Identification and Adaptive Control Methods for Some Stochastic Systems

This dissertation is focused on the identification and adaptive control of some stochastic systems. Initially a survey of some adaptive control problems for both discrete and continuous time stochastic systems is provided. Discrete time branching processes are described and some results on parameter estimation and adaptive control for these processes are reviewed. Then continuous time branching processes are introduced and the main results in this dissertation concerning estimation and adaptive control are given. The family of estimators is shown to be strongly consistent and the optimal rate of convergence of this family of estimators is obtained. Furthermore some other asymptotic properties of these estimators are verified. An adaptive control is given that posses self-tuning property. It is shown that it does not achieve the optimal asymptotic cost for the known system. Finally some computational methods and simulations are given for a variety of stochastic differential equations driven by a Brownian motion or an arbitrary fractional Brownian motion and computational properties of the parameter estimates for the branching processes are given

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Machine Learning assisted Digital Twin for event identification in electrical power system

The challenges of stable operation in the electrical power system are increasing with the infrastructure shifting of the power grid from the centralized energy supply with fossil fuels towards sustainable energy generation. The predominantly RES plants, due to the non-linear electronic switch, have brought harmonic oscillations into the power grid. These changes lead to difficulties in stable operation, reduction of outages and management of variations in electric power systems. The emergence of the Digital Twin in the power system brings the opportunity to overcome these challenges. Digital Twin is a digital information model that accurately represents the state of every asset in a physical system. It can be used not only to monitor the operation states with actionable insights of physical components to drive optimized operation but also to generate abundant data by simulation according to the guidance on design limits of physical systems. The work addresses the topic of the origin of the Digital Twin concept and how it can be utilized in the optimization of power grid operation.Die Herausforderungen für den zuverfässigen Betrieb des elektrischen Energiesystems werden mit der Umwandlung der Infrastruktur in Stromnetz von der zentralen Energieversorgung mit fossilen Brennstoffen hin zu der regenerativen Energieeinspeisung stetig zugenommen. Der Ausbau der erneuerbaren Energien im Zuge der klimapolitischen Zielsetzung zur CO²-Reduzierung und des Ausstiegs aus der Kernenergie wird in Deutschland zügig vorangetrieben. Aufgrund der nichtlinearen elektronischen Schaltanlagen werden die aus EE-Anlagen hervorgegangenen Oberschwingungen in das Stromnetz eingebracht, was nicht nur die Komplexität des Stromnetzes erhöht, sondern auch die Stabilität des Systems beeinflusst. Diese Entwicklungen erschweren den stabilen Betrieb, die Verringerung der Ausfälle und das Management der Netzschwankungen im elektrischen Energiesystem. Das Auftauchen von Digital Twin bringt die Gelegenheit zur Behebung dieser Herausforderung. Digital Twin ist ein digitales Informationsmodell, das den Zustand des physikalischen genau abbildet. Es kann nicht nur zur Überwachung der Betriebszustände mit nachvollziehbarem Einsichten über physischen Komponenten sondern auch zur Generierung der Daten durch Simulationen unter der Berücksichtigung der Auslegungsgrenze verwendet werden. Diesbezüglich widmet sich die Arbeit zunächste der Fragestellung, woher das Digital Twin Konzept stammt und wie das Digitan Twin für die Optimierung des Stromnetzes eingesetzt wird. Hierfür werden die Perspektiven über die dynamische Zustandsschätzung, die Überwachung des des Betriebszustands, die Erkennung der Anomalien usw. im Stromnetz mit Digital Twin spezifiziert. Dementsprechend wird die Umsetzung dieser Applikationen auf dem Lebenszyklus-Management basiert. Im Rahmen des Lebenszyklusschemas von Digital Twin sind drei wesentliche Verfahren von der Modellierung des Digital Twins zur deren Applizierung erforderlich: Parametrierungsprozess für die Modellierung des Digital Twins, Datengenerierung mit Digital Twin Simulation und Anwendung mit Machine Learning Algorithmus für die Erkennung der Anomalie. Die Validierung der Zuverlässigkeit der Parametrierung für Digital Twin und der Eventserkennung erfolgt mittels numerischer Fallstudien. Dazu werden die Algorithmen für Online und Offline zur Parametrierung des Digital Twins untersucht. Im Rahmen dieser Arbeit wird das auf CIGRÉ basierende Referenznetz zur Abbildung des Digital Twin hinsichtlich der Referenzmessdaten parametriert. So sind neben der Synchronmaschine und Umrichter basierende Einspeisung sowie Erreger und Turbine auch regler von Umrichter für den Parametrierungsprozess berücksichtigt. Nach der Validierung des Digital Twins werden die zahlreichen Simulationen zur Datengenerierung durchgeführt. Jedes Event wird mittels der Daten vo Digital Twin mit einem "Fingerprint" erfasst. Das Training des Machine Learning Algorithmus wird dazu mit den simulierten Daten von Digital Twin abgewickelt. Das Erkennungsergebnis wird durch die Fallstudien validiert und bewertet

