Final LDRD report human interaction with complex systems: advances in hybrid reachability and control.

Computer systems are becoming increasingly complex, especially interactive software systems, namely software user interfaces. The scientific community relies on different methods to assess their safety. This article provides an updated literature survey on hazard analysis approaches used to improve the safety of complex systems. To support the survey, we conceptualise complex systems, highlighting the challenge in terms of assessing their safety. We provide a brief overview on the approaches historically available to tackle issues in those systems, along with their most common methods. Finally, the article focuses in one method of a non-traditional approach, which is described in more details, along with some of its extensions, which seeks to improve the hazard analysis in complex systems.

With the creation of unmanned systems the development of ergonomics has entered a new stage in studying human-machine interaction and exacerbated safety issues. The requirements for safety, resistance to failures and reliability of the systems and their main connection – the human operator, were changed. New challenges – new human problems in digital systems. Digitization has made it possible to create unmanned systems, but it has also created an ergonomic contradiction between intentions and results. Its essence is that, in contrast to the expected increase in efficiency and safety, the increase in unmanned systems automation leads to an increase in the risks associated with human influence. Unmanned aerial systems have unique ergonomic challenges related to the specifics of the UAV operator and the technical features of the UAVs themselves as complex systems. In this regard, it is relevant to analyze the reasons for the continuing manifestations of the human factor phenomenon in the functioning of unmanned systems and the search for ways to reduce them. The purpose is to study the human factor problem in complex systems that use information and communication technologies, using the example of unmanned aircraft systems. The article takes a critical look at the human factor issues in unmanned aviation that are not being addressed by existing approaches. An iceberg model of change management in the safety of complex systems is proposed. It is shown that the human factor in unmanned systems arises and severely decreases because of the nontransdisciplinary nature of the education of modern engineers and operators. And the essential role in overcoming this problem is knowledge aimed at understanding the capabilities and limitations of a person as the main link of the system, which then makes decisions in the management process. It is shown that it is necessary to pay considerable attention to the development of ergonomic thinking among everyone whose activities relate to the life cycle of a complex system. That is, it is the transdisciplinarity of the education of specialists that allows for reducing the specified risks of human-machine interaction.

The demand for risk and safety analysis is becoming greater due to the increased complexity of modern systems such as autonomous ships. Safety is one of the main motivations behind the efforts of multiple organizations to develop autonomous ships. The adoption of an early system-theoretic approach to safety such as System Theoretic Process Analysis (STPA) is an effective way to integrate safety in complex systems. Furthermore, many authors have urged the use of this method to analyse the risks of autonomous vessels in the development phases. Applying STPA requires a description of the system to model the hierarchical control structure and conduct the analysis. At this stage, the functional description of autonomous ships remains limited, which poses a challenge in building the hierarchical control structure. This paper proposes a framework for developing a hierarchical control structure of an autonomous ship. The framework is founded on the principles of the STPA control structure and its five main elements. It makes use of the current shipping operation system, the available information about autonomous ships and the experience of seafarers executing diverse tasks on-board conventional ships. The application of the framework showed that the information provided by the seafarers is essential in developing an initial functional description of an autonomous ship. Furthermore, the results revealed that in addition to the technical aspects of autonomous ships, introducing these vessels into the organizational control structure of current maritime operation also poses challenges that need to be addressed in the earliest design phase.

Health status assessment is crucial for ensuring the reliability and safety of complex systems. Belief rule bases (BRBs) are widely used in complex system modeling because of it advanced causal reasoning capabilities. However, traditional BRB-based assessment models often rely on fixed triangular membership functions (MFs), which limit their flexibility and inference performance. Thus, a new adaptive interval type II belief rule base (AII-BRB) is proposed. Firstly, a shape coefficient of the MF is defined, which can effectively adjust its shape according to the observed measurement data. Based on this, an adaptive interval Type II MF is constructed. Thus, the reasoning process of the BRB model changes from quantitative value reasoning to interval probability reasoning. Furthermore, the basic performance of the AII-BRB is explored to illustrate its rationality. AII-BRB model not only provides point estimate of the health status, but also provides the interval range of health status. Finally, the health status assessments of spacecraft flywheel systems and lithium batteries are used as case studies to validate the effectiveness of the AII-BRB method.

Managers’ conceptions regarding human factors in air traffic management and in airport operations

Risk management and improve the reliability of the process, are the issues which have become more important in production and operations management literature. Risk assessment is an important tool in risk management to reduce project risks and achieve sustainable development. At present the risk assessment is concerned in planning and policy-making in most of the world countries. There are several techniques for identifying hazards and assessing risks. One of the most important of these techniques is Failure Modes and Effects Analysis (FMEA). FMEA is an efficient tool for the identification of potential failure modes and their effects in order to increase the reliability and safety of complex systems. Also this technique is useful to gather data needed for decision making and risk control. In fact, the purpose of this technique is: a. to identify failure modes and their effects; b. to specify the corrective actions to eliminate or reduce the probability of failure and ultimately c. development of efficient maintenance system to reduce the occurrence of potential scenarios. In this study, several other studies have investigated and tried to explore a range of its benefits and uses and also the method of risks computation using this technique is presented.

