Home Publications
Publications
Cancel

Publications

Prof. Ratnesh Kumar’s research area is, “ESSeNCE: Embedded Software, Sensors, Networks, Cyberphysical, Energy”, spanning Sensors, Data Analytics, Networks, Controls, and Formal Methods for Correctness/Accuracy, Efficiency, Safety, Stability, Security, Resiliency, Diagnosability, and Prognosability, in Cyber-Physical (Hybrid) and Embedded and Real-time Systems, Model-based Software and Web-services, Agri-, Bio- and Envionmental Sensing, Wearables, and Power Grid and Energy Harvesting. For full list of publications, refer Ratnesh Kumar’s CV

Prof. Kumar has published extensively in the areas of ESSeNCE: 374 peer reviewed articles (139 Journal articles, 209 Conference articles, and 12 book-chapters, 1 Textbook, and 13 Patents—9 already awarded, and 5 already licensed by companies).


Sensors:

Agriculture Sensing

  1. Development and deployment of sensing technologies is one of the main steps in achieving sustainability in crop production through precision agriculture. Key sensing methodologies developed for monitoring soil moisture and nutrients with recent advances in the sensing devices reported in literature using those techniques are overviewed in this article. The soil moisture determination has been divided into four main sections describing soil moisture measurement metrics and laboratory-based testing, followed by in-situ, remote and proximal sensing techniques. The application, advantages and limitations for each of the mentioned technologies are discussed. The nutrient monitoring methods are reviewed beginning with laboratory-based methods, ion-selective membrane based sensors, bio-sensors, spectroscopy-based methods, and capillary electrophoresis-based systems for inorganic ion detection. Attention has been given to the core principle of detection while reporting recent sensors developed using the mentioned concepts. The latest works reported on the different sensing methodologies point towards the trend of developing low-cost, easy to use, field-deployable or portable sensing systems aimed towards improving technology adoption in crop production leading to efficient site-specific soil and crop management which in turn will bring us closer to reaching sustainability in the practice of agriculture.

  2. We present an effective yet simple approach to study the dynamic variations in optical properties (such as the refractive index (RI)) of graphene oxide (GO) when exposed to gases in the visible spectral region, using the thin-film interference method. The dynamic variations in the complex refractive index of GO in response to exposure to a gas is an important factor affecting the performance of GO-based gas sensors. In contrast to the conventional ellipsometry, this method alleviates the need of selecting a dispersion model from among a list of model choices, which is limiting if an applicable model is not known a priori. In addition, the method used is computationally simpler, and does not need to employ any functional approximations. Further advantage over ellipsometry is that no bulky optics is required, and as a result it can be easily integrated into the sensing system, thereby allowing the reliable, simple, and dynamic evaluation of the optical performance of any GO-based gas sensor. In addition, the derived values of the dynamically changing RI values of the GO layer obtained from the method we have employed are corroborated by comparing with the values obtained from ellipsometry.

  3. This paper reports on a microfluidic impedimetric nitrate sensor using a graphene oxide (GO) nanosheets and poly(3,4-ethylenedioxythiophene) nanofibers (PEDOT-NFs) enabled electrochemical sensing interface. The sensor has demonstrated the ability to accurately detect and quantify nitrate ions in real samples extracted from soil. The PEDOT NFs-GO composite serves as an effective matrix for immobilization of nitrate reductase enzyme molecules. The oxygenated functional groups available at GO allows an increased charge transfer resistance of the electrochemical electrode. Microscopic, spectroscopic, and electrochemical studies were systematically conducted to illustrate synergic interactions between the GO and PEDOT NFs. The sensor provides a sensitivity of 61.15Ω/(mg/L)/cm2 within a wide concentration range of 0.44–442mg/L for nitrate ions in agricultural soils. The detection limit of the sensor is 0.135mg/L with good specificity, reliability, and reproducibility.

