A hierarchical hybrid system model and its simulation

Abstract

This paper presents a hierarchical hybrid system modeling and simulation framework using the Ptolemy I1 environment. Ptolemy I1 is a system-level design tool that supports the integration of multiple models of computation. The modeling of hierarchical hybrid systems is achieved by combining continuous-time models with finite state automata. Breakpoint handling, event detection and invariant monitoring techniques are studied. A hybrid helicopter control system is simulated as an example.

Hierarchical Cascade Control Systems for Time-Dependent Dynamical Plants as Applied to Magnetic Plasma Control in D-Shaped Tokamaks

“…The combination of robust and adaptive methods using identification methods and artificial intelligence is most effective for solving the problems of controlling dynamic plants with variable parameters over a limited time interval. There are only three known monographs on the theory and practice of hierarchical automatic control systems [47][48][49], as well as journal articles, for example [50], and conference papers, in particular [45,51]. It is impossible to construct a unified structure for a hierarchical control system because of the possibility of an almost infinite combination of different control algorithms at different levels of the hierarchy.…”

“…It is impossible to construct a unified structure for a hierarchical control system because of the possibility of an almost infinite combination of different control algorithms at different levels of the hierarchy. For this reason, in works on hierarchical control systems, various structural schemes of hierarchical systems are constructed based on applied control tasks and desired control objectives at different levels [45][46][47][48][49][50][51]. The same approach is applied in this work to generalize the structural systems of hierarchical magnetic plasma control in Tokamaks.…”

Hybrid systems modeling for critical infrastructures interdependency analysis

“…We consider the components of interdependent infrastructures as hybrid systems, and we model each component with an open hybrid automaton. In this paper we adopt the open hybrid automaton framework from [41,42] which is the following collection: where q ∈ Q denotes the discrete state and x ∈ X ⊆ R n denotes the continuous state. We let V be a finite set of input variables, which we partition into two subsets V = U ∪Υ, where v ∈ U denotes an internal dependency input andv ∈ Υ denotes an external dependency input.…”

“…This can be achieved with the composition operation of open hybrid automata [42,43,44]. In general, the composition of two open hybrid automata models consists, roughly, the connection between some of the inputs/outputs (I/O) of one hybrid automaton with some O/I of another, while the remaining I/O become the I/O of the newly composed hybrid automaton [41].…”

Role of reinforcement learning for risk‐based robust control of cyber‐physical energy systems

“…Essential features of a CPS-E include (Alur, 2015): (1) reactive and real-time computation-continuous interaction with the environment through inputs/outputs including timing delays and timing-dependent coordination protocols, (2) concurrency-multithreaded information execution and parallel information exchange to meet compute goals, (3) feedback control-measurement via sensors and influence via actuators, and (4) safety-critical operations-assurance for detecting errors and ensuring reliable operations. Based on these system properties and from a safe and secure operability/control standpoint, a critical emerging research challenge is the seamless coupling of closed-loop dynamics with discrete actions of networked components especially under hazard and information uncertainty (Liu et al, 1999;Lygeros et al, 2008;Cardenas et al, 2009;Kim et al, 2013).…”