The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform

Carl N. Nett - One of the best experts on this subject based on the ideXlab platform.

  • worst Case deterministic identification in h sub infinity the Continuous Time Case
    IEEE Transactions on Automatic Control, 1992
    Co-Authors: Arthur J. Helmicki, Clas A. Jacobson, Carl N. Nett
    Abstract:

    Results obtained by the authors (1991) worst-Case/deterministic H/sub infinity / identification of discrete-Time plants are extended to Continuous-Time plants. The problem involves identification of the transfer function of a stable strictly proper Continuous-Time plant from a finite number of noisy point samples of the plant frequency response. The assumed information consists of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, an upper bound on the roll-off rate of the plant, and an upper bound on the noise level. Concrete plans of identification algorithms are provided for this problem. Explicit worst-Case/deterministic error bounds for each algorithm establish that they are robustly convergent and (essentially) asymptotically optimal. Additionally, these bounds provide an a priori computable H/sub infinity / uncertainty specification, corresponding to the resulting identified plant transfer function, as an explicit function of the plant and noise prior information and the data cardinality. >

  • Worst-Case/deterministic identification in H/sub infinity /: the Continuous-Time Case
    IEEE Transactions on Automatic Control, 1992
    Co-Authors: Arthur J. Helmicki, Clas A. Jacobson, Carl N. Nett
    Abstract:

    Results obtained by the authors (1991) worst-Case/deterministic H/sub infinity / identification of discrete-Time plants are extended to Continuous-Time plants. The problem involves identification of the transfer function of a stable strictly proper Continuous-Time plant from a finite number of noisy point samples of the plant frequency response. The assumed information consists of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, an upper bound on the roll-off rate of the plant, and an upper bound on the noise level. Concrete plans of identification algorithms are provided for this problem. Explicit worst-Case/deterministic error bounds for each algorithm establish that they are robustly convergent and (essentially) asymptotically optimal. Additionally, these bounds provide an a priori computable H/sub infinity / uncertainty specification, corresponding to the resulting identified plant transfer function, as an explicit function of the plant and noise prior information and the data cardinality. >

  • Technical Notes and Correspondence Worst-Case/Deterrninistic Identification in H,: The Continuous-Time Case
    1992
    Co-Authors: Arthur J. Helmicki, Clas A. Jacobson, Carl N. Nett
    Abstract:

    In this note, recent results obtained by the authors for worst-Case/deterministic H, identification of discrete-Time plants are extended to Continuous-Time plants. The problem considered involves identification of the transfer function of a stable strictly proper continu- ous-Time plant from a finite number of noisy point samples of the plant frequency response. The assumed a priori information consists of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, an upper bound on the "roll-off rate" of the plant, and an upper bound on the noise level. Concrete plans of identification algorithms are provided for this problem. Explicit worst-Case/deterministic error bounds are provided for each algorithm in these plans. These hounds establish that the given plans of algorithms are robustly convergent and (essentially) asymptotically optimal. Addi- tionally, these bounds provide an a priori computable H, uncertainty specification, corresponding to the resulting identified plant transfer function, as an explicit function of the plant and noise apriori informa- tion and the data cardinality.

Elena Zattoni - One of the best experts on this subject based on the ideXlab platform.

  • output regulation in switched linear parameter varying systems with preview the Continuous Time Case
    European Control Conference, 2009
    Co-Authors: Elena Zattoni
    Abstract:

    This paper deals with the problem of perfect elimination of the transients of the regulated output caused by parameter variations, in Continuous-Time multivariable linear systems. Parameter changes are assumed to be instantaneous and known in advance within a finite Time horizon. The Continuous-Time Case needs separate investigation from the discrete-Time Case, that has recently been solved both in the exact context and in the l 2 -optimal context. In fact, the Continuous-Time solution of the exact problem cannot be found through a plain conversion from Continuous to discrete, since discretizations do not generically preserve the geometric properties of the set of switched systems playing a key role in the specific synthesis procedure. Moreover, the Continuous-Time exact solution cannot straightforwardly be retrieved as the zero-cost solution of the corresponding optimization problem, since the latter turns out to be a singular problem in general, due to the structure of the systems involved. In the light of these considerations, geometric conditions for the Continuous-Time problem solvability are proved and a complete design procedure is presented.