  4. This paper reports on a microfluidic impedimetric nitrate sensor using a graphene oxide (GO) nanosheets and poly(3,4-ethylenedioxythiophene) nanofibers (PEDOT-NFs) enabled electrochemical sensing interface. The sensor has demonstrated the ability to accurately detect and quantify nitrate ions in real samples extracted from soil. The PEDOT NFs-GO composite serves as an effective matrix for immobilization of nitrate reductase enzyme molecules. The oxygenated functional groups available at GO allows an increased charge transfer resistance of the electrochemical electrode. Microscopic, spectroscopic, and electrochemical studies were systematically conducted to illustrate synergic interactions between the GO and PEDOT NFs. The sensor provides a sensitivity of 61.15Ω/(mg/L)/cm2 within a wide concentration range of 0.44–442mg/L for nitrate ions in agricultural soils. The detection limit of the sensor is 0.135mg/L with good specificity, reliability, and reproducibility.

Bio-Sensors

Energy:

Energy Harvesting

  1. Ambient triboelectric vibration energy harvesters used for self-powering IoT sensor nodes provide enhanced power output when interfaced with synchronous switched energy extraction circuits. However, these active interface circuits require overhead power for their switching actions and may suffer from cold-start issue, for example, in batteryless IoT system. This work develops a start-up circuit that initiates energy extraction via passive full-wave-rectification path and on accumulation of enough energy for control actions, autonomously toggles to an active synchronous switched optimal path, maximizing the energy extraction from the harvester. We integrate the here developed start-up circuit with our previously demonstrated multi-shot synchronous charge extraction (MS-SCE) circuit. The resultant self-starting end-to-end power management circuit is designed and simulated in TSMC 70V 0.18μm BCD process and its operation is validated in a triboelectric energy harvester powered batteryless IoT setting. The transition from passive to active optimal steady-state mode is achieved in 41.1 seconds and the start-up circuit’s overhead/parasitic-loss is only 3.43 μW or 6.7% of the harvested power in the steady-state.

  2. Triboelectric Nanogenerators (TENG) suitable for mechanical energy harvesting typically have ultra-high open-circuit voltage in several hundreds of volts, challenging the energy extraction circuit (EEC) design required for charging load battery/capacitor. Here, we present a novel multi-shot switched EEC that extracts energy in multiple discrete steps to regulate the TENG voltage below the breakdown limit of the technology (70 V in our case), making it suitable for Integrated Circuit (IC) implementation. The proposed strategy maintains high TENG voltage just below the breakdown limit to offer a high electrostatic retardation, enhancing the work done against it by the mechanical source in the form of transduced electrical energy. Mathematical derivation of the circuit’s output shows a constant transduction power at all load voltages, fully eliminating Maximum Power Point (MPP) Tracking and saving power for the same. The design and simulation of the proposed EEC in TSMC 0.18 μm BCD process achieve a maximum power conversion efficiency of 63.3% and a 1.91x gain over even an ideal conventional Full Wave Rectifier (FWR) circuit at its optimal MPP load (gain will be higher for a real FWR implementation).

  3. Triboelectric Nanogenerator (TENG) is a class of ambient mechanical energy harvesters used to augment the battery life of electronic devices such as sensors in implantables, wearables, and Internet of Things (IoT) applications. In this work, the fundamentals of pre-biasing (pre-charging) the TENG at the start of the operation cycle to enhance the per-cycle extracted energy is presented. The energy gain is mathematically formulated, and the optimum pre-biasing voltage (equivalently charge) is derived by analyzing the energy exchange between the mechanical and the electrical domain over a periodic cycle. Further, a novel Energy Extraction Circuit (EEC) termed as "Pre-biased Synchronous Charge Extraction (pSCE)" is introduced to 1) Realize synchronous pre-biasing of TENG using the load battery itself and 2) Achieve enhanced energy extraction from TENG. Energy output per-cycle is derived analytically for the pSCE circuit and compared to the state of the art Synchronous Charge Extraction (SCE) circuit. The experimental implementation is performed for the proposed pSCE circuit that shows a 6.65 fold gain over the Full Wave Rectifier (standard EEC) and 1.45 over the SCE circuit for a 5V battery load.

  4. Triboelectric Nanogenerators (TENGs) are suitable for harvesting ambient mechanical energy to increase the battery life of Internet of Things (IoT) devices. Energy Extraction Circuits (EECs) are required as an interface between TENG and the onboard battery load to rectify and improve the energy transfer efficiency. Here, for the first time, a novel ‘Self-propelled Pre-biased Synchronous Charge Extraction (spSCE)’ EEC is presented with theoretical analysis as well as experimental results. The proposed EEC offers a universal plug-and-play solution for any TENG operating under any ambient vibration, owing to its self-propelled switching feature. In addition, its inbuilt pre-biasing (pre-charging of TENG capacitor at the operation extremes) action enhances the net transduced energy from the mechanical source beyond the per-cycle energy limit for any non-pre-biasing EEC set by the existing Synchronous Charge Extraction (SCE) circuit. Accounting for the energy costs of spSCE actions, our experiments validated 119.7% (respectively, 163.7%) energy gain over the SCE circuit at a load of 5V (respectively, 15V).