Advanced process control in manufacturing process with high dimensional measurements

Automatic feedback or feedforward control has been widely used in manufacturing systems to reduce process variability and ensure on-target product quality. In this thesis, the methodologies of automatic control are further investigated to address model uncertainties, high-dimensional sensing feedback control, and their applications in challenging engineering problems. Motivated by real needs from current industrial production systems, three control methods are studied in this thesis in Chapters 2, 3, and 4. In Chapter 2, an adaptive cautious regularized run-to-run control scheme is developed for overlay control in photolithography processes. Photolithography is the bottleneck for quality improvement in semiconductor manufacturing. The decreasing critical dimensions of the semiconductor product require more effective run-to-run control technology. Currently, Exponential Weighted Moving Average (EWMA) control scheme is widely used in the overlay control of lithography processes. In this chapter, three shortcomings of the current EWMA run-to-run control scheme are investigated: (i) the existing EWMA control scheme has its weight parameter λ set as a fixed value, which does not perform well when the process changes; (ii) the existing EWMA control scheme does not consider the model and parameter uncertainties in practice; and (iii) the adjustable range of the control variables is not considered in the existing EWMA control scheme. To address these limitations, we propose a new adaptive, cautious, and optimal run-to-run control scheme. The effectiveness of the new controller is validated through surrogated simulation studies. In Chapter 3, an image-based feedback control strategy is developed by using tensor representation and analysis. The problem is motivated by the photolithography process, where the system output is image signals measuring the overlay error, and the control inputs are tuning vectors. To develop a control strategy, one first needs to off-line estimate the process model by finding the relationship between the image output and vector inputs, and then to obtain the control law by online minimizing the control objective function. The main challenges in achieving such a control strategy include (i) the high dimensionality of the output in building a regression model, (ii) the spatial structure of image outputs and the temporal structure of the image sequence, and (iii) non-i.i.d noises. To address these challenges, we propose a novel tensor-based process control approach by incorporating the tensor time series and regression techniques. Based on the process model, we obtain the control law by minimizing a control objective function. Although our proposed approach is demonstrated with the 2D images as the system output, it can have the potential to be extended to the higher-order tensors such as video signals or point cloud data. Simulation and case studies show that our proposed method is more effective than benchmark methods in terms of relative mean square error. Chapter 4 will investigate how to achieve half-fuselage assembly via active control. In a half fuselage assembly process, shape control is vital for achieving ultra-high precision assembly. To achieve better shape adjustment, we need to determine the optimal location and force of each actuator to push and pull a fuselage to compensate for its initial shape distortion. The current practice achieves this goal by solving a surrogate model-based optimization problem. However, there are two limitations in this surrogate model-based method: (1) Low efficiency: Collecting training data for surrogate modeling from many FEA replications is time-consuming. (2) Non-optimality: The required number of FEA replications for building an accurate surrogate model will increase as the potential number of actuator locations increases. Therefore, the surrogate model can only be built on a limited number of prespecified potential actuator locations, which will lead to sub-optimal control results. To address these issues, this chapter proposes an FEA model-based automatic optimal shape control (AOSC) framework. This method directly loads the system equation from the FEA simulation platform to determine the optimal location and force of each actuator. Moreover, the proposed method further integrates the cautious control concept into the AOSC system to address model uncertainties in practice. The case study with industrial settings shows that the proposed Cautious AOSC method achieves higher control accuracy compared to current industrial practice.Ph.D

Regelungstheorie

The workshop “Regelungstheorie” (control theory) covered a broad variety of topics that were either concerned with fundamental mathematical aspects of control or with its strong impact in various fields of engineering

Self-aware and self-adaptive autoscaling for cloud based services

Modern Internet services are increasingly leveraging on cloud computing for flexible, elastic and on-demand provision. Typically, Quality of Service (QoS) of cloud-based services can be tuned using different underlying cloud configurations and resources, e.g., number of threads, CPU and memory etc., which are shared, leased and priced as utilities. This benefit is fundamentally grounded by autoscaling: an automatic and elastic process that adapts cloud configurations on-demand according to time-varying workloads. This thesis proposes a holistic cloud autoscaling framework to effectively and seamlessly address existing challenges related to different logical aspects of autoscaling, including architecting autoscaling system, modelling the QoS of cloudbased service, determining the granularity of control and deciding trade-off autoscaling decisions. The framework takes advantages of the principles of self-awareness and the related algorithms to adaptively handle the dynamics, uncertainties, QoS interference and trade-offs on objectives that are exhibited in the cloud. The major benefit is that, by leveraging the framework, cloud autoscaling can be effectively achieved without heavy human analysis and design time knowledge. Through conducting various experiments using RUBiS benchmark and realistic workload on real cloud setting, this thesis evaluates the effectiveness of the framework based on various quality indicators and compared with other state-of-the-art approaches

An introductory survey of probability density function control

YesProbability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.De Montfort University - DMU HEIF’18 project, Natural Science Foundation of Shanxi Province [grant number 201701D221112], National Natural Science Foundation of China [grant numbers 61503271 and 61603136