Minimization of Sensor Activation in Discrete-Event Systems with Control Delays and Observation Delays

In discrete-event systems, to save sensor resources, the agent continuously adjusts sensor activation decisions according to a sensor activation policy based on the changing observations. However, new challenges arise for sensor activations in networked discrete-event systems, where observation delays and control delays exist between the sensor systems and the agent. In this paper, a new framework for activating sensors in networked discrete-event systems is established. In this framework, we construct a communication automaton that explicitly expresses the interaction process between the agent and the sensor systems over the observation channel and the control channel. Based on the communication automaton, we can define dynamic observations of a communicated string. To guarantee that a sensor activation policy is physically implementable and insensitive to random control delays and observation delays, we further introduce the definition of delay feasibility. We show that a delay feasible sensor activation policy can be used to dynamically activate sensors even if control delays and observation delays exist. A set of algorithms are developed to minimize sensor activations in a transition-based domain while ensuring a given specification condition is satisfied. A practical example is provided to show the application of the developed sensor activation methods. Finally, we briefly discuss how to extend the proposed framework to a decentralized sensing architecture

Property Enforcement for Partially-Observed Discrete-Event Systems

Engineering systems that involve physical elements, such as automobiles, aircraft, or electric power pants, that are controlled by a computational infrastructure that consists of several computers that communicate through a communication network, are called Cyber-Physical Systems. Ever-increasing demands for safety, security, performance, and certi cation of these critical systems put stringent constraints on their design and necessitate the use of formal model-based approaches to synthesize provably-correct feedback controllers. This dissertation aims to tackle these challenges by developing a novel methodology for synthesis of control and sensing strategies for Discrete Event Systems (DES), an important class of cyber-physical systems. First, we develop a uniform approach for synthesizing property enforcing supervisors for a wide class of properties called information-state-based (IS-based) properties. We then consider the enforcement of non-blockingness in addition to IS-based properties. We develop a nite structure called the All Enforcement Structure (AES) that embeds all valid supervisors. Furthermore, we propose novel and general approaches to solve the sensor activation problem for partially-observed DES. We extend our results for the sensor activation problem from the centralized case to the decentralized case. The methodology in the dissertation has the following novel features: (i) it explicitly considers and handles imperfect state information, due to sensor noise, and limited controllability, due to unexpected environmental disturbances; (ii) it is a uniform information-state-based approach that can be applied to a variety of user-speci ed requirements; (iii) it is a formal model-based approach, which results in provably correct solutions; and (iv) the methodology and associated theoretical foundations developed are generic and applicable to many types of networked cyber-physical systems with safety-critical requirements, in particular networked systems such as aircraft electric power systems and intelligent transportation systems.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137097/1/xiangyin_1.pd

Recent advances in intelligent-based structural health monitoring of civil structures

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner

Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata

In the last several decades, the model-based diagnosis of discrete-event systems (DESs) has increasingly become an active research topic in both control engineering and artificial intelligence. However, in contrast with the widely applied minimal diagnosis of static systems, in most approaches to the diagnosis of DESs, all possible candidate diagnoses are computed, including nonminimal candidates, which may cause intractable complexity when the number of nonminimal diagnoses is very large. According to the principle of parsimony and the principle of joint-probability distribution, generally, the minimal diagnosis of DESs is preferable to a nonminimal diagnosis. To generate more likely diagnoses, the notion of the minimal diagnosis of DESs is presented, which is supported by a minimal diagnoser for the generation of minimal diagnoses. Moreover, to either strongly or weakly decide whether a minimal set of faulty events has definitely occurred or not, two notions of minimal diagnosability are proposed. Necessary and sufficient conditions for determining the minimal diagnosability of DESs are proven. The relationships between the two types of minimal diagnosability and the classical diagnosability are analysed in depth

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Sensor selection for fine-grained behavior verification that respects privacy

