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Single-unit probabilistic safety assessment (SUPSA) has complex Boolean logic equations for accident sequences. Multi-unit probabilistic safety assessment (MUPSA) model is developed by revising and combining SUPSA models in order to reflect plant state combinations (PSCs). These PSCs represent combinations of core damage and non-core damage states of nuclear power plants (NPPs). Since all these Boolean logic equations have complemented gates (not gates), it is not easy to generate exact Boolean solutions. Delete-term approximation method (DTAM) has been widely applied for generating approximate minimal cut sets (MCSs) from the complex Boolean logic equations with complemented gates. By applying DTAM, approximate conditional core damage probability (CCDP) has been calculated in SUPSA and MUPSA. It was found that CCDP calculated by DTAM was overestimated when complemented gates have non-rare events. Especially, the CCDP overestimation drastically increases if seismic SUPSA or MUPSA has complemented gates with many non-rare events. The objective of this study is to suggest a new quantification method named probability subtraction method (PSM) that replaces DTAM. The PSM calculates accurate CCDP even when SUPSA or MUPSA has complemented gates with many non-rare events. In this paper, the PSM is explained, and the accuracy of the PSM is validated by its applications to a few MUPSAs.

This paper initially proposes an optimization problem and after presents its optimal solution. Then, this result is applied to obtain relaxed conditions to design controllers for nonlinear plants described by Takagi-Sugeno (TS) models, based on fuzzy Lyapunov function (FLF) and Linear Matrix Inequalities (LMI). The FLF is given by $V(x(t)) = x(t)^{T}P(\alpha (x(t)))x(t)$ , where $x(t)$ is the plant state vector, $P(\alpha (x(t))) = \alpha _{1}(x(t))P_{1} + \alpha _{2}(x(t))P_{2} + \cdots + \alpha _{r}(x(t))P_{r}$ , $P_{i}=P_{i}^{T} > 0$ and $\alpha _{i}(x(t))$ is the weight related to the local model $i$ in the representation of the plant by TS fuzzy models, for $i=1,2,\cdots,r$ . When one calculates the time derivative of this $V(x(t))$ , it appears the term $x(t)^{T}\dot {P}(\alpha (x(t)))x(t)$ , that is usually handled using conservative upper bounds, supposing that the bounds of the time derivative of $\alpha _{i}(x(t))$ , $i=1,2,\cdots,r$ , are available. The main result of this paper is a procedure to obtain optimal upper bounds for the term $x(t)^{T}\dot {P}(\alpha (x(t)))x(t)$ , such that they contemplate the maximum value and are always smaller than or equal to the maximum value. It is a relevant result on this subject, because these optimal upper bounds do not add any constraint. With these optimal upper bounds, a relaxed design method for stabilization of TS fuzzy models is proposed. Two numerical examples illustrate the effectiveness of this procedure.

This paper is about the estimation of the cyber-resilience of CPS. We define two new resilience estimation metrics: $k$ -steerability and $\ell $ -monitorability. They aim at assisting designers to evaluate and increase the cyber-resilience of CPS when facing stealthy attacks. The $k$ -steerability metric reflects the ability of a controller to act on individual plant state variables when, at least, $k$ different groups of functionally diverse input signals may be processed. The $\ell $ -monitorability metric indicates the ability of a controller to monitor individual plant state variables with $\ell $ different groups of functionally diverse outputs. Paired together, the metrics lead to CPS reaching $(k,\ell)$ -resilience. When $k$ and $\ell $ are both greater than one, a CPS can absorb and adapt to control-theoretic attacks manipulating input and output signals. We also relate the parameters $k$ and $\ell $ to the recoverability of a system. We define recoverability strategies to mitigate the impact of perpetrated attacks. We show that the values of $k$ and $\ell $ can be augmented by combining redundancy and diversity in hardware and software, in order to apply the moving target paradigm. We validate the approach via simulation and numeric results.

