This thesis deals with the design of a lecture model of an inverse pendulum controled by a flywheel, which is a system of an unstable beam with an electromotor and a reaction wheel at its end. The moment of motor acting on the flywheel also causes a moment acting on the beam, which is the way the system is controled. The device works connected to a personal computer.

In this thesis an equation of heat conduction including phase and structural changes is derived, involving various boundary conditions. It seems to be the most suitable to calculate the equation by enthalpy method. In this equation not only enthalpy occurs, but also the temperature, and in consequence the relationship between these variables is quite complicated. In this paper I use the values measured or calculated using solidifcation models. The calculation is implemented in Matlab Simulink, which is a very popular blocks scheme in common practice of regulation. The calculation is based on steady state set up with help of experts and as a result, the program could be put into practice. The program calculates the intensity of cooling according to the initial casting speed, casting inlet temperature and the desired temperature curves. The rate of inuence of cooling can be changed according to the given criteria. The thesis compares the surface temperatures and cooling in the case of a predictive controller is or is not applied in the program.

<div>Collision avoidance design plays an essential role in autonomous vehicle technology. It's an attractive research area that will need much experimentation in the future. This research area is very important for providing the maximum safety to automated vehicles, which have to be tested several times under different circumstances for safety before use in real life. This thesis proposes a method for designing and presenting a collision avoidance maneuver by using a model predictive controller with a moving obstacle for automated vehicles. It consists of a plant model, an adaptive MPC controller, and a reference trajectory. The proposed strategy applies a dynamic bicycle model as the plant model, adaptive model predictive controller for the lateral control, and a custom reference trajectory for the scenario design. The model was developed using the Model Predictive Control Toolbox and Automated Driving Toolbox in Matlab. Builtin tools available in Matlab/Simulink were used to verify the modeling approach and analyze the performance of the system. The major contribution of this thesis work was implementing a novel dynamic obstacle avoidance control method for automated vehicles. The study used validated parameters obtained from previous research. The novelty of this research was performing the studies using a MPC based controller instead of a sliding mode controller, that was primarily used in other studies. The results obtained from the study are compared with the validated models. The comparisons consisted of the lateral overlap,lateral error, and steering angle simulation results between the models. Additionally,this study also included outcomes for the yaw angle. The comparisons and other outcomes obtained in this study indicated that the developed control model produced reasonably acceptable results and recommendations for future studies.</div>

This dissertation is focused on the modeling and robust model-based control of high degree-of-freedom (DoF) systems. While most of the contributions are applicable to any difficult-to-model system, this dissertation focuses specifically on applications to large-scale soft robots because their many joints and pressures constitute a high-DoF system and their inherit softness makes them difficult to model accurately. First a joint-angle estimation and kinematic calibration method for soft robots is developed which is shown to decrease the pose prediction error at the end of a 1.5 m robot arm by about 85\%. A novel dynamic modelling approach which can be evaluated within microseconds is then formulated for continuum type soft robots. We show that deep neural networks (DNNs) can be used to approximate soft robot dynamics given training examples from physics-based models like the ones described above. We demonstrate how these machine-learning-based models can be evaluated quickly to perform a form of optimal control called model predictive control (MPC). We describe a method of control trajectory parameterization that enables MPC to be applied to systems with more DoF and with longer prediction horizons than previously possible. We show that this parameterization decreases MPC's sensitivity to model error and drastically reduces MPC solve times. A novel form of MPC is developed based on an evolutionary optimization algorithm that allows the optimization to be parallelized on a computer's graphics processing unit (GPU). We show that this evolutionary MPC (EMPC) can greatly decrease MPC solve times for high DoF systems without large performance losses, especially given a large GPU. We combine the ideas of machine learned DNN models of robot dynamics, with parameterized and parallelized MPC to obtain a nonlinear version of EMPC which can be run at higher rates and find better solutions than many state-of-the-art optimal control methods. Finally we demonstrate an adaptive form of MPC that can compensate for model error or changes in the system to be controlled. This adaptive form of MPC is shown to inherit MPC's robustness to completely unmodeled disturbances and adaptive control's ability to decrease trajectory tracking errors over time.

