System Theoretic Approach Research Articles

Integrated methodology for safety analysis based on a system-theoretic approach and numerical simulation

The System-Theoretic Process Analysis (STPA) method is a recognized approach to system safety analysis. However, it still has inadequate capabilities for quantitative safety analysis. To overcome this limitation, an integrated methodology was investigated for quantitative safety analysis of the complex socio-technical system that combines a system-theoretic approach and numerical simulation. In the proposed methodology, STPA method is utilized to reveal potential unsafe control actions (UCAs) and corresponding causes based on the operational principle of the target system from systemic perspective. Moreover, the consequences of identified UCAs can be quantified and safety constraints also can be improved to prevent UCAs using numerical simulations. As a complex system, the blind shear ram preventers (BSRPs) in deepwater drilling activities, with tightly interacting diverse subsystems or components, is employed to illustrate the applicability of the methodology. The results verified that the proposed methodology could evaluate potential hazards and quantify the analysis results. These results will be helpful for the design and safe operations of the BSRP system. The developed methodology has the potential to be used for safety analyses in other process industries.

Safety analysis of evaluation and acceptance processes for space systems and operations based on system-theoretic approach

The proposed analysis consists of identifying systemic aspects that can influence safety and mission fulfillment in the Evaluation and Acceptance Processes of Space Systems and Operations for Launch through the application of System-Theoretic Process Analysis, a technique capable of identifying potential hazardous design and operational flaws, including system design errors and unsafe interactions among multiple procedures and system components. This research identifies losses, hazards, system-level safety constraints, the control structure of the general system, unsafe control actions, loss scenarios that could occur and related causal factors, detecting improvement possibilities for future verifications, evaluations, approvals, and acceptances of space systems and launch operations. These findings can promote safety in space system designs and operations, supporting the activities conducted by launch vehicle and payload developers, certification authorities, and launch centers management, enabling means to proactively act in order to mitigate risks, avoiding unsafe actions and undesired system behaviors, or even to mitigating their consequences. The practical applications of this work can result in safety and mission fulfillment improvements for safety management systems, launch approval regulations and standards; launch operation procedures; space systems design; vehicle, payload, and ground support equipment productions; systems testing; and launch authorization processes.

Algebraic Reduction of Hidden Markov Models

The problem of reducing a Hidden Markov Model (HMM) to one of smaller dimension that exactly reproduces the same marginals is tackled by using a system-theoretic approach. Realization theory tools are extended to HMMs by leveraging suitable algebraic representations of probability spaces. We propose two algorithms that return coarse-grained equivalent HMMs obtained by stochastic projection operators: the first returns models that exactly reproduce the single-time distribution of a given output process, while in the second the full (multi-time) distribution is preserved. The reduction method exploits not only the structure of the observed output, but also its initial condition, whenever the latter is known or belongs to a given subclass. Optimal algorithms are derived for a class of HMM, namely observable ones.

A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships

Shore control center (SCC) operators of maritime autonomous surface ships (MASS) may be allocated with complex responsibilities within different degrees of autonomy (DoA), from remote control to remote supervision, and the role of human in-the-loop is significant. In the current research on MASS, however, the safety of interactions between humans and systems are not sufficiently considered. Hence, this paper proposes a system-theoretic approach to safety analysis for human-system collaboration. The novelty of the approach is the definition of operational contexts of MASS and the integration of a human cognitive model into the system theoretic process analysis (STPA), called STPA-Cog. The method is demonstrated in two case studies of MASS: (i) remote control mode (RCM) and (ii) remote supervision mode (RSM), focusing on two types of navigational accidents (i.e., collision and grounding). The analysis results are derived by comparing the human-related and technical-related causal scenarios in RCM and in RSM. The findings can assist in human-oriented design and operational planning for SCC of MASS. Further, the proposed approach also has the potential to be used and extended to systemic safety analysis in other intelligent transportation systems.