In this study, the authors address the problem of composite cooperative state estimation and system identification for linear multi-agent systems (MASs) under the leader-follower framework. This problem specifies an objective for each follower agent to estimate its local plant state and identify its plant dynamics simultaneously through interacting and communicating with its neighbours. A novel distributed adaptive estimation and identification protocol is proposed, which possesses five important properties to improve existing cooperative approaches: (i) it deals with general linear MASs with a completely unknown agent dynamics, allowing dynamics heterogeneity between the leader and the followers; (ii) it enables simultaneous exact state estimation and accurate system identification of MASs with guaranteed exponential convergence performance; (iii) it utilises only relative measurements from neighbouring agents, but without requiring any knowledge of absolute local plant states/outputs; (iv) it is fully distributed in the sense that its design and implementation do not involve any global information, including the overall network connectivity and the leader's dynamics; (v) exact system identification can be achieved under a cooperative persistent excitation (PE) condition, which significantly relaxes the classical PE condition. Simulation studies have been conducted to demonstrate the effectiveness of the proposed approach.

Based on the performance analysis on discrete-time ESO and the gain parameter design provided in Chap. 2, we consider the event-triggered extended state observer (ET-ESO) design for a continuous-time nonlinear system with uncertainty and disturbance in this chapter. The aim of the design is to guarantee the observation performance while saving communication source. Since the convergence of the ET-ESO directly depends on the design of the event-triggering condition, the primary task is to ensure an implementable event-triggering condition design that does not rely on the unknown plant state. For the ET-ESO introduced, we perform a rigorous analysis on its asymptotic performance. The obtained theoretical results are validated through numerical simulations.

International audience; Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.

Between 2016 and 2017, we conducted structured interviews with herbalists in market stands in the providence of Trujillo, La Libertad, Peru in order to create a catalog of plants with anticarcinogenic properties. Herbalists shared information about species they use in cancer treatment, including common names, part of the plant used, methods of preparation, plant state, and frequency and method of administration as medicine. We combined this information with the Shannon-Wiener diversity and evenness index to complete a quantitative analysis of the anticarcinogenic species. Our results demonstrate that 46 different species representing 24 genera and 19 families are locally used in cancer treatment, with a Shannon-Wiener index of 3.6 and 0.9 respectively. Our catalog thus represents a great variety of species and source of potentially useful knowledge for fighting cancer.

In this paper, the stabilization problem of nonlinear systems with time-varying input and state delays is considered by an approximate predictor. Due to the introduction of state delays, the nonlinear dynamic without state delays is used to predict the plant state, and a new error term is introduced into the approximation error between the prediction value and the future state of the plant. By Lyapunov–Krasovskii functional and small-gain argument, the stability of the whole closed-loop system is guaranteed provided that the sampling period, the predictor accuracy, and the upper bound of state delays satisfy an inequality constraint. The effectiveness of the proposed results is illustrated by a numerical example.

An algorithm was proposed to stabilize nonlinear systems with reduced level of impact of the measurement noise, parametric uncertainty, and external perturbation. Consideration was given to the noise of the measurements of dimensionality coinciding with that of the plant state vector. The parametric uncertainty and external perturbations can occur in any equation of the plant model. Conditions were obtained to calculate algorithm parameters in the form of solvability of the linear matrix inequality. Efficiency of the proposed scheme was illustrated by numerical examples.

Operators face challenges to plan alternative countermeasures when no procedure exists to address the current plant state. A model-based approach is desired to aid operators in acquiring plant resources and deriving response plans. Multilevel flow modeling (MFM) is a functional modeling methodology that can represent intentional knowledge about systems, which is essential in response planning. This article investigates the capabilities of MFM to plan alternatives. It is concluded that MFM has a knowledge capability to represent alternative means that are designed for given ends and a reasoning capability to identify alternative functions that can causally influence the goal achievement. The second capability can be applied to find originally unassociated means to achieve a goal. This is vital in a situation where all designed means have failed. A technique of procedure synthesis can be used to express identified alternatives as a series of operations. A case of station blackout occurring at the boiling water reactor is described. An MFM model of a boiling water reactor is built according to the analysis of goals and functions. The accident situations are defined by the model, and several alternative countermeasures in terms of operating procedures are generated to achieve the goal of core cooling. Keywords: Alternative Countermeasure, Decision-Making Support, Multilevel Flow Modeling, Response Planning, Station Blackout

Motivated by the problem of developing a system that can switch between low-level control vector fields, we study the feedback interconnection of a decision mechanism based on the pitchfork bifurcation with a plant whose dynamics are given by the linear combination of two control vector fields; the state of the pitchfork system then determines the linear combination coefficients for the plant. We show how the plant state can be fed back to the pitchfork by using the Lyapunov functions of the control vector fields as an unfolding parameter in the pitchfork, and derive conditions under which the closed-loop system exhibits a Hopf bifurcation leading to stable limit cycles.