Energy Systems

  1. This article presents a distributed predictive design for real-time voltage control under changing load and generation profiles at discrete intervals. The existing control designs include centralized approach, providing an optimal solution but less scalable and susceptible to single-point failures/attacks, as well as decentralized or localized approach, having increased scalability and attack resilience but lacking optimality. The proposed distributed solution offers the attractive features of both approaches, where the neighboring nodes share their local information to attain an optimal solution while retaining scalability and resilience to single-point failures/attacks. We first introduce the centralized version of the voltage control problem assuming grid observability and then transfer it to the distributed versions based on both bus-wise and area-wise decompositions of the network. The distributed version is solved via alternating direction method of multipliers (ADMM) that, for bus-wise decomposition, needs a full set of local measurements, whereas only a partial set of local measurements (that guarantee area-wise grid observability for each area) is needed for area-wise decomposition, along with neighbor-to-neighbor communications. Additionally, leveraging the availability of measurement data, the framework includes a distributed method to estimate the admittance matrix \mathbf Y of the underlying network graph. The proposed framework is validated against IEEE-30, IEEE-57 bus transmission systems, and IEEE-123 bus distribution systems and can tolerate certain levels of generation/load prediction uncertainties, modeling errors, and communication failures; plus, its in-built redundancy supports attack detection.

  2. Stability certification and identification of a safe and stabilizing initial set are two important concerns in ensuring operational safety, stability, and robustness of dynamical systems. With the advent of machine-learning tools, these issues need to be addressed for the systems with machine-learned components in the feedback loop. To develop a general theory for stability and stabilizability of neural network (NN)-controlled nonlinear systems subject to bounded parametric variations, a Lyapunov-based stability certificate is proposed and is further used to devise a maximal Lipschitz bound for a class of stabilizing NN controllers, and also a corresponding maximal Region of Attraction (RoA) within a user-specified safety set. To compute a robustly stabilizing NN controller that also maximizes the system’s long-run utility, a stability-guaranteed training (SGT) algorithm is proposed. The effectiveness of the proposed framework is validated through an illustrative example.

Embedded Software:

Networks:

Cyber-Physical Systems:

Agriculture Systems

Machine Learning

Resilience

Security

  1. Supervisory control and data acquisition/industrial control systems (SCADA/ICSs) networks are becoming more vulnerable to attacks that exploit the interdependence of security weaknesses at the atomic level to compromise system-level security. Attack graphs are an effective approach to depict these complex attack scenarios, assisting security administrators in determining how to best safeguard their systems. However, due to time and financial constraints, it is frequently not possible to address all atomic-level flaws at the same time. In this article, we propose a method for automatically detecting a minimal set of critical attacks that, when defended against, render the system secure. Finding a minimal label cut is typically an NP-complete problem. However, we propose a linear complexity approximation that uses the attack graph’s strongly connected components (SCCs) to create a simplified version of the graph in the form of a tree over the SCCs. Then, we perform an iterative backward search over this tree to find a set of backward-reachable SCCs, as well as their outward edges and labels, in order to find a cut of the tree with the fewest labels, which is a critical attack set. We put our proposed method to the test on real-world case studies, such as IT and SCADA networks for a cyber–physical system for water treatment, and outperformed previous state-of-the-art algorithms in terms of approximation accuracy and/or computational speed. Our solution provides security administrators with a practical and efficient method for prioritizing efforts to address vulnerabilities in SCADA/ICS networks.