Complex roughening dynamics and wettability mechanism in MoS2 thin films — A system theoretic approach

We utilized fractal geometry to advance insights into the complex roughening and its facilitated wettability mechanism of sputtered MoS2 film surfaces with varying thickness are studied. Fractal parameters indicate the dominance of shadowing at lower thickness while enhancement in vertical growth rate and variation in local irregularities are observed at higher thicknesses along with anomalous roughening up to thickness of 200 nm. In addition, anti-persistent trend of surface heights was observed to be independent of thickness. Multifractal analysis exposed the non-linear variation in multifractality strength and vertical complexities of surface heights. The results corroborates the stereometric analysis revealing the 3D surface topography of analyzed films. The wettability was observed to increase with thickness. In consideration, the roughening expedited wetting trend is defined in light of stereometric parameters and multifractility strength suggesting increases the penetration of water droplets into the surface voids with decline in the surface complexities. The mechanism for increase in contact angle is realized from the augmentation in curvature pressure indicating the existence of large air pressure within the void lying lower than the water droplets as compared to the pressure from the meniscus sides of the droplets. In addition, principal component analysis is utilized to reveal the significance of fractal parameters in probing the surface roughening. The results hold prospects in surface engineering of MoS2 thin films as a function of thickness for enhanced catalytic activity in electrochemical splitting of water for hydrogen evolution reactions.

Epidemic population games and evolutionary dynamics

We propose a system theoretic approach to select and stabilize the endemic equilibrium of an SIRS epidemic model in which the decisions of a population of strategically interacting agents determine the transmission rate. Specifically, the population’s agents recurrently revise their choices out of a set of strategies that impact to varying levels the transmission rate. A payoff vector quantifying the incentives provided by a planner for each strategy, after deducting the strategies’ intrinsic costs, influences the revision process. An evolutionary dynamics model captures the population’s preferences in the revision process by specifying as a function of the payoff vector the rates at which the agents’ choices flow toward strategies with higher payoffs. Our main result is a dynamic payoff mechanism that is guaranteed to steer the epidemic variables (via incentives to the population) to the endemic equilibrium with the smallest infectious fraction, subject to cost constraints. We use a Lyapunov function not only to establish convergence but also to obtain an (anytime) upper bound for the peak size of the population’s infectious portion.

On biological networks capable of robust adaptation in the presence of uncertainties: A linear systems-theoretic approach.

Biological adaptation, the tendency of every living organism to regulate its essential activities in environmental fluctuations, is a well-studied functionality in systems and synthetic biology. In this work, we present a generic methodology inspired by systems theory to discover the design principles for robust adaptation, perfect and imperfect, in two different contexts: (1)in the presence of deterministic external and parametric disturbances and (2)in a stochastic setting. In all the cases, firstly, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as design requirements for the underlying networks. Thus, contrary to the existing approaches, the proposed methodologies provide an exhaustive set of admissible network structures without resorting to computationally burdensome brute-force techniques. Further, the proposed frameworks do not assume prior knowledge about the particular rate kinetics, thereby validating the conclusions for a large class of biological networks. In the deterministic setting, we show that unlike the incoherent feed-forward network structures (IFFLP), the modules containing negative feedback with buffer action (NFBLB) are robust to parametric fluctuations when a specific part of the network is assumed to remain unaffected. To this end, we propose a sufficient condition for imperfect adaptation and show that adding negative feedback in an IFFLP topology improves the robustness concerning parametric fluctuations. Further, we propose a stricter set of necessary conditions for imperfect adaptation. Turning to the stochastic scenario, we adopt a Wiener-Kolmogorov filter strategy to tune the parameters of a given network structure towards minimum output variance. We show that both NFBLB and IFFLP can be used as a reduced-order W-K filter. Further, we define the notion of nearest neighboring motifs to compare the output variances across different network structures. We argue that the NFBLB achieves adaptation at the cost of a variance higher than its nearest neighboring motifs whereas the IFFLP topology produces locally minimum variance while compared with its nearest neighboring motifs. We present numerical simulations to support the theoretical results. Overall, our results present a generic, systematic, and robust framework for advancing the understanding of complex biological networks.