  • ECC - Output regulation in switched linear parameter varying systems with preview: The Continuous-Time Case
    2009 European Control Conference (ECC), 2009
    Co-Authors: Elena Zattoni
    Abstract:

    This paper deals with the problem of perfect elimination of the transients of the regulated output caused by parameter variations, in Continuous-Time multivariable linear systems. Parameter changes are assumed to be instantaneous and known in advance within a finite Time horizon. The Continuous-Time Case needs separate investigation from the discrete-Time Case, that has recently been solved both in the exact context and in the l 2 -optimal context. In fact, the Continuous-Time solution of the exact problem cannot be found through a plain conversion from Continuous to discrete, since discretizations do not generically preserve the geometric properties of the set of switched systems playing a key role in the specific synthesis procedure. Moreover, the Continuous-Time exact solution cannot straightforwardly be retrieved as the zero-cost solution of the corresponding optimization problem, since the latter turns out to be a singular problem in general, due to the structure of the systems involved. In the light of these considerations, geometric conditions for the Continuous-Time problem solvability are proved and a complete design procedure is presented.

Arthur J. Helmicki - One of the best experts on this subject based on the ideXlab platform.

  • worst Case deterministic identification in h sub infinity the Continuous Time Case
    IEEE Transactions on Automatic Control, 1992
    Co-Authors: Arthur J. Helmicki, Clas A. Jacobson, Carl N. Nett
    Abstract:

    Results obtained by the authors (1991) worst-Case/deterministic H/sub infinity / identification of discrete-Time plants are extended to Continuous-Time plants. The problem involves identification of the transfer function of a stable strictly proper Continuous-Time plant from a finite number of noisy point samples of the plant frequency response. The assumed information consists of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, an upper bound on the roll-off rate of the plant, and an upper bound on the noise level. Concrete plans of identification algorithms are provided for this problem. Explicit worst-Case/deterministic error bounds for each algorithm establish that they are robustly convergent and (essentially) asymptotically optimal. Additionally, these bounds provide an a priori computable H/sub infinity / uncertainty specification, corresponding to the resulting identified plant transfer function, as an explicit function of the plant and noise prior information and the data cardinality. >

  • Worst-Case/deterministic identification in H/sub infinity /: the Continuous-Time Case
    IEEE Transactions on Automatic Control, 1992
    Co-Authors: Arthur J. Helmicki, Clas A. Jacobson, Carl N. Nett
    Abstract:

    Results obtained by the authors (1991) worst-Case/deterministic H/sub infinity / identification of discrete-Time plants are extended to Continuous-Time plants. The problem involves identification of the transfer function of a stable strictly proper Continuous-Time plant from a finite number of noisy point samples of the plant frequency response. The assumed information consists of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, an upper bound on the roll-off rate of the plant, and an upper bound on the noise level. Concrete plans of identification algorithms are provided for this problem. Explicit worst-Case/deterministic error bounds for each algorithm establish that they are robustly convergent and (essentially) asymptotically optimal. Additionally, these bounds provide an a priori computable H/sub infinity / uncertainty specification, corresponding to the resulting identified plant transfer function, as an explicit function of the plant and noise prior information and the data cardinality. >

  • Technical Notes and Correspondence Worst-Case/Deterrninistic Identification in H,: The Continuous-Time Case
    1992
    Co-Authors: Arthur J. Helmicki, Clas A. Jacobson, Carl N. Nett
    Abstract:

    In this note, recent results obtained by the authors for worst-Case/deterministic H, identification of discrete-Time plants are extended to Continuous-Time plants. The problem considered involves identification of the transfer function of a stable strictly proper continu- ous-Time plant from a finite number of noisy point samples of the plant frequency response. The assumed a priori information consists of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, an upper bound on the "roll-off rate" of the plant, and an upper bound on the noise level. Concrete plans of identification algorithms are provided for this problem. Explicit worst-Case/deterministic error bounds are provided for each algorithm in these plans. These hounds establish that the given plans of algorithms are robustly convergent and (essentially) asymptotically optimal. Addi- tionally, these bounds provide an a priori computable H, uncertainty specification, corresponding to the resulting identified plant transfer function, as an explicit function of the plant and noise apriori informa- tion and the data cardinality.

Pramod P. Khargonekar - One of the best experts on this subject based on the ideXlab platform.