  2. Prompt online detection of anomalies induced by malicious attacks enhances the efficacy of real-time operation and mitigation of attack, an indispensable part of any cyber-physical system (CPS) management. This article proposes a novel online rapid detection scheme that continuously monitors the data packet stream and infers the sequence of probability distributions, estimated as histograms, and alerts when a change in the histogram is detected, reporting both the attack as well as an estimate of its instant of commencement. A statistical data-driven attack model is proposed and employed that is general enough to represent two ubiquitous types of attacks on CPS: 1) replay and 2) bias-injection. The proposed detection framework relies on the fact that CPSs possess well-defined dynamics that are affected by quasistationary noise, which allows the histogram sequences of the system data packets to converge (to different distributions under the presence of the attack versus the absence of attack). The proposed online scheme detects an attack, and estimates the attack commencement time by relying on the computed distance between real-time estimated histogram versus apriori learned nominal histogram. Our formulation further sheds light on two different attack initiation-time-based subcases, “early” (attack starts before sufficient data of nominal behavior was collected to allow its histogram sequence to be closer to its nominal value) versus “late.” The designed algorithm of our scheme has linear time complexities in the dimension of data packets and algorithm parameters, which makes it suited for rapid detection. The proposed algorithm is implemented and validated on two real supervisory control and data acquisition system datasets, where a low detection delay demonstrates the effectiveness of the scheme.

Testing

Real Time

  1. We study diagnosis of timed discrete-event systems (TDESs) modeled as timed-automata. Earlier works on diagnosis of TDESs assumed that a diagnoser has partial observation of events but can measure (or observe) time with arbitrary precision. In practice, however, time can only be measured with finite precision. We model the finite precision observability of time using a digital-clock that measures time discretely by executing ticks. For the diagnosis purposes, the set of nonfaulty timed-traces is specified as another timed-automaton that is deterministic, generalizing the forms of nonfaulty specifications considered in the earlier works. We show that the set of timed-traces observed using a digital-clock with finite precision is regular, i.e., can be represented using a finite (untimed) automaton. We show that the verification of diagnosability (ability to detect the execution of a faulty timed-trace within a bounded time delay) as well as the offline synthesis of a diagnoser are decidable by reducing these problems to the untimed setting. The reduction of the diagnosis problem to the untimed setting also suggests an effective method for the offline computation of a diagnoser as well as its online implementation for diagnosis.

  2. We study the supervisory control of dense-time discrete event systems (DESs) in which the supervisors employ finite-precision digital-clocks to observe the event occurrence times, thereby relaxing the assumption of the prior works that time can be measured precisely. In our paper, the passing of time is measured using the number of ticks generated by a digital-clock and we allow the plant events and digital-clock ticks to occur concurrently. We formalize the notion of a control policy that issues the control actions based on the observations of events and their occurrence times as measured using a digital-clock, and show that such a control policy can be equivalently represented as a “digitalized”-automaton, namely, an untimed-automaton that evolves over events (of the plant) and ticks (of the digital-clock). We introduce the notion of observability with respect to the partial observation of time resulting from the use of a digital-clock, and show that this property together with controllability serves as a necessary and sufficient condition for the existence of a supervisor to enforce a real-time specification on a dense-time discrete event plant. The observability condition presented in the paper is very different from the one arising due to a partial observation of events since a partial observation of time is in general nondeterministic (the number of ticks generated in any time interval can vary from execution to execution of a digital-clock). We also present a method to verify the proposed observability and controllability conditions, and an algorithm to compute a supervisor when such conditions are satisfied.

Control

  1. This paper presents a mixed integer linear programming formulation for load-side control of electrical energy demand. The formulation utilizes demand prediction to determine if control actions are necessary, and it schedules both shedding and restoration times based on an optimization model that minimizes the net cost of load shedding. Operational constraints are satisfied through the use of minimum/maximum uptimes/downtimes, which depend upon the current state of the system. The algorithm is evaluated using a simulation model of an underground coal mining operation where, (i) its performance is compared with a traditional static, priority-based, load-shedding schedule, and, (ii) its potential is established for producing net savings through demand control.

Stochastics

  1. This article studies the prognosis of failure, i.e., its prediction prior to its occurrence, in stochastic discrete event systems. Prior work has focused on the definition and offline verification of m-steps stochastic-prognosability, or S_m-prognosability, which allows the prediction of a fault at least m-steps in advance. This article complements the existing work by proposing an algorithm for the computation of online failure prognoser. The proposed algorithm reduces the condition for issuing an affirmative prognostic decision to verification condition of a safety property of a Markov chain. We discuss how such a verification condition can be computed using a finitely terminating algorithm.

