Reachability analysis in stochastic directed graphs by reinforcement learning

We characterize the reachability probabilities in stochastic directed graphs by means of reinforcement learning methods. In particular, we show that the dynamics of the transition probabilities in a stochastic digraph can be modeled via a difference inclusion, which, in turn, can be interpreted as a Markov decision process. Using the latter framework, we offer a methodology to design reward functions to provide upper and lower bounds on the reachability probabilities of a set of nodes for stochastic digraphs. The effectiveness of the proposed technique is demonstrated by application to the diffusion of epidemic diseases over time-varying contact networks generated by the proximity patterns of mobile agents

Design of controllers for hybrid linear systems with impulsive inputs and periodic jumps

In this study, the problem of designing a controller for a hybrid system with impulsive input and periodic jumps is addressed. In particular, it is shown that any hybrid system with impulsive inputs and periodic jumps can be recast into a discrete-time, linear, time-invariant system, which, in turn, can be used to design a controller by using classical methods. Furthermore, it is shown that, once such a controller has been designed, it can be readily used to control the hybrid system by mean of an interfacing system that is based just on the continuous-time dynamics of the plant to be controlled. Several examples, spanning from aerospace to biomedical applications, are reported in order to corroborate the theoretical results

Citokininek, a növényi in vitro fejlődés kulcsregulátorai

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Overview of the FTU results

Since the 2016 IAEA Fusion Energy Conference, FTU operations have been mainly devoted to experiments on runaway electrons and investigations into a tin liquid limiter; other experiments have involved studies of elongated plasmas and dust. The tearing mode onset in the high density regime has been studied by means of the linear resistive code MARS, and the highly collisional regimes have been investigated. New diagnostics, such as a runaway electron imaging spectroscopy system for in-flight runaway studies and a triple Cherenkov probe for the measurement of escaping electrons, have been successfully installed and tested, and new capabilities of the collective Thomson scattering and the laser induced breakdown spectroscopy diagnostics have been explored

An iterative data-driven linear quadratic method to solve nonlinear discrete-time tracking problems

The objective of this note is to introduce a novel data-driven iterative linear quadratic control method for solving a class of nonlinear optimal tracking problems. Specifically, an algorithm is proposed to approximate the Q-factors arising from linear quadratic stochastic optimal tracking problems. This algorithm is then coupled with iterative linear quadratic methods for determining local solutions to nonlinear optimal tracking problems in a purely data-driven setting. Simulation results highlight the potential of this method for field applications

Local sliding mode inversion algorithms and state observers with space applications

The main objective of this work is to propose novel finite-time algorithms to either invert a time-varying map or to estimate the state of a time-varying nonlinear plant. The former goal is pursued by using a sliding mode version of the Newton algorithm, whereas the latter objective is achieved by resorting to the inverse of the Jacobian matrix of the observability map to implement a sliding mode differentiator in the original coordinates. Finally, it is shown how these two techniques can be efficiently used to solve some relevant space applications, such as the estimation of the angular velocity of an axial symmetric satellite, the estimation of the inertia parameters of an orbiting object from measurements of its angular velocities, and the estimation of a Keplerian orbital element given measurements of the other five ones