Optimal linear filter design for process state and packet loss estimation in networked control systems

Considering the effect of packet losses on the behavior of networked systems, this work is concerned with estimation of the packet loss occurrences in the input channels possibly together with the system state. For this purpose, the commonly used Markov chain model of the successive packet loss occurrences is transformed to a linear recursive model in which the packet loss occurrence variables appear as new state variables. Two methods are proposed for combining the recursive packet loss model with the plant model to obtain an overall model for the whole networked control system (NCS). In the first method, a state space model of the plant is used which allows for simultaneous estimation of the packet loss occurrences and the plant state. In the second method, an input-output model of the plant is employed which allows for estimating only the packet loss occurrences. Both the zero and the hold packet loss handling strategies are considered and stability of the filters is analyzed. The proposed methods are compared with some existing results during an example to show their advantages.

Identification of Boolean control networks with time delay

This paper investigates the identification of time-delay Boolean networks (TBNs) and time-delay Boolean control networks (TBCNs) via Cheng product. According to all admissible (input-)output sequences, definition on identifiability of the (TBCN) TBN is given. Two algorithms are designed to select suitable delay parameters of the TBN and TBCN, respectively. Based on these, the original systems are divided into several subsystems. Then by virtue of observability, the criteria for identifiability of the TBN and TBCN are obtained. Moreover, the corresponding constructing processes are presented to establish the internal structures of the TBN and TBCN. Finally, two illustrative examples are given to show the feasibility of the proposed methods.

An English list of trait words including valence, social desirability, and observability ratings

To enable flexible and controlled research on personality, information processing, and interactions in socio-emotional contexts, the availability of highly controlled stimulus material, especially trait words and related attributes, is indispensable. Existing word databases contain mainly nouns and rating dimensions, and their role in studies within socio-emotional contexts are limited. This study aimed to create an English list of traits (ELoT), a database containing 500 trait adjectives rated by a large sample (n = 822, 57.42% female). The rating categories refer to the perceived valence associated with the traits and their social desirability and observability. Participants of different ages (18 to 65 years of age) and educational levels rated the words in an online survey. Both valence and social desirability ratings showed a bimodal distribution, indicating that most traits were rated either positive (respectively socially desirable) or negative (respectively socially undesirable), with fewer words rated as neutral. For observability, a bell-shaped distribution was found. Results indicated a strong association between valence and social desirability, whereas observability ratings were only moderately associated with the other ratings. Valence and social desirability ratings were not related to participants' age or gender, but observability ratings were different for females and males, and for younger, middle-aged, and older participants. The ELoT is an extensive, freely available database of trait norms. The large sample and the balanced age and gender distributions allow to account for age- and gender-specific effects during stimulus selection.

ϵ-Controllability/ϵ-Observability of linear switched sampled-data systems with the non-equidistant time schedule

In this work, a novel perspective is developed to investigate the property of controllability/observability of linear switched sampled-data systems under the non-equidistant sampling schedule. In this regard, two new notions are introduced as ϵ-controllability and ϵ-observability to create a feasible ground for controlling and observing linear switched sampled-data systems with any initial time chosen from outside of ϵ neighborhood of switching instants. The main motivation is to control and observe the given switched sampled-data system, which consists of a finite number of discrete-time sub-systems within each sub-system. Hence, the system requires a lower number of sampling times for ϵ-controllability and ϵ-observability compared to the original notions of controllability and observability in this context. Although the number of sampling times in a certain time interval increases whenever ϵ tends to zero, the number of sampling candidates required for ϵ-controllability and ϵ-observability becomes finite. Numerical experiments are performed on several continuous time switched linear systems, and ϵ-controllability and ϵ-observability of their corresponding switched sampled-data systems are derived for various ϵ values under constructed non-equidistant sampling patterns.

A unified method for assessing the observability of dynamic complex systems

PROBLEM

Systems theory applied to biology and medicine assumes that the complexity of a system can be described by quasi-generic models to predict the behavior of many other similar systems. To this end, the aim of various research works in systems theory is to develop inductive modeling (based on data-intensive analysis) or deductive modeling (based on the deduction of mechanistic principles) to discover patterns and identify plausible correlations between past and present events, or to connect different causal relationships of interacting elements at different scales and compute mathematical predictions. Mathematical principles assume that there are constant and observable universal causal principles that apply to all biological systems. Nowadays, there are no suitable tools to assess the soundness of these universal causal principles, especially considering that organisms not only respond to environmental stimuli (and inherent processes) across multiple scales but also integrate information about and within these scales. This implies an uncontrollable degree of uncertainty.

METHODOLOGY

A method has been developed to detect the stability of causal processes by evaluating the information contained in the trajectories identified in a phase space. Time series patterns are analyzed using concepts from geometric information theory and persistent homology. In essence, recognizing these patterns in different time periods and evaluating their geometrically integrated information leads to the assessment of causal relationships. With this method, and together with the evaluation of persistent entropy in trajectories in relation to different individual systems, we have developed a method called Φ-S diagram as a complexity measure to recognize when organisms follow causal pathways leading to mechanistic responses.