This study was undertaken to compare the MIC (minimum inhibitory concentration) and MPC (mutant prevention concentration) values for oxytetracycline and florfenicol against strains of Pasteurella multocida isolated from cattle and pigs, and for enrofloxacin against strains of Salmonella Typhimurium isolated from horses. Isolates of P. multocida from cattle and pigs, and S. Typhimurium from horses were obtained from specimens or isolates from contributing laboratories. All the equine isolates and 50% of the cattle and pig isolates were from clinically sick animals. All isolates were tested in duplicate with both the MIC and the MPC methods. The MIC method used was the standardized microdilution method performed in microtitre plates. The MPC method used was according to the method described by Blondeau. This method was modified, to make use of smaller plates and lower volumes of antimicrobials, but retaining a final bacterial concentration of 109 colony-forming units per ml. The antimicrobials were dissolved as described in the certificates of analyses. Enrofloxacin and oxytetracycline were dissolved in water, and florfenicol was dissolved in alcohol. For the MPC method, an additional control was added to one quadrant of a four-quadrant 90mm plate/petri dish. The antimicrobials were tested as individual antimicrobials and not as combinations. Both the MIC and MPC methods included ATCC (American Type Culture Collection) strains as control organisms and were evaluated according to the guidelines of the CLSI (Clinical and Laboratory Standards Institute). The MIC50 values for enrofloxacin against Salmonella Typhimurium isolates from horses was 0.25 ìg/ml and the MPC50 values 0.5 ìg/ml. A comparative reference range was not available as enrofloxacin is not registered in South Africa for use in horses, and is used extra-labelly. The results for florfenicol against P. multocida yielded an MIC50 value of 0.5 ìg/ml and an MPC50 value of <2 ìg/ml. The close relationship of these two concentrations is an indication of the effectiveness of florfenicol when used against P. multocida. The PD/PK data with a value of 141.78 for AUC/MIC provided additional support for the efficacy of florfenicol against P. multocida. The PD/PK value of >125, is an effective parameter for treatment of Gram-negative bacteria. The corresponding results for oxytetracycline were above the MIC value but fell within the mutant selection window. The results point to the fact that the use of oxytetracycline against P. multocida may not be effective in preventing the appearance of first step mutant strains when used at current recommended dosages. The PK/PD data, using AUC/MIC, yielded a value of 56. Some of the isolates (55.17%) had an MPC value of 16 ìg/ml. Whereas the MIC method is used routinely in diagnostic laboratories, the MPC method can be employed to generate data that can be applied where antimicrobial treatment of certain bacteria is problematic and standard treatment may lead to the development of resistance. Data obtained from such studies will enable manufacturers of antimicrobial drugs to adapt antimicrobial therapy where practical and feasible to prevent the development of first step mutants. / Dissertation (MSc)--University of Pretoria, 2012. / Veterinary Tropical Diseases / unrestricted