Nuclear power plants have proved their worth in energy sector by providing clean and uninterrupted power over decades. However, a Nuclear Power Plant (NPP) is a complex, dynamic system with potential radioactive release risk which makes it crucial to achieve highest standards of safety. Specially, in preview of massive monitoring data received in modern NPPs which makes it difficult for operators to extract vital information about actual plant state in a timely and accurate manner. On the other hand, advancements in latest machine learning methods have made it possible to process such massive data for operators to act accordingly. However, current machine learning approaches cited for this field, fall short of required capabilities needed for such safety critical industry. In manuscript, an online fault monitoring system is proposed which utilizes deep neural networks and sliding window technique. The proposed model not only fulfills the requirement of validity but also encompass all necessary diagnosis functions like detection, identification, assessment and robustness. The model allows for a fault to be identified and assessed in different plant states and then validate the predicted results through online correlation of simulation vs original data. The study was conducted for IP-200 NPP utilizing RELAP5 thermal-hydraulic code. The proposed model was verified by inducing 04 different faults for different states and severities. The results were found to be conducive for improving reliability and accuracy of next generation fault monitoring systems of Nuclear Power Plants.

In this paper we consider the distributed estimation problem for continuous-time linear time-invariant (LTI) systems. A single linear plant is observed by a network of local observers. Each local observer in the network has access to only part of the output of the observed system, but can also receive information on the state estimates of its neigbours. Each local observer should in this way generate an estimate of the plant state. In this paper we study the problem of existence of a reduced order distributed observer. We show that if the observed system is observable and the network graph is a strongly connected directed graph, then a distributed observer exists with state space dimension equal to $Nn - \sum_{i =1}^N p_i$, where $N$ is the number of network nodes, $n$ is the state space dimension of the observed plant, and $p_i$ is the rank of the output matrix of the observed output received by the $i$th local observer. In the case of a single observer, this result specializes to the well-known minimal order observer in classical observer design., Comment: 12 pages, 1 figure

A new adaptive algorithm to compensate the unknown a priori multisinusoidal disturbance affecting the plant state and the measured output was proposed. It was intended for the plants that can be unstable, have time delay in the control channel and arbitrary relative degree of the model, as well as be nonminimal-phase, which is much superior to the existing counterparts.

A stabilization problem in a networked control system with packet dropouts is studied and dynamic output-feedback control is constructed. Predictive control approach is employed for obtaining plant state estimates at each step k. This eliminates the need for considering explicitly plant state switching when a packet dropout occurs.

This paper addresses the problem of event-triggered control for infinite-dimensional systems. We employ event-triggering mechanisms that compare the plant state and the error of the control input induced by the event-triggered implementation. Under the assumption that feedback operators are compact, a strictly positive lower bound on the inter-event times can be guaranteed. We show that if the threshold of the event-triggering mechanisms is sufficiently small, then the event-triggered control system with a bounded control operator and a compact feedback operator is exponentially stable. For infinite-dimensional systems with unbounded control operators, we employ two event-triggering mechanisms that are based on system decomposition and periodic event-triggering, respectively, and then analyze the exponential stability of the closed-loop system under each event-triggering mechanism., Comment: 31 pages, 5 figures

The paper describes the robust algorithm for compensation of mismatched disturbances. The synthesis of control system based on a backstepping algorithm and an auxiliary loop algorithm. The proposed algorithm guarantees that the plant state vector tends to the zero with the required accuracy. We also compare the proposed algorithm with the backstepping algorithm and the auxiliary loop algorithm. The simulation results illustrate the efficiency and robustness of the suggested control system.

The work is devoted to the method development of an optimal control systems design for nonlinear technical plants basis on a Jordan controlled form of their equations. The definition of this form for the plants with single control is given. The solution of the problem design of the optimal system there is if all the nonlinear plant state variables are measured and the plant equations are represented in Jordan controlled form. Analytical expressions which define the optimal control in the sense of a nonlinear square-law and undetermined criteria are given. The design example of the optimal system for nonlinear plants is resulted.