Cybersafety: A System-Theoretic Approach to Identify Cyber-Vulnerabilities & Mitigation Requirements in Industrial Control Systems

Recent cyber-physical attacks, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stuxnet, Triton</i> etc., have invoked an ominous realization about the vulnerability of critical infrastructure, including water, power and gas distribution systems. Traditional IT security-biased protection methods that focus on improving <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyber hygiene</i> are largely impotent in the face of targeted attacks by advanced cyber-adversaries. Thus, there is an urgent need to analyze the safety and security of critical infrastructure in a holistic fashion, leveraging the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physics</i> of the cyber-physical system. System-Theoretic Accident Model & Processes (STAMP) offers a powerful framework to analyze complex systems; hitherto, STAMP has been used extensively to perform safety analyses but an integrated safety and cybersecurity analysis of industrial control systems (ICS) has not been published. This paper uses the electrical generation and distribution system of an archetypal industrial facility to demonstrate the application of a STAMP-based method – called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cybersafety</i> – to identify and mitigate cyber-vulnerabilities in ICS. The key contribution of this work is to differentiate the additional steps required to perform a holistic cybersecurity analysis for an ICS of significant size and complexity and to present the analysis in a structured format that can be emulated for larger systems with many interdependent subsystems.

Control-oriented modeling of a high-aspect-ratio flying wing with coupled flight dynamics

The flight-structural dynamics of a high-aspect-ratio wing challenge the flight control design. This paper develops a reduced model of coupled dynamics with stability consideration. The structural dynamics are formulated with dihedrals, and the central loads drive the deformation. The control-oriented model with essential coupled dynamics is formulated. Modal sensitivity analysis and input–output pairing are performed to identify the control structure. Besides, an example of flight control design is provided to discuss the necessity of considering structural dynamics in controller design. Analytical coupled flight dynamics provide a system-theoretic approach for stability and facilitate model-based control techniques. Simulation results reveal the characteristics of flight-structural coupled dynamics and demonstrate that the influence of flexible modes should be considered in control design, especially in lateral dynamics.

A system-theoretic approach for image-based infectious plant disease severity estimation.

The demand for high level of safety and superior quality in agricultural products is of prime concern. The introduction of new technologies for supporting crop management allows the efficiency and quality of production to be improved and, at the same time, reduces the environmental impact. Common strategies to disease control are mainly oriented on spraying pesticides uniformly over cropping areas at different times during the growth cycle. Even though these methodologies can be effective, they present a negative impact in ecological and economic terms, introducing new pests and elevating resistance of the pathogens. Therefore, consideration for new automatic and accurate along with inexpensive and efficient techniques for the detection and severity estimation of pathogenic diseases before proper control measures can be suggested is of great realistic significance and may reduce the likelihood of an infection spreading. In this work, we present a novel system-theoretic approach for leaf image-based automatic quantitative assessment of pathogenic disease severity regardless of disease type. The proposed method is based on a highly efficient and noise-rejecting positive non-linear dynamical system that recursively transforms the leaf image until only the symptomatic disease patterns are left. The proposed system does not require any training to automatically discover the discriminative features. The experimental setup allowed to assess the system ability to generalise symptoms detection beyond any previously seen conditions achieving excellent results. The main advantage of the approach relies in the robustness when dealing with low-resolution and noisy images. Indeed, an essential issue related to digital image processing is to effectively reduce noise from an image whilst keeping its features intact. The impact of noise is effectively reduced and does not affect the final result allowing the proposed system to ensure a high accuracy and reliability.

Sparse System Identification of Leptin Dynamics in Women With Obesity.

The prevalence of obesity is increasing around the world at an alarming rate. The interplay of the hormone leptin with the hypothalamus-pituitary-adrenal axis plays an important role in regulating energy balance, thereby contributing to obesity. This study presents a mathematical model, which describes hormonal behavior leading to an energy abnormal equilibrium that contributes to obesity. To this end, we analyze the behavior of two neuroendocrine hormones, leptin and cortisol, in a cohort of women with obesity, with simplified minimal state-space modeling. Using a system theoretic approach, coordinate descent method, and sparse recovery, we deconvolved the serum leptin-cortisol levels. Accordingly, we estimate the secretion patterns, timings, amplitudes, number of underlying pulses, infusion, and clearance rates of hormones in eighteen premenopausal women with obesity. Our results show that minimal state-space model was able to successfully capture the leptin and cortisol sparse dynamics with the multiple correlation coefficients greater than 0.83 and 0.87, respectively. Furthermore, the Granger causality test demonstrated a negative prospective predictive relationship between leptin and cortisol, 14 of 18 women. These results indicate that increases in cortisol are prospectively associated with reductions in leptin and vice versa, suggesting a bidirectional negative inhibitory relationship. As dysregulation of leptin may result in an abnormality in satiety and thereby associated to obesity, the investigation of leptin-cortisol sparse dynamics may offer a better diagnostic methodology to improve better treatments plans for individuals with obesity.

Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference

We consider the Bayesian approach to the linear Gaussian inference problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken after the initial time. In practical applications, the large dimension of the dynamical system state poses a computational obstacle to computing the exact posterior distribution. Model reduction offers a variety of computational tools that seek to reduce this computational burden. In particular, balanced truncation is a system-theoretic approach to model reduction which obtains an efficient reduced-dimension dynamical system by projecting the system operators onto state directions which trade off the reachability and observability of state directions as expressed through the associated Gramians. We introduce Gramian definitions relevant to the inference setting and propose a balanced truncation approach based on these inference Gramians that yield a reduced dynamical system that can be used to cheaply approximate the posterior mean and covariance. Our definitions exploit natural connections between (i) the reachability Gramian and the prior covariance and (ii) the observability Gramian and the Fisher information. The resulting reduced model then inherits stability properties and error bounds from system theoretic considerations, and in some settings yields an optimal posterior covariance approximation. Numerical demonstrations on two benchmark problems in model reduction show that our method can yield near-optimal posterior covariance approximations with order-of-magnitude state dimension reduction.

A Novel System-Theoretic Matrix-Based Approach to Analysing Safety and Security of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are getting increasingly complex and interconnected. Consequently, their inherent safety risks and security risks are so intertwined that the conventional analysis approaches which address them separately may be rendered inadequate. STPA (Systems-Theoretic Process Analysis) is a top-down hazard analysis technique that has been incorporated into several recently proposed integrated Safety and Security (S&amp;S) analysis methods. This paper presents a novel methodology that leverages not only STPA, but also custom matrices to ensure a more comprehensive S&amp;S analysis. The proposed methodology is demonstrated using a case study of particular commercial cloud-based monitoring and control system for residential energy storage systems.

Analysis of assembly-time performance (ATP) in manufacturing operations with collaborative robots: a systems approach

Reducing station processing times has a significant importance in manufacturing assembly systems. In recent years, there has been a growing interest in using collaborative robots to assist human operators in many manufacturing systems, which can not only improve ergonomics measures but also reduce processing time and increase throughput. In this paper, a system-theoretic approach is introduced to analyse the assembly-time performance (ATP) of assembly systems with collaborative robots, where ATP is defined as the probability to finish all the assembly operations in a station within a desired time interval. Specifically, the assembly operations are described by stochastic processes with both individual (human operator and robot) preparation tasks and joint collaboration tasks, characterised by general or arbitrary distributions of task times. Then an efficient algorithm is presented by using gamma distributions to approximate task times and aggregate multiple interacting tasks to calculate ATP. High accuracy in ATP evaluation is obtained through such an approximation method. In addition, system properties, such as monotonicity and sensitivity, i.e. bottlenecks, are investigated. Finally, a case study at an automotive powertrain assembly plant is introduced to illustrate the applicability of the method and the effectiveness for assembly time reduction through using collaborative robots.

Emergency preparedness for tunnel fires – A systems-oriented approach

Efficient emergency response is key to preventing major losses in tunnel fires. Our general concern in this paper is the degree to which tunnel systems are prepared and the means by which we can be prepared for a major fire in a single-tube road tunnel. Conformance to prescriptive regulations dominates existing practice in the area of emergency preparedness. Risk-based approaches exist but have little influence on emergency preparedness designs for tunnel systems. A successful emergency response to tunnel fires is dependent on many actors collaborating under serious time constraints. Safety becomes a matter of controlling critical processes necessary to keep the system in a safe state. Efficient decision-making in situations of major uncertainty is vital, to achieve safety goals. This essentially means that efficient emergency preparedness for road tunnels is a matter that needs attention in the early design phases and continuous improvements during the operational phase. To achieve high-performance emergency preparedness against tunnel fires, there is a need for radical changes to the design and operation of tunnels. In this paper, it is claimed that a system-theoretic approach is appropriate to deal with the tunnel system’s complexity and to drive the design of appropriate control structures for critical processes, from the design phase to the actual emergency. It is shown how system theoretic approaches will change the safety management practices for tunnels and how this will increase consistency between potential fire scenarios and associated control actions.