With the ongoing trend in moving the upper levels of the automation hierarchy to the cloud, there has been investigation into supplying industrial automation as a cloud based service. There are many practical considerations which pose limitations on the feasibility of the idea. This research investigates some of the requirements which would be needed to implement a platform which would facilitate competition between different controllers which would compete to control a process in real-time. This work considers only the issues relating to implementation of the philosophy from a control theoretic perspective, issues relating to hardware/communications infrastructure and cyber security are beyond the scope of this work. A platform is formulated and all the relevant control requirements of the system are discussed. It is found that in order for such a platform to determine the behaviour of a controller, it would need to simulate the controller on a model of the process over an extended period of time. This would require a measure of the disturbance to be available, or at least an estimate thereof. This therefore increases the complexity of the platform. The practicality of implementing such a platform is discussed in terms of system identification and model/controller maintenance. A model of the surge tank from SibanyeStillwater’s Platinum bulk tailings treatment (BTT) plant, the aim of which is to keep the density of the tank outflow constant while maintaining a steady tank level, was derived, linearised and an input-output controllability analysis performed on the model. Six controllers were developed for the process, including four conventional feedback controllers (decentralised PI, inverse, modified inverse and H¥) and two Model Predictive Controllers (MPC) (one linear and another nonlinear). It was shown that both the inverse based and H¥ controllers fail to control the tank level to set-point in the event of an unmeasured disturbance. The competing concept was successfully illustrated on this process with the linear MPC controller being the most often selected controller, and the overall performance of the plant substantially improved by having access to more advanced control techniques, which is facilitated by the proposed platform. A first appendix presents an investigation into a previously proposed switching philosophy [15] in terms of its ability to determine the best controller, as well as the stability of the switching scheme. It is found that this philosophy cannot provide an accurate measure of controller performance owing to the use of one step ahead predictions to analyse controller behaviour. Owing to this, the philosophy can select an unstable controller when there is a stable, well tuned controller competing to control the process. A second appendix shows that there are cases where overall system performance can be improved through the use of the proposed platform. In the presence of constraints on the rate of change of the inputs, a more aggressive controller is shown to be selected so long as the disturbance or reference changes do not cause the controller to violate these input constraints. This means that switching back to a less aggressive controller is necessary in the event that the controller attempts to violate these constraints. This is demonstrated on a simple first order plant as well as the surge tank process. Overall it is concluded that, while there are practical issues surrounding plant and system identification and model/controller maintenance, it would be possible to implement such a platform which would allow a given plant access to advanced process control solutions without the need for procuring the services of a large vendor. / Dissertation (MEng)--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Privacy is a crucial aspect of any system involving user-supplied data. There exist multiple approaches to protecting the identity and secrecy of users in data submission systems. In this thesis, I consider the case of privacy-enhancing of data reporting in Participatory Sensing systems. I conducted an extensive literature overview to explore privacy-oriented enhancements to data submission that are applicable in the PS systems. I designed a protocol for proximity-based data aggregation that utilizes Multi-party Secure Computations over Bluetooth Low Energy. Users are divided into groups that perform sub-aggregations and report results to central entities, protecting themselves from honest-but-curious adversary threats. I present a mobile app and web servers for central entities that follow the design of the protocol. I evaluated the achieved effectiveness and discuss the utility and privacy trade-offs. The implementation performs typically for an MPC system with high communication overhead, and is implemented over Bluetooth, with the additional time needed for discovering and connecting devices. The overall performance of the system suggests that deployments targeting 1-second intervals of data submission are feasible. Main use cases are sensitive measurements, such as medical data or highly private user information. / Sekretess är en avgörande aspekt av alla system som involverar data som tillhandahålls av användare. Det finns flera tillvägagångssätt för att skydda användarnas identitet och sekretess i datainlämningssystem. I den här avhandlingen behandlar jag fallet med integritetsförbättrande datarapportering i Participatory Sensing-system. Jag genomförde en omfattande litteraturöversikt för att utforska integritetsorienterade förbättringar av datainlämning som är tillämpliga i PS-systemen. Jag designade ett protokoll för närhetsbaserad dataaggregering som använder flerpartssäkra beräkningar över Bluetooth Low Energy. Användare är indelade i grupper som utför sub-aggregeringar och rapporterar resultat till centrala enheter, och skyddar sig själva från ärliga men nyfikna motståndarhot. Jag presenterar en mobilapp och webbservrar för centrala enheter som följer protokollets design. Jag utvärderade den uppnådda effektiviteten och diskuterade nytta och sekretessavvägningar. Implementeringen fungerar som man kan förvänta sig för ett MPC-system med höga kommunikationskostnader, och implementeras över Bluetooth, med den extra tid som krävs för att upptäcka och ansluta enheter. Systemets övergripande prestanda tyder på att implementeringar som är inriktade på 1-sekunds intervaller för datainlämning är genomförbara. Huvudsakliga användningsfall är känsliga mätningar, såsom medicinska data eller mycket privat användarinformation.

