International audienceThis paper presents a new approach for guaranteed state estimation based on zonotopes for linear discrete-time multivariable systems with interval multiplicative uncertainties, in the presence of bounded state perturbations and noises. At each sample time, the presented approach computes a zonotope which contains the real system state. A P-radius-based criterion is minimized in order to decrease the size of the zonotope at each sample time and to obtain an increasingly accurate state estimation. The proposed approach allows one to efficiently handle the trade-off between the complexity of the computation and the accuracy of the estimation. An illustrative example is analyzed in order to highlight the advantages of the proposed state estimation technique
Abstract. In this paper we consider the problem of drawing and displaying a series of related graphs, i.e., graphs that share all, or parts of the same vertex set. We designed and implemented three different algorithms for simultaneous graph drawing and three different visualization schemes. The algorithms are based on a modification of the force-directed algorithm that allows us to take into account vertex weights and edge weights in order to achieve mental map preservation while obtaining individually readable drawings. The implementation is in Java and the system can be downloaded at http://simg.cs.arizona.edu/.
Abstract. In this paper we consider the problem of drawing and displaying a series of related graphs, i.e., graphs that share all, or parts of the same vertex set. We designed and implemented three different algorithms for simultaneous graph drawing and three different visualization schemes. The algorithms are based on a modification of the force-directed algorithm that allows us to take into account vertex weights and edge weights in order to achieve mental map preservation while obtaining individually readable drawings. The implementation is in Java and the system can be downloaded at http://simg.cs.arizona.edu/.
This paper proposes a methodology for guaranteed state estimation of linear discretetime systems in the presence of bounded disturbances and noises. This aims at computing an outer approximation of the state estimation domain represented by a zonotope. A new criterion is used to reduce the size of the zonotope at each sample time. An illustrative example is analyzed in order to highlight the advantages of the proposed algorithm.
In this paper, a second order sliding mode observer for the induction motor without mechanical sensor is presented. This observer converges in finite time and is robust to the variation of parameters. Using Matlab/Simulink, the simulation results show the performance of the proposed observer. Furthermore, an industrial application is presented in order to highlight the technological interest of the proposed method and also show the difficulties due to real time computation constraints.
This paper presents an improved method for guaranteed state estimation of discrete-time linear-time varying systems affected by disturbances, noises and structured uncertainties modeled as interval uncertainties. Under the hypothesis that the disturbances and the noises are bounded, a zonotopic outer approximation of the state estimation domain is computed offering good performance and low complexity compared to the existing methods. The size of this zonotope is decreased by solving an off-line optimization problem. The advantages of the proposed approach are illustrated via a numerical example.
This paper proposes an approach to deal with the problem of robust output feedback model predictive control for linear discrete-time systems subject to state and input constraints, in the presence of unknown but bounded disturbances and measurement noises. The estimation of the states is built using a zonotopic set-membership estimation. This set is timedecreasing and is computed off-line as the solution of a Linear Matrix Inequality optimization problem. The control law is designed by using tube-based model predictive control such that the closed-loop stability is guaranteed and the state and input constraints are fulfilled. The proposed methodology is illustrated through numerical simulations.
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