The Experts below are selected from a list of 168 Experts worldwide ranked by ideXlab platform

K. Nakano - One of the best experts on this subject based on the ideXlab platform.

  • Trajectory tracking control of bimodal piecewise affine systems
    Proceedings of the 2005 American Control Conference 2005., 1
    Co-Authors: Kazunori Sakurama, Toshiharu Sugie, K. Nakano
    Abstract:

    This paper deals with a trajectory tracking problem of a class of bimodal piecewise affine systems, which have rarely been discussed so far. This would be very challenging because of the discontinuous changes of their vector fields. First, we introduce an Error Variable and an Error system as a generalization of the tracking Error and its system. As an Error Variable, a function switched by the mode of a piecewise affine system is adopted to overcome an inherent difficulty in trajectory tracking of piecewise affine systems. Next, we design a tracking controller which stabilizes the Error system using a Lyapunov-like function, which can be applied to systems including state jumps. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

Nicolas Gac - One of the best experts on this subject based on the ideXlab platform.

  • Bayesian Inference with Error Variable Splitting and Sparsity Enforcing Priors for Linear Inverse Problems
    2018
    Co-Authors: Ali Mohammad-djafari, Mircea Dumitru, Camille Chapdelaine, Nicolas Gac
    Abstract:

    Regularization and Bayesian inference based methods have been successfully applied for linear inverse problems. In these methods, often simple Gaussian or Poisson models for the forward model Errors have been considered. In this work, we use Variable splitting for the Errors to model different sources of Errors and their possible non-stationarity or impulsive nature using Student-t or other heavy tailed distributions. Also, as a prior model, a sparsity enforcing hierarchical model of Infinite Gaussian Mixture model is introduced. With these prior models, we obtain a complete Bayesian inference framework which can efficiently be implemented for any linear inverse problem. Interestingly , many recent regularization-based algorithms such as Alternating Direction Method of Multipliers (ADMM) as well as more classical Bayesian based methods such as Sparse Bayesian Learning (SBL) are obtained as particular cases. One advantage of the Bayesian approach is the possibility to estimate, jointly with the reconstruction, the hyper-parameters such as the regulariza-tion parameter, thus the capability of proposing unsupervised methods. Examples of implementation of the proposed method in different signal and image processing such as deconvolution in mass spectrometry, estimation of periodic components estimation in biological signals and computed tomography are mentioned and referenced.

  • EUSIPCO - Bayesian Inference with Error Variable Splitting and Sparsity Enforcing Priors for Linear Inverse Problems
    2018 26th European Signal Processing Conference (EUSIPCO), 2018
    Co-Authors: Ali Mohammad-djafari, Mircea Dumitru, Camille Chapdelaine, Nicolas Gac
    Abstract:

    Regularization and Bayesian inference based methods have been successfully applied for linear inverse problems. In these methods, often simple Gaussian or Poisson models for the forward model Errors have been considered. In this work, we use Variable splitting for the Errors to model different sources of Errors and their possible non-stationarity or impulsive nature using Student-t or other heavy tailed distributions. Also, as a prior model, a sparsity enforcing hierarchical model of Infinite Gaussian Mixture model is introduced. With these prior models, we obtain a complete Bayesian inference framework which can efficiently be implemented for any linear inverse problem. Interestingly, many recent regularization-based algorithms such as Alternating Direction Method of Multipliers (ADMM) as well as more classical Bayesian based methods such as Sparse Bayesian Learning (SBL) are obtained as particular cases. One advantage of the Bayesian approach is the possibility to estimate, jointly with the reconstruction, the hyper-parameters such as the regularization parameter, thus the capability of proposing unsupervised methods. Examples of implementation of the proposed method in different signal and image processing such as deconvolution in mass spectrometry, estimation of periodic components estimation in biological signals and computed tomography are mentioned and referenced.

Kazunori Sakurama - One of the best experts on this subject based on the ideXlab platform.

