, but there may be hundreds of units in a micro-CHP fleet. The main argument behind the selection of these two case studies is that if the proposed learning system architecture can be adapted for these significantly different cases, it can be adapted for many other energy and process industrial cases. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control and diagnostics methods are studied for continuous pulp digesters, whereas mathematical modelling, model adaptation and diagnostics techniques are explored for the micro-CHP fleet. / FUDIPO – FUture DIrections for Process industry Optimization

A common choice of bearing for industrial applications such as turbomachinery and rotating compressors is the Tilting-Pad Journal Bearing (TPJB) due to its excellent stability properties. TPJB's are however limited by the reduction of damping in the fluid film at high velocities. In order to overcome this, the Active Tilting-Pad Journal Bearing (ATPJB) has been developed. By adding the possibility of high-pressure radial oil injection through servo-valves which can be controlled via a feedback-loop control system, the classically purely mechanical TPJB becomes a mechatronic device called ATPJB.  The objective of this project is to conduct an experimental evaluation of the dynamical behavior of the ATPJB test rig located at the Technical University of Denmark, use the experimental results to modify the previously developed dynamical model which is used for the calculation of a model-based control system. The control system is to be implemented and experimentally validated at high velocities. Improvements made to the test rig in order to achieve high velocities have been documented and described in this work. The mathematical modeling of the individual components, reduction methods, and the global system assembly is covered with an extensive overview. Parameters of the model have been made frequency dependant in order to have an accurate model, resulting in good agreement with experimental data over a wider operational range. With the implemented Linear Quadratic Gaussian controller it is shown that ATPJB has extended operational range compared to TPJB and shows reduction of vibrations over rotational speeds spanning from 1000 RPM to 10,000 RPM. The ATPJB-technology, as it is implemented in this project, does not improve frictional losses in the system. It is argued that the added sensing and actuating systems inherited in the ATPJB technology make the technology highly suitable for the ideas of Industry 4.0 and also allows for the implementation of Early Fault Diagnosis which gives an economical incitement to invest in ATPJB-technology.

A South African GSM telecommunications market consisting of two incumbents and an entering third player, is modelled utilising a non-linear, system dynamics approach. The model calculates subscriber choice based on a calculated utility. The utility is used to obtain a probability which is fed into a Bass type differential equation relating the different states in the model to their time derivatives. The model encapsulates all the prominent postpaid price plans in the market, as well as five different demographic market segments. Model Predictive Control is used to synthesise a linear feedback controller which uses the observed market state to optimally determine a price time series for one of the operators’ products. The series will maximise Average Revenue Per User (ARPU) for the operator over the simulation time interval. Besides ARPU, the controller is also able to increase total revenue and minimise churn over the simulated interval for the optimising operator, and thus provides valuable decision support to the marketing management of such an operator. / Dissertation (MEng (Electronic))--University of Pretoria, 2004. / Electrical, Electronic and Computer Engineering / unrestricted