A useful capability is that of classifying some agent's behavior using data from a sequence, or trace, of sensor measurements. The sensor selection problem involves choosing a subset of available sensors to ensure that, when generated, observation traces will contain enough information to determine whether the agent's activities match some pattern. In generalizing prior work, this paper studies a formulation in which multiple behavioral itineraries may be supplied, with sensors selected to distinguish between behaviors. This allows one to pose fine grained questions, e.g., to position the agent's activity on a spectrum. In addition, with multiple itineraries, one can also ask about choices of sensors where some behavior is always plausibly concealed by (or mistaken for, or conflated with) another. Using sensor ambiguity to limit the acquisition of knowledge is a strong privacy guarantee, and one which some earlier work has examined. By concretely formulating privacy requirements for sensor selection, this paper connects both lines of work: privacy -- where there is a bound from above, and behavior verification -- where sensors are bounded from below. We examine the worst case computational complexity that results from both types of bounds, proving that upper bounds are more challenging under standard computational complexity assumptions. The problem is intractable in general, but we give a novel approach to solving this problem that can exploit interrelationships between constraints, and we see opportunities for a few optimizations. Case studies are presented to demonstrate the usefulness and scalability of our proposed solution, and to assess the impact of the optimizations

Malliprediktiivinen säädin Tennessee Eastman prosessille

This thesis aims to design a multivariable Model Predictive Control (MPC) scheme for a complex industrial process. The focus of the thesis is on the implementation and testing of a linear MPC control strategy combined with fault detection and diagnosis methods. The studied control methodology is based on a linear time invariant state-space model and the quadratic programming optimization procedure. The control scheme is realized as a supervisory one, where the MPC is used to calculate the optimal set point trajectories for the lower level PI controllers, thus aiming to decrease the fluctuations in the end product flows. The Tennessee Eastman (TE) process is used as the testing environment. The TE process is a benchmark based on a real process modified for testing. It has five units, four reactants, an inert, two products and a byproduct. The control objective is to maintain the production rate and the product quality at the desired level. To achieve this, the MPC implemented in this thesis gives setpoints to three stabilizing PI control loops around the reactor and the product stripper. The performance of the designed control systems is evaluated by inducing process disturbances, setpoint changes, and faults for two operational regimes. The obtained results show the efficiency of the adopted approach in handling disturbances and flexibility in control of different operational regimes without the need of retuning. To suppress the effects caused by faults, an additional level that provides fault detection and controller reconfiguration should be developed as further research.Tämän diplomityön tavoite on suunnitella monimuuttujainen-malliprediktiivinen säädin (MPC) teolliselle prosessille. Diplomityö keskittyy toteuttamaan ja testaamaan lineaarisen MPC strategian, joka yhdistettynä vikojen havainnointiin ja tunnistukseen sekä uudelleen konfigurointiin voidaan laajentaa vikasietoiseksi. Tutkittu säätöstrategia perustuu lineaariseen ajan suhteen muuttumattomaan tilataso-malliin ja neliöllisen ohjelmoinnin optimointimenetelmään. Säätö on toteutettu nk. ylemmän tason järjestelmänä, eli MPC:tä käytetään laskemaan optimaaliset asetusarvot alemman säätötason PI säätimille, tavoitteena vähentää vaihtelua lopputuotteen virroissa. Tennessee Eastman (TE) prosessia käytetään testiympäristönä. TE on testiprosessi, joka perustuu todelliseen teollisuuden prosessiin ja jota on muokattu testauskäyttöön sopivaksi. Prosessissa on viisi yksikköä, neljä lähtöainetta, inertti, kaksi tuotetta ja yksi sivutuote. Säätötavoite on ylläpitää haluttu taso tuotannon määrässä ja laadussa. Tämän saavuttamiseksi tässä diplomityössä toteutettu MPC antaa asetusarvoja kolmelle stabiloivalle PI-säätimelle reaktorin ja stripperin hallinnassa. Säätösysteemin suorituskykyä arvioitiin aiheuttamalla prosessiin häiriöitä, asetusarvon muutoksia ja vikoja eri operatiivisissa olosuhteissa. Saavutetut tulokset osoittavat valitun menetelmän tehokkuuden häiriöiden käsittelyyn ja joustavaan säätöön eri olosuhteissa. Tutkimuksen jatkokehityksenä vikojen vaikutuksen vaimentamiseksi säätöön tulisi lisätä taso, joka havaitsee viat ja uudelleen konfiguroi säätimen sen mukaisesti