Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults

Linear Regression Models Applied to Imperfect Information Spacecraft Pursuit-evasion Differential Games

Within satellite rendezvous and proximity operations lies pursuit-evasion differential games between two spacecraft. The extent of possible outcomes can be mathematically bounded by differential games where each player employs optimal strategies. A linear regression model is developed from a large data set of optimal control solutions. The model is shown to map pursuer relative starting positions to final capture positions and estimate capture time. The model is 3.8 times faster than the indirect heuristic method for arbitrary pursuer starting positions on an initial relative orbit about the evader. The linear regression model is shown to be well suited for on-board implementation for autonomous mission planning

Bioprocess Monitoring and Control

Process monitoring and control are fundamental to all processes; this holds especially for bioprocesses, due to their complex nature. Usually, bioprocesses deal with living cells, which have their own regulatory systems. It helps to adjust the cell to its environmental condition. This must not be the optimal condition that the cell needs to produce whatever is desired. Therefore, a close monitoring of the cell and its environment is essential to provide optimal conditions for production. Without measurement, no information of the current process state is obtained. In this book, methods and techniques are provided for the monitoring and control of bioprocesses. From new developments for sensors, the application of spectroscopy and modelling approaches, the estimation and observer implementation for ethanol production and the development and scale-up of various bioprocesses and their closed loop control information are presented. The processes discussed here are very diverse. The major applications are cultivation processes, where microorganisms were grown, but also an incubation process of bird’s eggs, as well as an indoor climate control for humans, will be discussed. Altogether, in 12 chapters, nine original research papers and three reviews are presented

Precision Control of a Sensorless Brushless Direct Current Motor System

Sensorless control strategies were first suggested well over a decade ago with the aim of reducing the size, weight and unit cost of electrically actuated servo systems. The resulting algorithms have been successfully applied to the induction and synchronous motor families in applications where control of armature speeds above approximately one hundred revolutions per minute is desired. However, sensorless position control remains problematic. This thesis provides an in depth investigation into sensorless motor control strategies for high precision motion control applications. Specifically, methods of achieving control of position and very low speed thresholds are investigated. The developed grey box identification techniques are shown to perform better than their traditional white or black box counterparts. Further, fuzzy model based sliding mode control is implemented and results demonstrate its improved robustness to certain classes of disturbance. Attempts to reject uncertainty within the developed models using the sliding mode are discussed. Novel controllers, which enhance the performance of the sliding mode are presented. Finally, algorithms that achieve control without a primary feedback sensor are successfully demonstrated. Sensorless position control is achieved with resolutions equivalent to those of existing stepper motor technology. The successful control of armature speeds below sixty revolutions per minute is achieved and problems typically associated with motor starting are circumvented.Research Instruments Ltd

Practical on-line model validation for model predictive controllers (MPC)

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2010.A typical petro-chemical or oil-refining plant is known to operate with hundreds if not thousands of control loops. All critical loops are primarily required to operate at their respective optimal levels in order for the plant to run efficiently. With such a large number of vital loops, it is difficult for engineers to monitor and maintain these loops with the intention that they are operating under optimum conditions at all times. Parts of processes are interactive, more so nowadays with increasing integration, requiring the use of a more advanced protocol of control systems. The most widely applied advanced process control system is the Model Predictive Controller (MPC). The success of these controllers is noted in the large number of applications worldwide. These controllers rely on a process model in order to predict future plant responses. Naturally, the performance of model-based controllers is intimately linked to the quality of the process models. Industrial project experience has shown that the most difficult and time-consuming work in an MPC project is modeling and identification. With time, the performance of these controllers degrades due to changes in feed, working regime as well as plant configuration. One of the causes of controller degradation is this degradation of process models. If a discrepancy between the controller’s plant model and the plant itself exists, controller performance may be adversely affected. It is important to detect these changes and re-identify the plant model to maintain control performance over time. In order to avoid the time-consuming process of complete model identification, a model validation tool is developed which provides a model quality indication based on real-time plant data. The focus has been on developing a method that is simple to implement but still robust. The techniques and algorithms presented are developed as far as possible to resemble an on-line software environment and are capable of running parallel to the process in real time. These techniques are based on parametric (regression) and nonparametric (correlation) analyses which complement each other in identifying problems -iiwithin on-line models. These methods pinpoint the precise location of a mismatch. This implies that only a few inputs have to be perturbed in the re-identification process and only the degraded portion of the model is to be updated. This work is carried out for the benefit of SASOL, exclusively focused on the Secunda plant which has a large number of model predictive controllers that are required to be maintained for optimal economic benefit. The efficacy of the methodology developed is illustrated in several simulation studies with the key intention to mirror occurrences present in industrial processes. The methods were also tested on an industrial application. The key results and shortfalls of the methodology are documented

Proceedings. 26. Workshop Computational Intelligence, Dortmund, 24. - 25. November 2016

Dieser Tagungsband enthält die Beiträge des 26. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen

Testing financial and real market operations in restructured electricity systems: four theoretical and empirical studies

To facilitate the U.S. wholesale electric power restructuring process and promote competitive market outcomes, in April 2003 the U.S. Federal Energy Regulatory Commission (FERC) proposed a complicated market design called the Wholesale Power Market Platform (WPMP) for common adoption by all U.S. wholesale power markets. Despite the fact that versions of the WPMP have been widely implemented in many states, strong opposition to the WPMP persists among some industry stakeholders due largely to a perceived lack of adequate performance testing. In this dissertation, I apply analytical, statistical and agent-based computational simulation tools to analyze and test financial and real power market operations under the current WPMP design. The overall dissertation objective is to better understand how and to what extent the WPMP design facilitates to produce orderly, fair and efficient market outcomes. Four related studies have been undertaken to address four different issues at four different levels. Specifically, my first paper is a theoretical study of financial transmission right (FTR) markets. My second paper is an empirical study on the Midwest FTR market using statistical estimation tools. My third paper is an agent-based computational wholesale power market simulation study for systematic market design tests and market structure analyses. And my fourth paper is an optimization study in which I develop a Java-based DC OPF solver

An Experimental and Numerical Study on the Heat Transfer Driven Dynamics and Control of Transient Variations in a Solar Reactor

University of Minnesota M.S.M.E. thesis.July 2019. Major: Mechanical Engineering. Advisor: Nesrin Ozalp. 1 computer file (PDF); xiv, 156 pages.There is a major challenge in utilization of concentrating solar power for stable energy conversion processes due to fluctuating nature of available irradiation. This challenge can be handled by development of a robust control system that is capable of absorbing fluctuations in the sun’s irradiance without significantly changing the flow dynamics. In this thesis, a heat transfer driven model predictive control system is developed for advanced control of changes in solar flux by implementing a heat exchanger coupled variable aperture mechanism to capture spilled radiation. Experimental testing of the system was performed using a new 10 kWe xenon arc high flux solar simulator which was fully characterized in this thesis. Experimental results showed that the aperture mechanism can maintain the temperature of the reactor within ± 5 °C under severe radiation fluctuations during a cloudy day. Therefore, the aperture mechanism is a promising alternative to current traditional temperature control methods