  • Trajectory tracking control of bimodal piecewise affine systems
    International Journal of Control, 2005
    Co-Authors: Kazunori Sakurama, Toshiharu Sugie
    Abstract:

    This paper deals with a trajectory tracking problem for a class of bimodal piecewise affine systems, which is inherently difficult because of the discontinuous changes of their vector fields. First, we introduce an Error Variable and an Error system as a generalization of the tracking Error and its system. As an Error Variable, a function switched by the mode of a piecewise affine system is adopted to overcome the inherent difficulty in trajectory tracking control of piecewise affine systems. Next, we design a tracking controller which stabilizes the Error system using a Lyapunov-like function, which can be applied to systems including state jumps. Furthermore, the feasibility condition of tracking for SISO piecewise linear systems is simplified. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

  • Trajectory tracking control of bimodal piecewise affine systems
    Proceedings of the 2005 American Control Conference 2005., 1
    Co-Authors: Kazunori Sakurama, Toshiharu Sugie, K. Nakano
    Abstract:

    This paper deals with a trajectory tracking problem of a class of bimodal piecewise affine systems, which have rarely been discussed so far. This would be very challenging because of the discontinuous changes of their vector fields. First, we introduce an Error Variable and an Error system as a generalization of the tracking Error and its system. As an Error Variable, a function switched by the mode of a piecewise affine system is adopted to overcome an inherent difficulty in trajectory tracking of piecewise affine systems. Next, we design a tracking controller which stabilizes the Error system using a Lyapunov-like function, which can be applied to systems including state jumps. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

Toshiharu Sugie - One of the best experts on this subject based on the ideXlab platform.

  • Trajectory tracking control of bimodal piecewise affine systems
    International Journal of Control, 2005
    Co-Authors: Kazunori Sakurama, Toshiharu Sugie
    Abstract:

    This paper deals with a trajectory tracking problem for a class of bimodal piecewise affine systems, which is inherently difficult because of the discontinuous changes of their vector fields. First, we introduce an Error Variable and an Error system as a generalization of the tracking Error and its system. As an Error Variable, a function switched by the mode of a piecewise affine system is adopted to overcome the inherent difficulty in trajectory tracking control of piecewise affine systems. Next, we design a tracking controller which stabilizes the Error system using a Lyapunov-like function, which can be applied to systems including state jumps. Furthermore, the feasibility condition of tracking for SISO piecewise linear systems is simplified. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

  • Trajectory tracking control of bimodal piecewise affine systems
    Proceedings of the 2005 American Control Conference 2005., 1
    Co-Authors: Kazunori Sakurama, Toshiharu Sugie, K. Nakano
    Abstract:

    This paper deals with a trajectory tracking problem of a class of bimodal piecewise affine systems, which have rarely been discussed so far. This would be very challenging because of the discontinuous changes of their vector fields. First, we introduce an Error Variable and an Error system as a generalization of the tracking Error and its system. As an Error Variable, a function switched by the mode of a piecewise affine system is adopted to overcome an inherent difficulty in trajectory tracking of piecewise affine systems. Next, we design a tracking controller which stabilizes the Error system using a Lyapunov-like function, which can be applied to systems including state jumps. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

Elio Usai - One of the best experts on this subject based on the ideXlab platform.

  • ECC - A new approach to causal output tracking for non-minimum phase nonlinear systems via combined first/second order sliding mode control
    2013 European Control Conference (ECC), 2013
    Co-Authors: Alessandro Pisano, Simon Baev, D. Salimbeni, Yuri B. Shtessel, Elio Usai
    Abstract:

    This paper deals with the output-tracking control problem for a class of non minimum phase nonlinear systems. In order to provide the desired output tracking features while, at the same time, stabilizing the internal dynamics in finite time, a novel cascade-like control structure is devised, which mixes together first and second order sliding mode methodologies. The scheme provides asymptotic convergence, under full state feedback, bringing the main benefit, as compared with previous literature, that the sliding mode dynamics of the output tracking Error Variable is of lower dimension, which simplifies the tuning of the scheme and gives rise to improved transient features of the Error Variables. Theoretical analysis and simulation results support the effectiveness of the proposed solution.