RESULTS

We calculated the Φ-S diagram of a deterministic dataset available in the ICU repository to test the method's interpretability. We also calculated the Φ-S diagram of time series from health data available in the same repository. This includes patients' physiological response to sport measured with wearables outside laboratory conditions. We confirmed the mechanistic nature of both datasets in both calculations. In addition, there is evidence that some individuals show a high degree of autonomous response and variability. Therefore, persistent individual variability may limit the ability to observe the cardiac response. In this study, we present the first demonstration of the concept of developing a more robust framework for representing complex biological systems.

Performance analysis of typical linear augmented observers for a class of MIMO systems with nonlinear uncertainty

To estimate the states and the uncertainties for multi-input-multi-output (MIMO) systems is significant in control engineering. The paper investigates the estimating performance of a widely-used linear augmented observer (LAO) by quantitatively analyzing the biased term of estimation error, which generalizes the previous studies. Then the necessary and sufficient condition for unbiased estimation of LAO is thoroughly discussed. More importantly, by investigating the observability of MIMO uncertain systems, the paper proves that the estimation of LAO is unbiased if and only if the states and the uncertainties of MIMO uncertain systems are observable. The presented theoretical analysis can help practitioners evaluate the feasibility of estimating the specific uncertainties via LAO in practical systems. Finally, even if the estimation of LAO is biased, the design procedure of extended state observer is presented, which offers the unbiased estimation of total disturbance and the derivatives of output.

Climate Action for (My) Children

How do we motivate cooperation across the generations-between parents and children? Here we study voluntary climate action (VCA), which is costly to today's decision-makers but essential to enable sustainable living for future generations. We predict that "offspring observability" is critical: parents will be more likely to invest in VCA when their own offspring observes their action, whereas when adults or genetically unrelated children observe them, the effect will be smaller. In a large-scale lab-in-the-field experiment, we observe a remarkable magnitude of VCA: parents invest 82% of their 69€ endowment into VCA, resulting in almost 14,000 real trees being planted. Parents' VCA varies across conditions, with the largest treatment effect occurring when a parent's own child is the observer. In subgroup analyses, we find that larger treatment effects occur among parents with a high school diploma. Moreover, VCA for parents who believe in climate change is most affected by the presence of their own child. In contrast, VCA of climate change skeptical parents is most influenced by the presence of children to whom they are not related. Our findings have implications for policy-makers interested in designing programs to encourage voluntary climate action and sustaining intergenerational public goods.

Structural identifiability and observability of compartmental models of the COVID-19 pandemic

The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights - as well as the possibility of controlling the system - may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.

Offset-free MPC strategy for nonzero regulation of linear impulsive systems

In various biomedical applications, drug administration treatment can be modeled as an impulsive control system. Despite the development of different control strategies for impulsive systems, the elimination of the offset generated by a plant-model mismatch has not yet been researched. In biomedical systems, this mismatch is a consequence of physiological changes and can result in inaccurate treatment of patients. Therefore, control techniques that accomplish the objectives by compensating the effect of variations are required. The present paper proposes and substantiates a novel offset-free model predictive control (MPC) strategy for impulsive systems. To that aim, an impulsive disturbance model is introduced, and an observer design is developed that includes new observability criteria for estimating the disturbance and the state. Further, it is demonstrated that the proposed control strategy achieves zero offset tracking from an analysis of the observer and the controller at steady state. Additionally, the controller incorporates a recent MPC formulation to steer the state to an equilibrium set using artificial/intermediary variables to achieve nonzero regulation. Finally, these results are evaluated and illustrated using a dynamical model for type 1 diabetic patients.

From the hospital scale to nationwide: observability and identification of models for the COVID-19 epidemic waves

Two mathematical models of the COVID-19 dynamics are considered as the health system in some country consists in a network of regional hospital centers. The first macroscopic model for the virus dynamics at the level of the general population of the country is derived from a standard SIR model. The second local model refers to a single node of the health system network, i.e. it models the flows of patients with a smaller granularity at the level of a regional hospital care center for COVID-19 infected patients. Daily (low cost) data are easily collected at this level, and are worked out for a fast evaluation of the local health status thanks to control systems methods. Precisely, the identifiability of the parameters of the hospital model is proven and thanks to the availability of clinical data, essential characteristics of the local health status are identified. Those parameters are meaningful not only to alert on some increase of the infection, but also to assess the efficiency of the therapy and health policy.