  2. This paper studies the prognosis of failure, i.e., its prediction prior to its occurrence, in stochastic discrete event systems. Prior work has focused on the definition and offline verification of m-steps Stochastic-Prognosability, or Sm-Prognosability, which allows the prediction of a fault at least m-steps in advance. This paper complements the existing work by proposing an algorithm for the computation of online failure prognoser. The proposed algorithm reduces the condition for issuing an affirmative prognostic decision to verification condition of a safety property of a Markov chain. We discuss how such a verification condition can be computed using a finitely terminating algorithm.

Temporal Logic

  1. In our earlier work, we introduced a state-based approach for the diagnosis of repeatedly occurring failures in discrete event systems (DESs). Since temporal logic provides a simpler way of specifying system properties, in this paper a temporal logic based approach for diagnosing the occurrence of a repeated number of failures is developed. Linear-time temporal logic (LTL) formulae are used to represent the specifications of DESs. Notions of pre-diagnosability for failures and diagnosability for repeated failures are introduced in the setting of temporal logic. A polynomial algorithm for the test of pre-diagnosability for failures is provided. The diagnosis problem for repeated failures in the temporal logic setting is reduced to one in a state-based setting, and so the prior results of state-based repeated failure diagnosis can be applied.

Bisimulation

  1. An article on bisimilarity enforcing supervisory control of discrete event systems was published recently in 2011, Issue 4, Volume 47, pages 782–788, of Automatica. The article introduced a certain definition of composition of plant and supervisor to define a controlled system, so as to come up with an existence condition of bisimilarity enforcing control. Such definition changes the semantics of event-based control to transition-based control, selectively disabling nondeterministic transitions. Any results reported in the paper thus raise question about value and applicability.

  2. Control for safety and nonblockingness using a deterministic supervisor requires the specification language be controllable and observable (under the setting that marking is also decided by a supervisor). We argue that there exist cases where the above properties do not hold, yet a safe and nonblocking control can be synthesized by allowing the supervisor to be nondeterministic. Use of a nondeterministic supervisor yields a controlled system that is nondeterministic for which a language equivalence only preserve the safety but not the nonblocking property, and so instead we require the stronger equivalence of bisimilarity (which preserves "sequential" behavior such as safety as well as "branching" behavior such as nonblockingness). This motivates us to consider control of deterministic systems for achieving bisimulation equivalence to possibly nondeterministic specifications. We introduce the notions of state-achievability (SA) and state-achievability-bisimilar (SAB) as part of the existence condition, and develop effective algorithms for verify the existence conditions as well as for synthesizing a supervisor when the existence condition holds. We show that the complexity of verifying the existence of a controller is polynomial, whereas that of computing a controller (when one exists) is singly exponential. The proposed approach can be applied to enforce any property that depends on branching and sequential behavior.

Discrete Event Systems

Discrete Event Systems: Modeling
  1. We previously introduced an inference-based framework for decentralized prognosis of discrete event systems, and formulated the notion of N-inference V-prognosability to characterize the existence of a disjunctive decentralized prognosis scheme such that any fault can be predicted prior to its occurrence by at least one of the prognosers, using at most N-levels of inferencing. While the disjunctive scheme relies on one of the prognosers making a positive decision, the dual conjunctive scheme relies on none of the prognosers making a negative decision. It is known that the two schemes are incomparable, and in this paper we extend our earlier work to provide a more general framework, by introducing a notion of N-inference prognosability, that captures both disjunctive and conjunctive schemes. We also develop a method for verifying N-inference prognosability.

  2. The analysis (verification, diagnosis, control, etc.) of discrete event systems requires a correct model of the system and of its specifications. In this paper, we present a new modeling formalism for generating valid models of complex systems. The class of systems this applies to is one which consists of signals that take binary values. The technique presented here makes the task of modeling considerably less cumbersome and less error-prone and is user-friendly. Another advantage of using this modeling formalism is that the size of the system model is polynomial in the number of signals, whereas the number of states in the automata models is exponential in the number of signals. We present automated techniques for deriving an automaton-based model from the model in the proposed formalism. We illustrate the modeling formalism using examples drawn from manufacturing systems and process control.

