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The Ability to Regulate, Govern and Control Financial Services Systems
Published in Gunilla SundstrÖm, Erik Hollnagel, Governance and Control of Financial Systems, 2018
Gunilla Sundström, Erik Hollnagel
The concept of state variables is used in control theory to denote variables that can be used to predict and anticipate a system’s future states (cf. Åström and Murray, 2009). In order for the three state variables shown in Figure 10.1 to be used for a shared view of how the Financial Services system can be monitored, they must first be developed into a set of Key Performance Indicators that support a state identification. Figure 10.1 also suggests that external events can impact the Financial Services system directly and force the regulatory system to a reactive response. Ideally, such events should be anticipated by the management system, thus enabling the regulatory system to be proactive and prepare the Financial Services system for the impact of the negative events.
Learning How to Create Resilience in Business Systems
Published in Erik Hollnagel, David D. Woods, Nancy Leveson, Resilience Engineering, 2017
Gunilla Sundström, Erik Hollnagel
A major contribution of control theory and systems engineering (e.g., Sheridan, 1992) has been the introduction of key concepts that can be used across many types of systems to describe system component types including control system components. Adopting a control theoretic and system engineering flavoured approach, a business system can be described as a state machine characterised by a set of variables and parameters. Business goals are associated with desired state values of the business system and the state machine describes how the various transitions among states can take place. For example, defining the desired state of profitable operation in terms of profitability (e.g., that profit margins need to be higher than 30%) and shareholder value, would be a first step towards articulating business goals. The management process of a business system is defined as the control system whose role it is to ensure that the business system meets or exceeds performance objectives. The behaviour of the control system is driven by the defined business goals, such as profitability, shareholder value and customer equity. The business actuators are the means used to change one or more state variables. Sensors provide information about the state variables, either by direct measurement or by some method of estimation. This control system’s view of a complex networked business is illustrated in Figure 15.1, adapted from Sundström & Deacon (2002).
Digital Filtering
Published in David C. Swanson, ®, 2011
In this section, we first describe the two most fundamental types of digital filter: the finite impulse response (FIR) and infinite impulse response (IIR) filters. Using results from system theory, FIR and IIR filter design techniques are presented with emphasis on physical applications to real-world filtering problems. Of particular importance are the techniques for insuring stable IIR filters and the effects of delay on the digital system parameters. Digital systems can also be designed based on state variables and this relationship to IIR filtering is presented. Generally, state variables are used when the information of interest is directly seen from the system state equations (position, velocity, acceleration, etc.). For processing two-dimensional (2D) data such as images or graphs, 2D FIR filters are presented. Image processing using 2D FIR filters (often referred to as convolution kernels) allows blurred or out-of-focus images to be sharpened. Finally, a set of popular applications of digital filters are given. Throughout the rest of this book, we will relate the more advanced adaptive processing techniques to some of these basic families of applications.
Efficiency of risk management for tunnel security of megaprojects construction in China based on system dynamics
Published in Journal of Asian Architecture and Building Engineering, 2023
Kai Liu, Yuming Liu, Yuanyuan Kou, Xiaoxu Yang, Guangzhong Hu
The study of system dynamics focuses on the behavioural patterns and characteristics of the system. Based on the internal information feedback control of the system, computer techniques are used to seek the dynamic correlations between the system structure and behaviour patterns with the help of model simulation analysis. The system dynamics model consists mainly composed of system variables, including state variables, rate variables, auxiliary variables, and constants. The causal relations and feedback mechanisms between the variables are determined by constructing equations. The construction of the SD model is an important component to complete the analysis of the security risk management in the tunnel of the megaproject. The main specific steps are shown in Figure 2, including (1) clarify the purpose of modeling. (2) define the system boundary. (3) determine the feedback relationship. (4) design the equations of variables. (5) run the model simulation.
Tracking control for a class of uncertain complex dynamical networks with outgoing links dynamics
Published in International Journal of Control, 2023
Peitao Gao, Yinhe Wang, Juanxia Zhao, LiLi Zhang, Shengping Li
(i) Assumption 2.2 is mainly inspired by some practical engineering systems. For example, in the two-links robots, and represent the position angle of the ith two-links robot and the angular velocity of the ith two-links robot, respectively. Their state variables values can be measured by sensors. (ii) In the dynamic continuous structural system, and denote the vibration displacement and the displacement velocity of the ith element, respectively. Their state variables values can also be measured by sensors, . (iii) If is continuous in a bounded closed set , then the function in Assumption 2.1 can be chosen as , in which and denotes the Euclidean norm of matrix ‘’, .
Stability analysis and synthesis of discrete-time semi-Markov jump singular systems
Published in International Journal of Control, 2023
Many practical (control) systems can be described perfectly by state variables and mathematical equations, wherein state variables have been used to present the main constituent elements of the system under study, and mathematical equations characterise mutual relationships of those state variables. Generally, mathematical equations usually are either differential equations or algebraic equations. Thus, in 1973, Singh and Liu first proposed the concept of singular systems, which are formed by differential equations and algebraic equations. So far, in many literatures, singular systems are also termed differential-algebraic systems or descriptor variable systems correspondingly. Singular systems, as a generalisation of normal differential systems are found in myriad dynamic processes, such as biology, economics, computer science and engineering, etc. and have exhibited many unusual properties which fail to associate with classic differential systems. In addition, several dynamic properties cannot be easily extended from standard differential systems to the differential-algebraic systems in a trivial way, which induces the research on differential-algebraic systems much harder. Singular systems, therefore, have naturally aroused extensive concerns from many experts and scholars. (see Dai, 1989; Kumaresan & Balasubramaniam, 2008; Migallon & Penades, 1996; Singh & Liu, 1973; Wang et al., 2015; Wu et al., 2013; Xu & Lam, 2006; Xu & Yang, 1999 and references therein).