Control systems that are resilient against malicious attacks have recently become one of the important research topics. This is due to the fact that many critical infrastructures in our society such as power grid, nuclear facility, and public transportation, rely on feedback control technology, and that malfunction of said systems due to attack could be disastrous. Malicious attacks on control systems indeed occurred in recent years. The problem of resilient state estimation against sensor attacks, which is concerned of correctly estimating plant state despite of sensor attacks, have been previously addressed. Existing methods, however, lose the accuracy of the estimation if external disturbance exists. In this paper, we investigate the problem of resilient state estimation that are robust against external disturbance. Specifically, unknown input observer based state estimation has been developed to eliminate the effect of external disturbance on the state estimation that are resilient against malicious attacks on sensors. Associated design conditions have been derived. Additionally, disturbance observer based state estimation method has been developed. Validation of the proposed methods is provided through experiments.

The paper deals with design and comparison of adaptive control systems based on plant state vector and output for unmanned aerial vehicle (UAV) with nonlinearity and uncertainty of parameters of the aircraft incomplete measurability of its state and presence of wind disturbances. The results of computer simulations of flight stabilization processes on the example of the experimental model UAV-70V (Aerospace Academy, Hanoi) with presence of periodic and non-periodic vertical wind disturbances with designed adaptive control systems based on plant state vector with state observer and plant output.

It is common that an efficient constrained plant operation under full range of disturbance inputs require meeting different sets of control objectives. This calls for application of model predictive controllers each of them being best fit into specific operating conditions. It further requires that not only designing robustly feasible model predictive controllers is needed to satisfy the real plant state/output constraints, but also a switching mechanism between these controllers during the operation is inevitable. A simple hard switching may introduce unwanted transients and more importantly may not achieve robustly feasible controller operation. In this paper, the soft switching method for nonlinear systems that allows switching between robustly feasible model predictive controllers is presented. The algorithm for the fast switching method is also addressed for the switching mechanism parameter design that minimizes that switching time duration. The method is illustrated by the application to hydraulic optimizing control in the Drinking Water Distribution Systems example. Copyright © 2013 IFAC

A convex formulation is derived for optimizing dynamic feedback laws for constrained linear systems with polytopic uncertainty. We show that, when it exists, the maximal invariant ellipsoidal set for the plant state under a dynamic feedback law incorporating any chosen static feedback gain is equal to the maximal invariant ellipsoidal set under any linear feedback law. The dynamic controller and its associated invariant set define a computationally efficient robust model predictive control (MPC) law with prediction dynamics belonging to a polytopic uncertainty set. © 2005 IEEE.

In this paper, the model-based networked control is addressed for singularly perturbed systems with time-varying transmission times. The slow and fast systems of singularly perturbed systems are used to approximate the plant state behavior, by which one can reduce the usage of network. Under the condition that their slow and fast systems are both Hurwitz stable, the approximate solutions of the whole systems are derived and it is shown that the whole systems via the network control are robust practically stable if the perturbation parameter is sufficient small.

Recently, attention has been drawn to the local modeling techniques of a new idea called “Just-In-Time (JIT) modeling”. To apply “JIT modeling” to a large amount of database online, “Large-scale database-based Online Modeling (LOM)” has been proposed. LOM is a technique that makes the retrieval of neighboring data more efficient by using both “stepwise selection” and quantization. In order to predict the long-term state of the plant without using future data of manipulated variables, an Extended Sequential Prediction method of LOM (ESP-LOM) has been proposed. In this paper, the LOM and the ESP-LOM are introduced.

This paper proposes an anti-windup design technique for systems which contain two sources of nonlinearity, namely actuator saturation and a quadratic nonlinearity in the plant state equation. It is shown how a nominal nonlinear dynamic inversion controller can be augmented with an anti-windup compensator of a form similar to that which is popular in linear anti-windup compensation. It is shown further how the free parameters of this compensator can be chosen, using linear matrix inequalities, in a way in which the region of attraction of the closed-loop compensated system is enlarged. A numerical example illustrates the effectiveness of the proposed technique.

Monitoring of greenhouse plants gives a straight diagnosis on their health and wellness. A computer vision method was investigated for detection and localization of leaves’ wilting as a sign of visual appearance. Tomato, cucumber and paprika were tested using side projected canopy images. The camera was focused on the plant canopy from horizontal direction in order to obtain images covering the leaves. At defined time scales images were taken to analyze leaf and stem movements as the visual signs of plant state. The machine based control can be applied successfully in crop irrigation. The parameters of the control unit were identified and adjusted in each case of the plant, as well.