Ausgangspunkt der hier vorgestellten Entwicklungen ist eine am ICM – Institut Chemnitzer Maschinen- und Anlagenbau e.V. entwickelte adaptronische Werkzeugmaschinen-Hauptspindel zur variablen Einstellung der Lagervorspannkräfte. Diese neuartige Werkzeugmaschinenspindel bietet Anwendungspotentiale in den Bereichen: verbessertes Hochdrehverhalten, erhöhte Flexibilität des Bearbeitungsspektrums, Lebensdauererhöhung der Wälzlager durch verbessertes Bohr-Roll-Verhältnis sowie einer optimierten Spindeldynamik durch gezielte Verschiebung der Eigenfrequenzen. Ziel war die Entwicklung einer möglichst dynamischen und genauen Ansteuerung des in der Hauptspindel enthaltenen hydraulischen Vorspannelementes. Dieser Beitrag fasst das Vorgehen und die bisherigen Ergebnisse zusammen. Auf die Eigenentwicklung eines hydraulischen Aktuators sollte verzichtet werden, sodass die Ansteuerung über ein direktgesteuertes Regelventil erfolgt. Mit einer einfachen Druckregelung mit PID-Regler konnten nur unzureichende Ergebnisse hinsichtlich Dynamik und Regelkreisstabilität erzielt werden. Hinzu kommt die geringe Genauigkeit der Krafteinstellung bei Anwendung einer einfachen Druckregelung. Zur Verbesserung der Ansteuerungsdynamik und -genauigkeit des Vorspannelementes sollten erweiterte Regelungsstrukturen zur Anwendung kommen. Im Projekt wurde hierzu ein modellbasierter Regler entwickelt und in einer dem Experiment vorgelagerten Untersuchung an einem Systemsimulationsmodell getestet und optimiert. Ausgangspunkt für die simulationsgestützte Reglerauslegung war die Entwicklung eines Regelstreckenmodells in SimulationX. Die Modellentwicklung umfasste den Vergleich zweier grundlegender Modellierungsansätze, eines bidirektionalen Modells mit einem linearen Signalflussmodell hinsichtlich Modellgenauigkeit und Rechenperformance. Zur Untersuchung der Reglerfunktionalität sowie zur Optimierung der Reglerparameter wurde die in SimulationX vorhandene COM-Schnittstelle genutzt und eine vereinfachte Optimierungsfunktion in Matlab umgesetzt. Für die experimentelle Validierung des Ansteuerungssystems wurde die entwickelte modellbasierte Reglervorsteuerung in der Programmierumgebung LabView umgesetzt. Erste Tests erfolgten zunächst an der stehenden Hauptspindel. Diese zeigten ein sehr dynamisches und stabiles Regelungsverhalten, sodass neben einfachen Sprungvorhaben auch Trajektorienvorgaben mit hoher Dynamik geregelt werden können. Es konnte eine deutliche Verbesserung gegenüber der zu Beginn vorhandenen einfachen Druckregelung erzielt werden. Umfangreichere experimentelle Untersuchungen an der drehenden Spindel für unterschiedliche Regelungs- und Bearbeitungsszenarien sollen in Zukunft durchgeführt werden. Dabei stehen vor allem die Verkürzung der Hochdrehzeiten für Bearbeitungsprozesse mit geringer Lagervorspannung sowie die anwendungsspezifische Optimierung der Spindeldynamik durch Eigenfrequenzverschiebungen im Vordergrund. / The starting point of the developments presented here is an adaptronic machine tool main spindle developed at the ICM - Institute Chemnitz Machine and Plant Construction e.V. for variable adjustment of the bearing preload forces. This new type of machine tool spindle offers application potential in the areas of: improved high-speed behavior, increased flexibility of the machining spectrum, increased service life of the roller bearings through improved drilling-rolling ratio and optimized spindle dynamics through targeted shifting of the natural frequencies. The aim was to develop the most dynamic and precise control possible for the hydraulic pretensioning element contained in the main spindle. This article summarizes the procedure and the results so far. The in-house development of a hydraulic actuator should be avoided, so that the control takes place via a directly controlled control valve. With a simple pressure control with a PID controller, only insufficient results could be achieved in terms of dynamics and control loop stability. Added to this is the low accuracy of the force setting when using a simple pressure control. Extended control structures should be used to improve the control dynamics and accuracy of the preload element. For this purpose, a model-based controller was developed in the project and tested and optimized on a system simulation model in an investigation prior to the experiment. The starting point for the simulation-based controller design was the development of a controlled system model in SimulationX. The model development included the comparison of two basic modeling approaches, a bidirectional model with a linear signal flow model in terms of model accuracy and computational performance. The COM interface available in SimulationX was used to examine the controller functionality and to optimize the controller parameters, and a simplified optimization function was implemented in Matlab. For the experimental validation of the control system, the developed model-based controller pre-control was implemented in the LabView programming environment. The first tests were initially carried out on the stationary main spindle. These showed a very dynamic and stable control behavior, so that in addition to simple jump projects, trajectory specifications can also be controlled with high dynamics. A clear improvement could be achieved compared to the simple pressure control that was available at the beginning. More extensive experimental investigations on the rotating spindle for different control and processing scenarios are to be carried out in the future. The main focus here is on reducing the ramp-up times for machining processes with low bearing preload and the application-specific optimization of the spindle dynamics through natural frequency shifts.

