Analysis and synthesis of attractive quantum Markovian dynamics

We propose a general framework for investigating a large class of stabilization problems in Markovian quantum systems. Building on the notions of invariant and attractive quantum subsystem, we characterize attractive subspaces by exploring the structure of the invariant sets for the dynamics. Our general analysis results are exploited to assess the ability of open-loop Hamiltonian and output-feedback control strategies to synthesize Markovian generators which stabilize a target subsystem, subspace, or pure state. In particular, we provide an algebraic characterization of the manifold of stabilizable pure states in arbitrary finite-dimensional Markovian systems, that leads to a constructive strategy for designing the relevant controllers. Implications for stabilization of entangled pure states are addressed by example.     

Linear offset-free Model Predictive Control

This work addresses the problem of offset-free Model Predictive Control (MPC) when tracking an asymptotically constant reference. In the first part, compact and intuitive conditions for offset-free MPC control are introduced by using the arguments of the internal model principle. In the second part, we study the case where the number of measured variables is larger than the number of tracked variables. The plant model is augmented only by as many states as there are tracked variables, and an algorithm which guarantees offset-free tracking is presented. In the last part, offset-free tracking properties for special implementations of MPC schemes are briefly discussed.     

Free vibration analysis of functionally graded conical, cylindrical shell and annular plate structures with a four-parameter power-law distribution

Based on the First-order Shear Deformation Theory (FSDT) this paper focuses on the dynamic behavior of moderately thick functionally graded conical, cylindrical shells and annular plates. The last two structures are obtained as special cases of the conical shell formulation. The treatment is developed within the theory of linear elasticity, when materials are assumed to be isotropic and inhomogeneous through the thickness direction. The two-constituent functionally graded shell consists of ceramic and metal. These constituents are graded through the thickness, from one surface of the shell to the other. A generalization of the power-law distribution presented in literature is proposed. Two different four-parameter power-law distributions are considered for the ceramic volume fraction. Some material profiles through the functionally graded shell thickness are illustrated by varying the four parameters of power-law distributions. For the first power-law distribution, the bottom surface of the structure is ceramic rich, whereas the top surface can be metal rich, ceramic rich or made of a mixture of the two constituents and on the contrary for the second one. Symmetric and asymmetric volume fraction profiles are presented in this paper. The homogeneous isotropic material can be inferred as a special case of functionally graded materials (FGM). The governing equations of motion are expressed as functions of five kinematic parameters, by using the constitutive and kinematic relationships. The solution is given in terms of generalized displacement components of the points lying on the middle surface of the shell. The discretization of the system equations by means of the Generalized Differential Quadrature (GDQ) method leads to a standard linear eigenvalue problem, where two independent variables are involved without using the Fourier modal expansion methodology. Numerical results concerning six types of shell structures illustrate the influence of the power-law exponent, of the power-law distribution and of the choice of the four parameters on the mechanical behaviour of shell structures considered.     

An algebraic characterization of the optimum of regularized kernel methods

The representer theorem for kernel methods states that the solution of the associated variational problem can be expressed as the linear combination of a finite number of kernel functions. However, for non-smooth loss functions, the analytic characterization of the coefficients poses nontrivial problems. Standard approaches resort to constrained optimization reformulations which, in general, lack a closed-form solution. Herein, by a proper change of variable, it is shown that, for any convex loss function, the coefficients satisfy a system of algebraic equations in a fixed-point form, which may be directly obtained from the primal formulation. The algebraic characterization is specialized to regression and classification methods and the fixed-point equations are explicitly characterized for many loss functions of practical interest. The consequences of the main result are then investigated along two directions. First, the existence of an unconstrained smooth reformulation of the original non-smooth problem is proven. Second, in the context of SURE (Stein’s Unbiased Risk Estimation), a general formula for the degrees of freedom of kernel regression methods is derived.     

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

Coordinated multirobot exploration involves autonomous discovering and mapping of the features of initially unknown environments by using multiple robots. Autonomously exploring mobile robots are usually driven, both in selecting locations to visit and in assigning them to robots, by knowledge of the already explored portions of the environment, often represented in a metric map. In the literature, some works addressed the use of semantic knowledge in exploration, which, embedded in a semantic map, associates spatial concepts (like ‘rooms’ and ‘corridors’) with metric entities, showing its effectiveness in improving the total area explored by robots. In this paper, we build on these results and propose a system that exploits semantic information to push robots to explore relevant areas of initially unknown environments, according to a priori information provided by human users. Discovery of relevant areas is significant in some search and rescue settings, in which human rescuers can instruct robots to search for victims in specific areas, for example in cubicles if a disaster happened in an office building during working hours. We propose to speed up the exploration of specific areas by using semantic information both to select locations to visit and to determine the number of robots to allocate to those locations. In this way, for example, more robots could be assigned to a candidate location in a corridor, so the attached rooms can be explored faster. We tested our semantic-based multirobot exploration system within a reliable robot simulator and we evaluated its performance in realistic search and rescue indoor settings with respect to state-of-the-art approaches.     

Command governor for constrained switched systems with scheduled model transition dwell times

In this paper, a switched control architecture for constrained control systems is presented. The strategy is based on command governor ideas that are here specialized to ‘optimally’ schedule switching events on the plant dynamics for improving control performance at the expense of low computational burdens. The significance of the method mainly lies in its capability to avoid constraints violation and loss of stability regardless of any configuration change occurrence in the plant/constraint structure. To this end, the concept of model transition dwell time is used within the proposed control framework to formally define the minimum time necessary to enable a switching event under guaranteed conditions on the overall stability and constraint fulfilment. Simulation results on a simple linear system and on a Cessna 182 aircraft model show the effectiveness of the proposed strategy. Copyright © 2017 John Wiley & Sons, Ltd.     

A study of co-occurrence based local features for camera model identification

Camera model identification has great relevance for many forensic applications, and is receiving growing attention in the literature. Virtually all techniques rely on the traces left in the image by the long sequence of in-camera processes which are specific of each model. They differ in the prior assumptions, if any, and in how such evidence is gathered in expressive features. In this work we study a class of blind features, based on the analysis of the image residuals of all color bands. They are extracted locally, based on co-occurrence matrices of selected neighbors, and then used to train a classifier. A number of experiments are carried out on the well-known Dresden Image Database. Besides the full-knowledge case, where all models of interest are known in advance, other scenarios with more limited knowledge and partially corrupted images are also investigated. Experimental results show these features to provide a state-of-the-art performance.