Discrete Event Systems: Stability
Discrete Event Systems: Control
  1. In our past work, we presented a framework for the decentralized control of discrete event systems involving inferencing over ambiguities about the system state of various local decision-makers, and introduced the notion of N-inference-observability as an existence condition of a certain decentralized supervisor. When a given specification fails to satisfy the N-inference-observability property, a supervisor achieving the given specification does not exist, and a technique for synthesizing a decentralized supervisor that achieves an N-inference-observable superlanguage is presented here (a dual problem of computing an N-inference-observable sublanguage was studied in our past work).

  2. The design of logic controllers for event-driven systems continue to rely largely on intuitive methods rather than on formal techniques. This approach results in a control code that requires extensive verification, is hard to maintain and modify, and may even fail at times. Supervisory control theory (SCT) provides a formal approach to logic control synthesis. In order to demonstrate the usefulness of the supervisory control theory in manufacturing systems, an educational test-bed that simulates an automated car assembly line has been built using LEGO/spl reg/ blocks. Finite state machines (FSMs) are used for modeling operations of the assembly line, and for the specifications that accomplish the task of successfully completing the assembly repeatedly. Using the technique of SCT, we derive a supervisor that enforces the specifications while offering the maximum flexibility of assembly. Subsequently a controller is extracted from the maximally permissive supervisor for the purpose of implementing the control by selecting, when possible, at most one controllable event from among the ones allowed by the supervisor. Testing to check the correctness of the control code is reduced, since the controller is guaranteed to enforce the specifications.

  3. A single maximally permissive and nonblocking supervisor to simultaneously fulfill several marked specifications pertaining to a single plant is investigated. Given a plant G and two marked specification languages K1 and K2, a supervisor S is said to be (K1,K2)-mutually nonblocking if (for i,j=1,2) Lm(G||S)∩Ki⊆Lm(G||S)∩Kj. This means that when the closed-loop system marks a trace of Ki, then it is always able to continue to a trace of Kj, also marked in the closed-loop system. Thus, the controlled system can execute traces within one specification while always being able to continue a trace of the other and hence not blocking the other specification. A complete, globally nonblocking and (K1,K2)-mutually nonblocking supervisor such that Lm(G||S)⊆K1∪K2 exists if and only if there exists a controllable mutually nonblocking sublanguage of the union of the specifications. There does exist a supremal such language. Furthermore, in the case that each specification is nonconflicting with respect to the prefix-closure of the other, this supremal language can be calculated by expressing it as the union of the supremal prefix-bounded sublanguages of the respective specifications. Finally, we show that the multiply nonblocking supervision of Thistle, Malhame, Hoang and Lafortune ((1997). Internal Report, Dept. de genie electrique et de genie informatique, Ecole Polytechnique de Montreal, Canada) is equivalent to globally and mutually nonblocking supervision.

  4. We study the existence and computation of extremal solutions of a system of inequations defined over lattices. Using the Knaster-Tarski fixed point theorem, we obtain sufficient conditions for the existence of supremal as well as infimal solution of a given system of inequations. Iterative techniques are presented for the computation of the extremal solutions whenever they exist, and conditions under which the termination occurs in a single iteration are provided. These results are then applied for obtaining extremal solutions of various inequations that arise in computation of maximally permissive supervisors in control of logical discrete event systems (DESs). Thus our work presents a unifying approach for computation of supervisors in a variety of situations.

Discrete Event Systems: Optimal Control
  1. We study the existence and computation of extremal solutions of a system of inequations defined over lattices. Using the Knaster-Tarski fixed point theorem, we obtain sufficient conditions for the existence of supremal as well as infimal solution of a given system of inequations. Iterative techniques are presented for the computation of the extremal solutions whenever they exist, and conditions under which the termination occurs in a single iteration are provided. These results are then applied for obtaining extremal solutions of various inequations that arise in computation of maximally permissive supervisors in control of logical discrete event systems (DESs). Thus our work presents a unifying approach for computation of supervisors in a variety of situations.

Discrete Event Systems: Diagnosis
  1. Certain faults, such as intermittent or non-persistent faults, may occur repeatedly. For discrete-event systems prone to repeated faults, Jiang, Kumar, and Garcia in 2003 introduced the notion of l-diagnosability requiring the diagnosis of the l th occurrence of a fault within a bounded delay. The present paper studies the identification of the set of all indices l for which the system is not l-diagnosable. (These are precisely the occurrence indices for which a repeatable-fault cannot be diagnosed.) We present an algorithm of cubic complexity to determine whether a system is diagnosable for every fault-occurrence index. For those systems for which the answer is negative, we show that the set of indices for which the system is non-diagnosable possesses the property that it is either finite or consists of a finite number of eventually periodic sets.