Quadruped robots offer distinct advantages in navigating challenging terrains due to their flexible and shock-absorbing characteristics. This flexibility allows them to adapt to uneven surfaces, enhancing their maneuverability. In contrast, rigid robots excel in tasks that require speed and precision but are limited in their ability to navigate complex terrains due to their restricted motion range. Another category of robots, known as soft robots, has gained attention for their unique attributes. Soft robots are characterized by their lightweight and cost-effective design, making them appealing for various applications. Recent advancements have made significant strides in practical control strategies for soft quadruped robots, particularly in diverse and unpredictable environments. An emerging approach in enhancing the autonomy of robots is through reinforcement learning. While this approach shows promise in enabling robots to learn and adapt to their surroundings, it necessitates rigorous training and must exhibit robustness in real-world scenarios. Moreover, a significant hurdle lies in bridging the gap between simulations and reality, as models trained in idealized virtual environments often struggle to perform as expected when deployed in the physical world. This thesis aims to address these challenges by optimizing the control of soft quadruped robots using a model-based reinforcement learning approach. The primary goal is to refine the gait control of these robots, taking into account the complexities encountered in real-world environments. The report covers the implementation of model-based reinforcement learning, including simulation setup, reward design, and policy refinement. Results show improved training efficiency and autonomous behavior, confirming the method’s effectiveness in enhancing soft quadruped robot capabilities.It is important to note that this report provides a concise summary of the thesis results due to the word limit imposed by the Department of Machine Design. For a comprehensive understanding of the research and its implications, the complete version is attached separately here. / Fyrbenta robotar är tack vare deras flexibla och stötdämpande egenskaper är väl lämpade att navigera utmanande terräng. Deras struktur möjliggör anpassning till ojämnheter i underlaget och bidrar till att öka deras rörelseförmåga. I kontrast utmärker sig stela robotar som det bästa valet för uppgifter som kräver snabbhet och precision, men deras förmåga att navigera komplex terräng är begränsad av deras rörelseomfång. En annan typ av robot, så kallade mjuka robotar, har nyligen uppmärksammats för sina unika egenskaper. Dessa robotar kännetecknas av en kostnadseffektiv lättviktsdesign, vilket gör dem attraktiva för många olika användningsområden. Nyligen har betydelsefulla framsteg gjorts inom kontroll av mjuka fyrbenta robotar, framför allt vad gäller kontroll i varierade miljöer. En av de huvudsakliga utmaningarna för att öka robotars autonomi är förstärkningsinlärning. Även om denna teknik är lovande för att ge robotar förmågan att lära sig och anpassa sig efter sin omgivning, kräver den omfattande träning samt måste uppvisa robusthet i verkliga scenarion. Ett större hinder är dessutom klyftan mellan simulation och verklighet, då modeller som tränats i ideella simuleringar ofta presterar sämre än väntat i den fysiska världen. Detta examensarbete behandlar dessa utmaningar genom att implementera en modellbaserad förstärkningsinlärningsmetod för kontroll av fyrbenta robotar, med det primära målet att förfina gångkontrollen för dessa robotar med hänsyn till de komplexa beteenden som uppstår i verkliga miljöer. Denna rapport behandlar implementeringen av modellbaserad förstärkningsin lärning samt simulering, belöningsdesign och policyförfining. Resultat visar på en förbättrad inlärningsförmåga och bättre autonomt beteende, vilket gör metoden lämplig för att förbättra prestandan av mjuka fyrbenta robotar. Var god att notera att denna rapport endast ger en nedkortad sammanfattning av forskningen och dess resultat på grund av krav från institutionen för maskinkonstruktion. En fullständig version innehållande mer detaljer kring studien och dess konsekvenser bifogas här.

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A recent development in the design of control system for a jet engine is to use a suitable, fast and accurate model running on board. Development of linear models is particularly important as most engine control designs are based on linear control theory. Engine control performance can be significantly improved by increasing the accuracy of the developed model. Current state-of-the-art is to use piecewise linear models at selected equilibrium conditions for the development of set point controllers, followed by scheduling of resulting controller gains as a function of one or more of the system states. However, arriving at an effective gain scheduler that can accommodate fast transients covering a wide range of operating points can become quite complex and involved, thus resulting in a sacrifice on controller performance for its simplicity. This thesis presents a methodology for developing a control oriented analytical linear model of a jet engine at both equilibrium and off-equilibrium conditions. This scheme requires a nonlinear engine model to run onboard in real time. The off-equilibrium analytical linear model provides improved accuracy and flexibility over the commonly used piecewise linear models developed using numerical perturbations. Linear coefficients are obtained by evaluating, at current conditions, analytical expressions which result from differentiation of simplified nonlinear expressions. Residualization of the fast dynamics states are utilized since the fast dynamics are typically outside of the primary control bandwidth. Analytical expressions based on the physics of the aerothermodynamic processes of a gas turbine engine facilitate a systematic approach to the analysis and synthesis of model based controllers. In addition, the use of analytical expressions reduces the computational effort, enabling linearization in real time at both equilibrium and off-equilibrium conditions for a more accurate capture of system dynamics during aggressive transient maneuvers. The methodology is formulated and applied to a separate flow twin-spool turbofan engine model in the Numerical Propulsion System Simulation (NPSS) platform. The fidelity of linear model is examined by validating against a detailed nonlinear engine model using time domain response, the normalized additive uncertainty and the nu-gap metric. The effects of each simplifying assumptions, which are crucial to the linear model development, on the fidelity of the linear model are analyzed in detail. A case study is performed to investigate the case when the current state (including both slow and fast states) of the system is not readily available from the nonlinear simulation model. Also, a simple model based control is used to illustrate benefits of using the proposed modeling approach.