Discrete event systems (DESs) are systems which involve quantities that take a discrete set of values, called states, and which evolve according to the occurrence of certain discrete qualitative changes, called events. Examples of DESs include many man-made systems such as computer and communication networks, robotics and manufacturing systems, computer programs, and automated trac systems. Supervisory control and failure diagnosis are two important problems in the study of DESs. This dissertation presents a temporal logic approach to the control and failure diagnosis of DESs. For the control of DESs, full branching time temporal logic-CTL* is used to express control specifications. Control problem of DES in the temporal logic setting is formulated; and the controllability of DES is defined. By encoding the system with a CTL formula, the control problem of CTL* is reduced to the decision problem of CTL*. It is further shown that the control problem of CTL* (resp., CTL{computation tree logic) is complete for deterministic double (resp., single) exponential time. A sound and complete supervisor synthesis algorithm for the control of CTL* is provided. Special cases of the control of computation tree logic (CTL) and linear-time temporal logic (LTL) are also studied; and for which algorithms of better complexity are provided. For the failure diagnosis of DESs, LTL is used to express fault specifications. Failure diagnosis problem of DES in the temporal logic setting is formulated; and the diagnosability of DES is defined. The problem of testing the diagnosability is reduced to that of model checking. An algorithm for the test of diagnosability and the synthesis of a diagnoser is obtained. The algorithm has a polynomial complexity in the number of system states and the number of fault specifications. For the diagnosis of repeated failures in DESs, different notions of repeated failure diagnosability, K-diagnosability, [1,K]-diagnosability, and [1,1]-diagnosability, are introduced. Polynomial algorithms for checking these various notions of repeated failure diagnosability are given, and a procedure of polynomial complexity for the on-line diagnosis of repeated failures is also presented.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 132-142) Also available in print.

In this thesis, different Machine learning (ML) algorithms were used in the detection of anomalies using a dataset from a Gas pipeline SCADA system which was generated by Mississippi State University’s SCADA laboratory. This work was divided into two folds: Binary Classification and Categorized classification. In the binary classification, two attack types namely: Command injection and Response injection attacks were considered. Eight Machine Learning Classifiers were used and the results were compared. The Light GBM and Decision tree classifiers performed better than the other algorithms. In the categorical classification task, Seven (7) attack types in the dataset were analyzed using six different ML classifiers. The light gradient-boosting machine (LGBM) outperformed all the other classifiers in the detection of all the attack types. One other aspect of the categorized classification was the use of an autoencoder in improving the performance of all the classifiers used. The last part of this thesis was using SHAP plots to explain the features that accounted for each attack type in the dataset.

Robots and other automated systems have potential use in many different fields. As the scope of robot applications that robots are used for increases, there is a growing desire to have human operators manage multiple robots. Typical methods of enabling operators to multi-task in this way involve some combination of user interfaces that support human cognition and advanced robot autonomy. Our research explores a complementary method of managing multiple robots by utilizing operator teams. The evidence suggests that for appropriate task scenarios, two cooperating operators can be more than twice as effective as one operator working alone.

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This thesis considers probabilistic supervisory control of probabilistic discrete event systems (PDES). PDES are modeled as generators of probabilistic languages. The probabilistic supervisors employed are a generalization of the deterministic ones previously employed in the literature. At any state, the supervisor enables/disables events with certain probabilities. The probabilistic supervisory control problem (PSCP) that has previously been considered in the literature is revisited: find, if possible, a supervisor under whose control the behavior of a plant is identical to a given probabilistic specification. The existing results are unified, complemented with a solution of a special case and the computational analysis of synthesis problem and the solution. The central place in the thesis is given to the solution of the optimal probabilistic supervisory control problem (OPSCP) in the framework: if the conditions for the existence of probabilistic supervisor for PSCP problem are not satisfied, find a probabilistic supervisor such that the achievable behaviour is as close as possible to the desired behaviour. The proximity is measured using the concept of pseudometric on states of generators. The distance between two systems is defined as the distance in the pseudometric between the initial states of the corresponding generators. The pseudometric is adopted from the research in formal methods community and is defined as the greatest fixed point of a monotone function. Starting from this definition, we suggest two algorithms for finding the distances in the pseudometric. Further, we give a logical characterization of the same pseudometric such that the distance between two systems is measured by a formula that distinguishes between the systems the most. A trace characterization of the pseudometric is then derived from the logical characterization by which the pseudometric measures the difference of (appropriately discounted) probabilities of traces and sets of traces generated by systems, as well as some more complicated properties of traces. Then, the solution to the optimal probabilistic supervisory control problem is presented. Further, the solution of the problem of approximation of a given probabilistic generator with another generator of a prespecified structure is suggested such that the new model is as close as possible to the original one in the pseudometric (probabilistic model fitting). The significance of the approximation is then discussed. While other applications are briefly discussed, a special attention is given to the use of ideas of probabilistic model fitting in the solution of a modified optimal probabilistic supervisory control problem. / Thesis / Doctor of Philosophy (PhD)