Discrete Event Systems: Prognosis
  1. This article studies the prognosis of failure, i.e., its prediction prior to its occurrence, in stochastic discrete event systems. Prior work has focused on the definition and offline verification of m-steps stochastic-prognosability, or S_m-prognosability, which allows the prediction of a fault at least m-steps in advance. This article complements the existing work by proposing an algorithm for the computation of online failure prognoser. The proposed algorithm reduces the condition for issuing an affirmative prognostic decision to verification condition of a safety property of a Markov chain. We discuss how such a verification condition can be computed using a finitely terminating algorithm.

  2. This paper studies the prognosis of failure, i.e., its prediction prior to its occurrence, in stochastic discrete event systems. Prior work has focused on the definition and offline verification of m-steps Stochastic-Prognosability, or Sm-Prognosability, which allows the prediction of a fault at least m-steps in advance. This paper complements the existing work by proposing an algorithm for the computation of online failure prognoser. The proposed algorithm reduces the condition for issuing an affirmative prognostic decision to verification condition of a safety property of a Markov chain. We discuss how such a verification condition can be computed using a finitely terminating algorithm.

  3. The task of failure prognosis requires the prediction of impending failures. This technical note formulates and studies the problem of distributed prognosis of discrete event systems, where the local prognosers exchange their observations for the sake of arriving at a prognostic decision. The observations are exchanged over communication channels that introduce bounded delays. A property of joint-prognosability is introduced to capture the condition under which any failure can be predicted by some local prognoser prior to its occurrence. We provide an algorithm to check the joint-prognosability property.

Discrete Event Systems: Fault Tolerance
Discrete Event Systems: Composition
  1. Studies the supervisory control problem of nondeterministic discrete event systems with driven events in the setting of masked prioritized synchronous composition (MPSC). MPSC was extended from prioritized synchronous composition (PSC) by Kumar and Heymann (2000) in order to permit systems interaction with their environment via interface masks. They also studied the supervisory control problem under the assumption that the set of driven events was empty. In this paper, the aforementioned assumption is relaxed. We first derive relations among behaviors at different levels of the system. Next, we solve the supervisor synthesis problem for controlling the plant behavior as observed at the interface level. Finally, we give a necessary and sufficient condition for the existence of the supervisor for controlling the plant behavior as observed at the plant level. We establish a link between MPSC and PSC by showing that a supervisory control problem in the setting of MPSC can be transferred to a supervisory control problem in the setting of PSC under certain conditions.

Discrete Event Systems: Infinite-behaviors
  1. Algorithms for computing a minimally restrictive control in the context of supervisory control of discrete-event systems have been well developed when both the plant and the desired behaviour are given as regular languages. In this paper the authors extend such prior results by presenting an algorithm for computing a minimally restrictive control when the plant behaviour is a deterministic Petri net language and the desired behaviour is a regular language. As part of the development of the algorithm, the authors establish the following results that are of independent interest: i) the problem of determining whether a given deterministic Petri net language is controllable with respect to another deterministic Petri net language is reducible to a reachability problem of Petri nets and ii) the problem of synthesizing the minimally restrictive supervisor so that the controlled system generates the supremal controllable sublanguage is reducible to a forbidden marking problem. In particular, the authors can directly identify the set of forbidden markings without having to construct any reachability tree.

Discrete Event Systems: Nondeterminism
  1. The supervisory control of nondeterministic discrete event dynamical systems (DEDSs) with driven events in the setting of prioritized synchronization and trajectory models introduced by Heymann are studied. Prioritized synchronization captures the notions of controllable, uncontrollable, and driven events in a natural way, and the authors use it for constructing supervisory controllers. The trajectory model is used for characterizing the behavior of nondeterministic DEDSs since it is a sufficiently detailed model (in contrast to the less detailed language or failures models), and serves as a language congruence with respect to the operation of prioritized synchronization. Results concerning controllability and observability in this general setting are obtained.

Contents