The Mean Time to Failure (MTTF) is a critical metric for assessing the reliability of non-repairable systems, and it plays a significant role in incident management. However, accurately estimating MTTF can be challenging due to the limited availability and quality of historical experimental data on a group of identical parts or systems. To address this challenge, we propose an adaptive surrogate modeling method, which approximate the system’s performance functions with a more computationally efficient model to predict the MTTF during the design stage. In this paper, we develop an adaptive Gaussian process (GP) approach that combines with Monte Carlo simulation (MCS) to predict MTTF. The proposed method initially trains a GP model for the system’s performance function, and then an MTTF surrogate model can be obtained. With a learning function, these models are updated dynamically based on new information as it becomes available. We demonstrate the effectiveness of our method on series system, parallel system, and mixed system with two examples, and the results show that the adaptive surrogate modeling method can accurately predict the MTTF of the system. Our method has the potential to improve the reliability and safety of complex system and reduces the need for costly performance testing.

Broadband Active Noise Control Design through Nonsmooth H∞ Synthesis

Motion planning of automated vehicles requires dynamical models to ensure that obtained trajectories are drivable. An often overlooked aspect is that motion planning is usually done using simplified models, which do not always sufficiently conform to the real behavior of vehicles. Thus, collision avoidance and drivability is not necessarily ensured. We address this problem by modeling vehicles as differential inclusions composed of simple dynamics and set-based uncertainty; conformance testing is used to determine the required uncertainty. To quickly provide the set of solutions for these uncertain models, we use pre-computed reachable sets (i.e., the union of all possible solutions) for pre-selected motion primitives. The reachable sets of vehicles are obtained through the novel combination of optimization techniques and reachability analysis in the controller synthesis — they enable us to guarantee safety by checking their mutual non-intersection for consecutive time intervals. The benefits of our approach are demonstrated by numerical experiments.

This document is intended to contribute to the area of validation and verification of communicating real time systems, with emphasis put on parametric reachability analysis of systems modeled using timed automata.Reachability analysis is a crucial aspect of validation and verification of software and hardware systems. The reachability analysis for real time systems is area that is studied by many researchers in academic and industrial communities. However, not much work has been done for systems, where time constraints are expressed using parameters. This is serious disproportion with real world, where specifications of most of the communication protocols or embedded software and hardware systems are indeed parameterized.This thesis presents a complete framework for forward and backward parametric reachability analysis. The solution presented here can be used as a base of algorithms for validation and verification of software and hardware real-time systems, modeled as timed automata with parameters. The results of the thesis can be easily applied to model checking or test generation tools and algorithms.The core idea of the thesis is a concept of Extended Difference Bound Matrix (EDBM). This is a data structure that stores relations between all system"s clocks and parameters. In contrast to Parametric DBM, that is the state-of-the-art data structure for parametric analysis, EDBM does not require storing constraints on clocks and constraints on parameters separately. This leads to significant benefits regarding memory consumption and time necessary to perform basic operations for symbolic analysis.The maturity of the solution was proven by implementation of a proof-of-concept tool and by experiments performed with modern communication protocol. The results show that even complex systems can be efficiently handled by the framework.

The nonlinear flight dynamic model of a hybrid regional aircraft is being developed in Simulink for integration into the Open Digital Platform of the European project ODE4HERA. The project aims to accelerate the development of hybrid regional architectures, which are far more complex systems than the in-service airplanes. The scope of this work is to develop the pilot behavior model in a typical flight mission. This is well captured with the Total Energy Control System technique, which simultaneously acts on throttle and elevator controls. The application case aims to verify the integration of on-board systems specifically designed for hybrid electric aircraft. In detail, the flight mechanics model is integrated with the hybrid propulsion system, electric distribution, and power sources. From this perspective, the main role of flight simulation is to demonstrate whether the proposed architecture can cover a typical mission profile expected for regional transport airplanes.

In this paper we present a logical characterization, by means of ACTL formulae, of safety requirements to be formally verified over safety critical complex systems. In this class of systems the formal verification of requirements is often hardened by state explosion problems. To deal with this problem, the characterization we propose allows the satisfiability of a safety requirement over a complex system to be derived by its satisfiability over those component subsystems that are directly involved in the given requirement. The proposed methodology has been successfully used for the formal verification of safety requirements of a particular system, that is a railway computer based signalling control system.

Multi-objective control synthesis: an application to 4WS passenger vehicles

Human reliability analysis: A critique and review for managers

Time-sensitive safety-critical systems store traces as a collection of time-stamped messages that are generated while a system is operating. Analysis of these traces becomes a key task as it allows one to find faults or errors within a system that is otherwise difficult to discern, especially in complex systems. Furthermore, finding any form of anomalous behaviour becomes critical in time-sensitive and safety-critical systems where a late detection will often lead to dire consequences. Most available approaches are generally used in networking or business process analysis. We focus on creating a lightweight and explainable approach for time-sensitive safety-critical systems.By using a set of system traces under both normal and anomalous conditions, our approach attempts to classify whether or not a trace is anomalous. In this work, we introduce MA2DF, Multi-Agent Anomaly Detection Framework, a novel multi-agent based graph design approach for online and offline anomaly detection in system traces. Our approach takes advantage of the timing information between a sequence of events and also the event sequences to learn and discern between normal and anomalous traces. We present two approaches, an offline approach to discern anomalous behaviour by utilizing the event occurrence workflow graph. The second approach is an online streaming algorithm that monitors the sequence of events as they arrive in real-time. This can be used to detect anomalies, find the cause, and improve system resilience. We show how our approach, MA2DF, is superior to other state-of-the-art models. The paper will explore the technical feasibility and viability of MA2DF by utilizing industry strength case study using traces from a field-tested hexacopter.