In the operation of unmanned vehicles, safety is a primary concern. This thesis focuses on the use of computer vision in the development of a situational awareness system that allows for safe deployment and operation of a ground robot from an unmanned aerial vehicle (UAV). A method for detecting utility cables in 3D range images is presented. This technique finds areas of an image that represent edges in 3D space, and uses the Hough transform to find those edges that take the shape of lines, indicating potential utility cables. A mission plan for stereo image capture is laid out as well for overcoming some weaknesses of the stereo vision system; this helps ensure that all utility cables in a scene are detected. In addition, the system partitions the point cloud into best-fit planes and uses these planes to locate areas of the scene that are traversable by a ground robot. Each plane's slope is tested against an acceptable value for negotiation by the robot, and the drop-off between the plane and its neighbors is examined as well. With the results of this analysis, the system locates the largest traversable region of the terrain using concepts from graph theory. The system displays this region to the human operator with the drop-offs between planes clearly indicated. The position of the robot is also simulated in this system, and real-time feedback regarding dangerous moves is issued to the operator. After a ground robot is deployed to the chosen site, the system must be capable of tracking it in real time as well. To this end, a software routine that uses ARToolkit's marker tracking capabilities is developed. This application computes the distance to the robot, as well as the horizontal distance from camera to the robot; this allows the flight controller to issue the proper commands to keep the robot centered underneath the UAV. / Master of Science

The automotive industry is shifting towards the development of electric vehicles (EVs) at a rapid pace with most manufacturers intending to be producing solely electric vehicles by 2050. This is due in large part to government regulations aiming to reduce emissions from the transportation sector. As a result, significant research is being conducted to advance the development of EV propulsion technology. This research explores two aspects of EV design: the analysis and selection of an efficient EV propulsion architecture, and the implementation and testing of an efficiency-based energy management strategy (EMS) for implementation on Mississippi State’s EcoCAR team vehicle for the EcoCAR EV Challenge (2023 Cadillac LYRIQ). The proposed EMS serves as a first step in the development of a complex and robust propulsion supervisory controller with the objective of minimizing energy consumption, maximizing vehicle acceleration performance and minimizing jerk, to deliver a pleasant driving experience

In order to operate UAVs under human supervisory control in more complex arenas such as urban environments, an obstacle detection system is a requirement to achieve safe navigation. The development of a system capable of meeting these requirements is presented. The first stage of development was sensor selection and initial testing. After this, the sensor was combined with a servomotor to allow it to rotate and provide obstacle detection coverage in front, below, and to both sides of the UAV. Utilizing a PC-104 single board computer running LabView Real-time for on-board control of the sensor and servomotor, a stand alone obstacle detection system was developed meeting the requirements of light weight, low power, and small size. The detection performance of the system for several parameters has been fully characterized. A human subjects study was conducted to assess the any advantages resulting from the addition of the obstacle detection system compared to that of a normal nadir camera. The study demonstrated that users with access to the three-dimensional display were able to navigate an obstacle course with greater success than those with only a camera. Additional development into more advanced visualization of the environment has potential to increase effectiveness of this obstacle detection system. / Master of Science

<p>Sampled-data supervisory control deals with timed discrete event systems (TDES) where the supervisors are to be implemented as sampled-data controllers. A sampled-data controller views the system as a series of inputs and outputs and is controlled by a periodic clock. It samples its inputs, changes state, and updates its outputs on each clock edge (the tick event). The sampled-data supervisory control framework provides a set of conditions that the TDES system must satisfy to ensure its correct behaviour in order to be implemented as sampled data controllers. A serious limitation for automatic verification of systems is the size of the system's synchronous product. To overcome this limitation, we propose the use of a compositional approach to the verification of sampled-data supervisory control. In this approach, first we recast the required conditions for sampled-data supervisory control in terms of other properties such as language inclusion, nonblocking or controllability, which already have existing compositional methods and algorithms. This makes the sampled-data properties suitable for compositional verification, considerably increasing the size of systems that can be handled using sampled-data supervisory control. We also develop and implement a set of algorithms for the compositional verification of these sampled-data properties. We provide an example of the SD Controlled Flexible Manufacturing System to test our algorithms.</p> / Master of Science (MSc)