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In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Other than sparse prior, structures on the sparse pattern of the signal have also been used as an additional prior, called modelbased compressive sensing, such as clustered structure and tree structure on wavelet coefficients. In this paper, the cluster structured sparse signals are investigated. Under the framework of Bayesian Compressive Sensing, a hierarchical Bayesian model is employed to model both the sparse prior and cluster prior, then Markov Chain Monte Carlo (MCMC) sampling is implemented for the inference. Unlike the state-of-the-art algorithms which are also taking into account the cluster prior, the proposed algorithm solves the inverse problem automatically -prior information on the number of clusters and the size of each cluster is unknown. The experimental results show that the proposed algorithm outperforms many state-of-the-art algorithms.

. Super twisting algorithm based step-by-step sliding mode observers for nonlinear systems with unknown inputs. International Journal of Systems Science, Taylor Francis, 2007, 38 (10), pp.803-815. inria-00128137v2 Super twisting algorithm based step-by-step sliding mode observers for nonlinear systems with unknown inputsThis paper highlights the interest of step-by-step higher order sliding mode observers for MIMO nonlinear systems with unknown inputs. A structural matching condition, stating on the possibility to design such observers and to reconstruct the unknown inputs, is derived. A finite time sliding mode observer, based on the hierarchical use of the super twisting algorithm, is developed. Then, it is shown that this observer is of interest in the field of hybrid systems and systems with observability singularities. Lastly, it is shown through an example how to relax the usual matching condition by the means of this type of finite time sliding mode observer.

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The design of observers for nonlinear systems with unknown, time-varying, bounded delays, on both state and input, still constitutes an open problem. In this paper, we show how to solve it for a class of nonlinear systems by combining the high gain observer approach with a Lyapunov-Krasovskii functional suitable choice. Sufficient conditions are provided to prove the practical stability of the observer. It is shown that the observation error is bounded and depends on the size of two parameters: the known upper bound delay of the unknown time-varying function delay and the instantaneous state dynamic variation. Furthermore, for the particular case of constant known time delay, the convergence of the proposed observer becomes exponential. The feasibility of the proposed strategy is illustrated by a numerical example.

The problem of converters coordination of a fuel cell system involving a hydrogen fuel cell with supercapacitors for applications with high instantaneous dynamic power is addressed in this paper. The problem is solved by using a non-linear controller based on passivity. The controller design is based on the interconnection and damping assignment approach, where the proof of the local system stability of the whole closed-loop system is shown. Simulation and experimental results on a reduced scale system prove the feasibility of the proposed approach for a real electrical vehicle.

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