Set Of Safe States Research Articles

ENTROPY APPROACH IN SYSTEM RESEARCH OF DIFFERENT COMPLEXITY OBJECTS TO ASSESS THEIR CONDITION AND FUNCTIONALITY

Context. Consideration of comprehensively studied object in the form “system – environment” to obtain an approximate accurate real situation reflection.
 Objective. Search for solutions to problematic research issues based on the entropy approach for systems “object – environment” of different nature and complexity, studying them and obtaining knowledge (stable information) and providing them as a set of complex system tasks modulated by different entropy functions.
 Method. The following criteria are used to assess the sustainability of the development of a system object: integrity – the failure of the trajectory of development of the object at a certain forecast time interval from a set of safe states; monotony of growth of indicators of development of object on a certain time interval with the subsequent preservation of them in the set intervals of admissible values; compliance of the development trajectory with the target changes according to the requirements of safety and sustainable development, resistance to disturbance, including asymptotic stability of the program trajectory and structural stability of the system.
 In the conditions of nonlinear development of events and spontaneity of processes “object – external systems” at stable structure of system object of research it is expedient to apply the entropic approach and knowledge from the field of the theory of stability developed for technical and cybernetic systems.
 Results. The proposed entropy approach to analysis is determined by the fact that the object is characterized from the standpoint of compliance with acceptable regulatory constraints and processes regarding the acceptability of the object of the external environment or the possibility of resolving the situation of coexistence “object – environment”.
 Within the analysis of a system object, this means that for both stationary and dynamic conditions, their state is described by a certain function, the changes of which indicate the approach to a certain point of homeostatic relations with the environment.
 The practical application of the provided methodological proposal for finding solutions in conditions of uncertainty of a certain kind is considered on the example of determining measures to influence the course of positive development of the child’s body in the situation of diagnosis of cerebral palsy in the form of information and software application at realization of the appointments of medical character applied to them (factors of influence of emergency).
 Conclusions. The proposed entropy approach to the choice of decision-making problems for determining the state and changes as a result of process transformations in system objects of the type “studied system – environment” in conditions of uncertainty does not require additional conditions characteristic of known estimates by criteria in common mathematical means of decision making.

Safety control, a quantitative approach

Safety control consists in maintaining the state of a given system inside a specified set of safe states. Traditionally, the problem is tackled using set-theoretic methods, which are mostly qualitative: states are partitioned between safety-controllable (i.e. states that belong to the maximal controlled invariant subset of the safe set) and safety-uncontrollable states. In this paper, we present a quantitative approach to safety controller synthesis. Our approach makes it possible to compute a measure of safety, which quantifies how far from the unsafe set (respectively, how close to the safe set) one can stay when starting from a given controllable (respectively, uncontrollable) state. For finite transition systems, such a measure can be computed in finite-time using a functional fixed-point iteration. In addition, we show that the level sets of the functional fixed-point coincide with the maximal controlled invariant subsets of a parameterized family of sets and that one can synthesize a common safety controller for all the sets of the family. In the second part of the paper, we show how the approach can be used in the framework of abstraction-based synthesis to lift these results to infinite transition systems with finite abstractions. To illustrate the effectiveness of the approach, we show an application of the approach to a simple boost DC-DC converter.

Detection of Safe Operating Regions: A Novel Dynamic Process Simulator Based Predictive Alarm Management Approach

The operation of complex production processes is one of the most important research and development problems in process engineering. A safety instrumented system (SIS) performs specified functions to achieve or maintain a safe state of the process when unacceptable or dangerous process conditions are detected. A logic solver is required to receive the sensor input signal(s), to make appropriate decisions based on the nature of the signal(s), and to change its outputs according to user-defined logic. The change of the logic solver output(s) results in the final element(s) taking action on the process (e.g., closing a valve) to bring it back to a safe state. Alarm management is a powerful tool to support the operators’ work to control the process in safe operating regions and to detect process malfunctions. Predictive alarm management (PAM) systems should be able not only to detect a dangerous situation early enough, but also to give advice to process operators which safety action (or safety element(s)) must be applied. The aim of this paper is to develop a novel methodology to support the operators how to make necessary adjustments in operating variables at the proper time. The essential of the proposed methodology is the simulation of the effect of safety elements over a prediction horizon. Since different manipulations have different time demand to avoid the evolution of the unsafe situation (safety time), the process operators should know which safety action(s) should be taken at a given time. For this purpose a method for model based predictive stability analysis has been worked out based on Lyapunov’s stability analysis of simulated state trajectories. The proposed algorithm can be applied to explore the stable and unstable operating regimes of a process (set of safe states), information that can be used for PAM. The developed methodology has been applied to two industrial benchmark problems related to the thermal runaway.