Ensuring constraint satisfaction in large-scale systems with hard constraints is vital in many safety critical systems. The challenge is to design controllers that are efficiently synthesized offline, easily implementable online, and provide formal correctness guarantees. We take a divide and conquer approach to design controllers for reachability and infinite-time/finite-time constraint satisfaction control problems given large-scale interconnected linear systems with polyhedral constraints on states, controls, and disturbances. Such systems are made of small subsystems with coupled dynamics. Our goals are to design controllers that are i) fully compositional and ii) decentralized, such that online implementation requires only local state information. We treat the couplings among the subsystems as additional disturbances and use assume-guarantee (AG) contracts to characterize these disturbance sets. For each subsystem, we design and implement a robust controller locally, subject to its own constraints and contracts. Our main contribution is a method to derive the contracts via a novel parameterization, and a corresponding potential function that characterizes the distance to the correct composition of controllers and contracts, where all contracts are held. We show that the potential function is convex in the contract parameters. This enables the subsystems to negotiate the contracts with the gradient information from the dual of their local synthesis optimization problems in a distributed way, facilitating compositional control synthesis that scales to large systems. We then incorporate Signal Temporal Logic (STL) specifications into our formulation. We develop a decentralized control method for a network of perturbed linear systems with dynamical couplings subject to STL specifications. We first transform the STL requirements into set containment problems, then we develop controllers to solve these problems. The set containment requirements and parameterized contracts are added to the subsystems’ constraints. We introduce a centralized optimization problem to derive the contracts, reachability tubes, and decentralized closed-loop control laws. We show that, when the STL formula is separable with respect to the subsystems, the centralized optimization problem can be solved in a distributed way, which scales to large systems. We present formal theoretical guarantees on robustness of STL satisfaction. We present numerical examples, including scalability studies on systems with tens of thousands of dimensions, and case studies on applying our method to a distributed Model Predictive Control (MPC) problem in a power system. / 2024-01-16T00:00:00Z

This thesis considers the problem of control of nonlinear systems subject to limited availability of measurements and uncertainty in model parameters. To address this problem, first a linear offset free MPC is designed. Subsequently, a Lyapunov-based offset free MPC design is presented to handle structured uncertainty subject to constant disturbances. The controller's ability to handle unstructured uncertainty and measurement noise is demonstrated through simulation examples. Next, the problem of handling lack of state measurements as well as uncertainty is considered. To achieve simultaneous state and disturbance parameter estimation, a Lyapunov-based model predictive controller (MPC) is integrated with a moving horizon based mechanism, to achieve (where possible) offset elimination in the unmeasured states as well. A chemical reaction process example is presented to illustrate the key points. Finally its efficacy is demonstrated through a polymerization process example. / Thesis / Doctor of Philosophy (PhD)

Cryogenic air separation units (ASUs) are the most widely used technology for industrialscale production of large amounts of high-purity air components. These are highly energyintensive processes, which have motivated the development of demand response strategies to adapt their operation in response to the increased volatility of the energy market. The startup of ASUs warrants particular consideration within this context. ASUs are tightly integrated, thermally and materially, and have slow dynamics. These result in startup times on the order of hours to a day, during which electricity is consumed with limited revenue generation. In the current environment of electricity price deregulation, it may be economically advantageous for ASUs to shut down during periods of high electricity pricing, increasing the occurrences of startups. This presents a promising research opportunity, especially because ASU startup has received relatively little attention in the literature. This thesis investigates the optimal startup of ASUs using dynamic optimization. First, this thesis focuses on startup model development for the multiproduct ASU. Startup model development requires accounting for discontinuities present at startup. Four main discontinuities are considered: stage liquid flow discontinuity, stage vapor flow discontinuities, flow liquid out of sumps and reboilers, and opening and closing valves. Other types of discontinuities accounted for include the change in the number of phases of streams. These discontinuities are approximated with smoothing formulations, using mostly hyperbolic tangent functions, to allow application of gradient-based optimization. The modeling approach was assessed through three case studies: dynamic simulation of a successful startup, dynamic simulation of a failed startup, and dynamic optimization using a least-squares minimization formulation. Following startup model development, this thesis investigates the development of a framework for optimizing ASU startups using readily interpretable metrics of time and economics. For economics, cumulative profit over the startup horizon is considered. Two events are tracked for the definition of time metrics: time taken to obtain product purities and time to obtain steady-state product flows. Novel approaches are proposed for quantifying these time metrics, which are used as objective functions and in formulating constraints. The / Thesis / Doctor of Philosophy (PhD)