A systems-theoretic approach for two-stage emergency risk analysis

Coronavirus disease (COVID-19) is an infectious disease that has dramatically spread worldwide. Regarding the safety issues of industries, there is a requirement of dealing with the emergency risk in the period of urgent situations. In this work, we proposed a systems-theoretic approach of the two-stage emergency risk analysis (ERA) based on the systems theory, that is the System-Theoretic Accident Model and Processes (STAMP). The two-stage ERA includes the normal to emergency risk analysis (N2E-RA) and emergency to normal risk analysis (E2N-RA). Besides N2E-RA, we advocate that E2N-RA is also an important and indispensable part of ERA. We elaborated the characteristics of N2E-RA and E2N-RA, separately. Eventually, based on our analysis, we provided recommendations for decision makers in preventing and controlling industrial accidents in the period of COVID-19.

Chester Barnard’s systems-theoretic approach to organisation theory: a reconstruction

Chester Barnard’s organisation theory is widely acknowledged to be grounded on a systems-theoretic approach which has however remained largely inchoate. The present paper ventures a hypothetical reconstruction of this approach by identifying the salient parallels between Barnard’s thought and Niklas Luhmann’s theory of social systems as well as Alfred North Whitehead’s philosophy of organism. While these parallels are by no means perfect, Barnard seems to have anticipated the Luhmannian view of organisations as complexity-reducing systems navigating a precarious outer environment. Drawing on Whitehead, Barnard argued that organisations may succeed with this task insofar as they operate as organismic wholes.

A system-theoretic approach to safety and security co-analysis of autonomous ships

The autonomous ship carrying valuable cargoes and passengers in a more effective and cost-saving manner will soon be state of the art technology, which most likely shall be introduced into the public horizon as the remote control mode within the foreseeable future. The highly connected intelligent systems though come at the cost of the increased system vulnerability to cyber-attacks. To smooth this innovative system can be released into actual context of operation, a novel STPA-based methodology is proposed that synthesizes safety and security, namely STPA-SynSS. In the novel method, a comprehensive process to identifying hazards and revealing causal factors is provided, hazard elimination/mitigation strategies are implemented into system design via system safety and security requirements, so that hazards can be continually tracked and closed-loop managed. The insight regards the operations of the method was demonstrated in a remotely-controlled ship with seafarers onboard, the analysis process focused on encountering ship-ship collision accidents and related security incidents. Results indicate that generated inadvertent/intentional causal factors and developed elimination/mitigation strategies can assist the processes of design and operational planning of the autonomous ships and its shore control centre. Further, the proposed method of this paper also has general relevance for other intelligent systems.

Surveying work-as-done in post-operative delirium risk factors collection and diagnosis monitoring

Post-operative delirium (POD) is a health hazard condition for the patients and it is associated with increased costs for the healthcare system. Following a system-theoretic approach, firstly a model, then a questionnaire, have been designed to probe the collective awareness about POD throughout the entire patient's perioperative pathway. The 58 reported answers pointed out that most of the information, specifically associated with POD, are routinely recorded but not used to stratify the patients' individual risk to develop POD. The results suggest the need for design a new socio-technical role within modern health care systems: the POD analyst. A Systems-Theoretic Accident Model and Processes (STAMP) model is proposed both to propel the awareness about POD and as a template for future POD risk factors collections.

Interpreting models of infectious diseases in terms of integral input-to-state stability

This paper aims to develop a system-theoretic approach to ordinary differential equations which deterministically describe dynamics of prevalence of epidemics. The equations are treated as interconnections in which component systems are connected by signals. The notions of integral input-to-state stability (iISS) and input-to-state stability (ISS) have been effective in addressing nonlinearities globally without domain restrictions in analysis and design of control systems. They provide useful tools of module-based methods integrating characteristics of component systems. This paper expresses fundamental properties of models of infectious diseases and vaccination through the language of iISS and ISS of components and whole systems. The systematic treatment is expected to facilitate development of effective schemes of controlling the disease spread via non-conventional Lyapunov functions.