Optimal placement of phasor measurement units considering islanding contingency, communication infrastructure, and quality of service

In this study, the PMUs are placed to operate in normal and islanded cases taking into account power system observability, reliability, Communication Infrastructure (CI), and latency time associated with this CI. Moreover, the economic study for additional new data transmission paths is considered as well as the preexisting Communication Devices (CDs) and the availability of predefined locations of some PMUs in some buses. The PMUs placement and their communication network topology and link channel capacity are co-optimized simultaneously. Two different approaches are applied to optimize the objective function; the first approach is combined from Binary Teaching Learning Based Optimization Algorithm (BTLBOA) and the Minimum Spanning Tree (MST) algorithm, while the second approach is based on BTLBOA. The proposed approaches are examined using IEEE 118-bus systems.

Suing for Peace in the War Against Mentalism

The antimentalists' war against mentalism has not vanquished it. To examine why, we focus on two theses-mind as causal and internal-and three standard attacks against mentalism as defined by both theses: 1) mentalism implies dualism; 2) mind is unobservable, which hinders its scientific study; and 3) mentalism is impractical. These salients fail because: 1) if the mind is causal and internal, it must be material; 2) the observable/unobservable distinction is too problematic, with antimentalists equivocal about where to draw that line, with some even embracing publicly unobservable behavior as causally relevant; and 3) mentalism has not been demonstrated to be less practical than antimentalism. For the war on mentalism to succeed, stronger attacks must be devised, both scientific and philosophical. We contemplate some possibilities, while expressing doubts as to the wisdom of continuing the war. Peace may be better than war, and the resulting intellectual commerce may be good for both sides.

Calculation of controllability and observability matrices for special case of continuous-time multi-order fractional systems

In this paper, controllability and observability matrices for pseudo upper or lower triangular multi-order fractional systems are derived. It is demonstrated that these systems are controllable and observable if and only if their controllability and observability matrices are full rank. In other words, the rank of these matrices should be equal to the inner dimension of their corresponding state space realizations. To reduce the computational complexities, these matrices are converted to simplified matrices with smaller dimensions. Numerical examples are provided to show the usefulness of the mentioned matrices for controllability and observability analysis of this case of multi-order fractional systems. These examples clarify that the duality concept is not necessarily true for these special systems.

Observation and sliding mode observer for nonlinear fractional-order system with unknown input

The main purpose of this paper is twofold. First, the observability and the left invertibility properties and the observable canonical form for nonlinear fractional-order systems are introduced. By using a transformation, we show that these properties can be deduced from an equivalent nonlinear integer-order system. Second, a step by step sliding mode observer for fault detection and estimation in nonlinear fractional-order systems is proposed. Starting with a chained fractional-order integrators form, a step by step first-order sliding mode observer is designed. The finite time convergence of the observer is established by using Lyapunov stability theory. A numerical example is given to illustrate the performance of the proposed approach.

Two-stage observer based offset-free MPC

This paper addresses design of model predictive control (MPC) for dynamic systems in the presence of disturbances. For the systems with disturbances, it is common to use observers to estimate states and disturbances, allowing control algorithm to reject disturbances. In this paper, we demonstrate how one can extend model predictive control for systems with disturbances by applying full state and disturbance observer. To this aim, a two-stage approach is proposed where the observer design procedure is completely decoupled from the MPC problem, known as the separation principle. We focus on the decoupling principal and it is shown that the observer can be designed independently via a norm minimization problem to reduce effects of disturbances and model mismatch. On the other hand, the MPC law is obtained using the multi-parametric quadratic programming approach where the parameters are the components of the state vector.

Structural redundancy of data from wastewater treatment systems. Determination of individual balance equations

Although data reconciliation is intensely applied in process engineering, almost none of its powerful methods are employed for validation of operational data from wastewater treatment plants. This is partly due to some prerequisites that are difficult to meet including steady state, known variances of process variables and absence of gross errors. However, an algorithm can be derived from the classical approaches to data reconciliation that allows to find a comprehensive set of equations describing redundancy in the data when measured and unmeasured variables (flows and concentrations) are defined. This is a precondition for methods of data validation based on individual mass balances such as CUSUM charts. The procedure can also be applied to verify the necessity of existing or additional measurements with respect to the improvement of the data's redundancy. Results are given for a large wastewater treatment plant. The introduction aims at establishing a link between methods known from data reconciliation in process engineering and their application in wastewater treatment.

The Effects of Perceived Normative Pressure and Observability of Behavior on Intention to Perform Sun Protection and Nutrition Behaviors on Behalf of Young Children among Parents

This paper describes research on two normative concepts thought to impact health behaviors: injunctive and descriptive norms. The study tests whether the extent to which the same health behavior is enacted in an observable or non-observable setting will lead to variation in normative influence on parent intention. In on-line experiments conducted in winter 2009, 467 participants were randomized to a behavioral scenario in which the health behavior was described as occurring in an observable or non-observable setting. For sun protection behaviors, observability primed the influence of descriptive norms on intention. For nutrition behaviors, observability primed the influence of injunctive norms on intention. Across both conditions, observability of the behavioral scenario increased the strength of the association between norms and intention.