1,720,976 research outputs found
Solution of Ambrosio-Tortorelli model for image segmentation by generalized relaxation method
No full textImage segmentation addresses the problem to partition a given image into its constituent objects and then to identify the boundaries of the objects. This problem can be formulated in terms of a variational model aimed to find optimal approximations of a bounded function by piecewise-smooth functions, minimizing a given functional. The corresponding Euler-Lagrange equations are a set of two coupled elliptic partial differential equations with varying coefficients. Numerical solution of the above system often relies on alternating minimization techniques involving descent methods coupled with explicit or semi-implicit finite-difference discretization schemes, which are slowly convergent and poorly scalable with respect to image size. In this work we focus on generalized relaxation methods also coupled with multigrid linear solvers, when a finite-difference discretization is applied to the Euler-Lagrange equations of Ambrosio-Tortorelli model. We show that non-linear Gauss-Seidel, accelerated by inner linear iterations, is an effective method for large-scale image analysis as those arising from high-throughput screening platforms for stem cells targeted differentiation, where one of the main goal is segmentation of thousand of images to analyze cell colonies morphology
Obstacle Avoidance via Landmark Clustering in a Path-Planning Algorithm
No full textIn this paper we present a new 2D decentralized path-planning algorithm for a swarm of multi-rotor UAVs operating in an unknown environment. The way-points of the reference trajectories are computed as solutions of a sequence of optimization problems. In order to obtain coordination, the optimization problems are defined by considering the goal of the flight mission, the desired formation shape and the detected obstacles. The obstacle avoidance strategy is based on the clustering of the detected landmarks. Then the no-fly zones are obtained by fitting the minimum area rectangle boxes surrounding the clusters. The algorithm is tested through simulations of realistic scenarios
Conditions for annular finite-time stability of Itô stochastic linear time-varing systems with Markov switching
No full textIn this study, the authors tackle some control problems related to the class of continuous-time, stochastic linear time-varying systems with Markov switching. First, the annular stochastic finite-time stability problem is considered, and two sufficient conditions are derived by considering the Itô formalism. Both conditions require the solution of a feasibility problem based on differential linear matrix inequalities. The former turns out to be less conservative and, therefore, is exploited in the analysis context; however, it cannot be converted into a computationally tractable condition for feedback purposes. The latter, which is based on a more conservative assumption, allows us to solve the state-feedback design problem. They show that the proposed approach obtains less conservative results with respect to the previous literature. Moreover, the application of the methodology to the finite-time control of a satellite illustrates the effectiveness of the proposed approach when facing engineering problems
An Observer-Based Output Feedback Controller for the Finite-Time Stabilization of Markov Jump Linear Systems
No full textIn this letter, we investigate the finite-time output
feedback control problem for continuous-time Markov
jump linear systems. In this context, the first result is
a sufficient condition for stochastic finite-time stability,
requiring the solution of a feasibility problem constrained
by differential linear matrix inequalities. Afterward, we
consider the stabilization problem via output feedback
dynamical controllers. The usual machinery pursued in the
deterministic case would lead to stabilization conditions
depending on differential bilinear matrix inequalities, that
cannot be solved in practice. Therefore, a different methodology,
based on the separation approach provided by
Amato et al., is exploited to design an observer-based output
feedback controller, which can be computed by solving
an optimization problem depending on linear constraints.
A non-trivial application example, involving the finite-time
stabilization of the longitudinal dynamics of a helicopter,
is presented in order to illustrate the effectiveness of the
proposed technique
Development of an autonomous multi-rotor UAV for outdoor missions in unknown environments
No full textIn this paper we present the development of a multi-rotor system for autonomous outdoor flights in an unknown environment. We propose a modular framework scheme to perform the functions of guidance, navigation and control using the sensor measurements. The localization and mapping tasks are performed simultaneously by the guidance module through an Extended Kalman Filter (EKF). The estimated map allows the guidance module to plan the reference trajectory avoiding the collision by evaluating the solutions of a sequence of constrained optimization problems. The control module computes the autopilot commands to follow the reference trajectory through a robust Model Predictive Control technique. The overall system is tested through simulations of realist scenarios
On the Numerical Solution of Differential Linear Matrix Inequalities
No full textThis paper presents a novel approach for the numerical solution of differential linear matrix inequalities. The solutions are searched in the class of piecewise-quadratic functions with symmetric matrix coefficients to be determined. To limit the numbers of unknowns, congruence constraints are considered to guarantee continuity of the solution and of its derivative. In Example section, some control problems involving differential linear matrix inequalities are considered and solved in order to compare the proposed approach with alternative approximation methods adopted in the literature
A hybrid model-based and learning-based approach for the plasma magnetic control in DEMO
No full textNew conditions for finite‐time stability of impulsive dynamical systems via piecewise quadratic functions
No full textIn this paper, the use of time-varying piecewise quadratic functions is investigated to characterize the finite-time stability of state-dependent impulsive dynamical linear systems. Finite-time stability defines the behavior of a dynamic system over a bounded time interval. More precisely, a system is said to be finite-time stable if, given a set of initial conditions, its state vector does not exit a predefined domain for a certain finite interval of time. This paper presents new sufficient conditions for finite-time stability based on time-varying piecewise quadratic functions. These conditions can be reformulated as a set of Linear Matrix Inequalities that can be efficiently solved through convex optimization solvers. Different numerical analysis are included in order to prove that the presented conditions are able to improve the results presented so far in the literature.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
A Modular Approach based on a Deep Reinforcement Learning Technique for the Plasma Magnetic Control in DEMO
No full textIn this paper we propose a modular approach, based on a deep reinforcement learning technique, for the control of a plasma with a limited configuration in the DEMO tokamak. Three different reinforcement learning agents are used to perform the magnetic confinement of the plasma, i.e. to stabilize the vertical plasma instability, to control the radial centroid position, and to ramp-up the plasma current. This modular approach allows us to simplify the training procedure of the control policy, since it requires a lower overall computational load. Performance of the proposed approach are characterized by numerical simulations
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