  • a class of algorithms for identification in h sub infinity Continuous Time Case
    IEEE Transactions on Automatic Control, 1993
    Co-Authors: Hüseyin Akçay, Pramod P. Khargonekar
    Abstract:

    The problem of system identification in H/sub infinity / for the Continuous-Time Case is investigated. It is shown that the class of systems with a lower bound on the relative stability, an upper bound on the steady-state gain, and an upper bound on the roll-off rate is admissible. This allows one to develop a class of robustly convergent nonlinear algorithms. The algorithms in this class have a two-stage structure and are characterized by the use of window functions. Explicit worst-Case error bounds in H/sub infinity / norm between the identified model and the unknown system are given for a particular algorithm. An example is provided to illustrate the application of the results obtained. >

  • A class of algorithms for identification in H/sub infinity /: Continuous-Time Case
    IEEE Transactions on Automatic Control, 1993
    Co-Authors: Hüseyin Akçay, Pramod P. Khargonekar
    Abstract:

    The problem of system identification in H/sub infinity / for the Continuous-Time Case is investigated. It is shown that the class of systems with a lower bound on the relative stability, an upper bound on the steady-state gain, and an upper bound on the roll-off rate is admissible. This allows one to develop a class of robustly convergent nonlinear algorithms. The algorithms in this class have a two-stage structure and are characterized by the use of window functions. Explicit worst-Case error bounds in H/sub infinity / norm between the identified model and the unknown system are given for a particular algorithm. An example is provided to illustrate the application of the results obtained. >

Choon Yik Tang - One of the best experts on this subject based on the ideXlab platform.

  • Zero-Gradient-Sum Algorithms for Distributed Convex Optimization: The Continuous-Time Case
    IEEE Transactions on Automatic Control, 2012
    Co-Authors: Choon Yik Tang
    Abstract:

    This technical note presents a set of Continuous-Time distributed algorithms that solve unconstrained, separable, convex optimization problems over undirected networks with fixed topologies. The algorithms are developed using a Lyapunov function candidate that exploits convexity, and are called Zero-Gradient-Sum (ZGS) algorithms as they yield nonlinear networked dynamical systems that evolve invariantly on a zero-gradient-sum manifold and converge asymptotically to the unknown optimizer. We also describe a systematic way to construct ZGS algorithms, show that a subset of them actually converge exponentially, and obtain lower and upper bounds on their convergence rates in terms of the network topologies, problem characteristics, and algorithm parameters, including the algebraic connectivity, Laplacian spectral radius, and function curvatures. The findings of this technical note may be regarded as a natural generalization of several well-known algorithms and results for distributed consensus, to distributed convex optimization.

  • Zero-Gradient-Sum Algorithms for Distributed Convex Optimization: The Continuous-Time Case
    arXiv: Systems and Control, 2011
    Co-Authors: Choon Yik Tang
    Abstract:

    This paper presents a set of Continuous-Time distributed algorithms that solve unconstrained, separable, convex optimization problems over undirected networks with fixed topologies. The algorithms are developed using a Lyapunov function candidate that exploits convexity, and are called Zero-Gradient-Sum (ZGS) algorithms as they yield nonlinear networked dynamical systems that evolve invariantly on a zero-gradient-sum manifold and converge asymptotically to the unknown optimizer. We also describe a systematic way to construct ZGS algorithms, show that a subset of them actually converge exponentially, and obtain lower and upper bounds on their convergence rates in terms of the network topologies, problem characteristics, and algorithm parameters, including the algebraic connectivity, Laplacian spectral radius, and function curvatures. The findings of this paper may be regarded as a natural generalization of several well-known algorithms and results for distributed consensus, to distributed convex optimization.

  • Zero-gradient-sum algorithms for distributed convex optimization: The Continuous-Time Case
    Proceedings of the 2011 American Control Conference, 2011
    Co-Authors: Jie Lu, Choon Yik Tang
    Abstract:

    This paper presents a family of Continuous-Time distributed algorithms called Zero-Gradient-Sum (ZGS) algorithms, which solve unconstrained, separable, convex optimization problems over undirected networks with fixed topologies. The ZGS algorithms are derived using a Lyapunov function candidate that exploits convexity, and get their name from the fact that they yield nonlinear networked dynamical systems whose states slide along an invariant, zero-gradient-sum manifold and converge asymptotically to the unknown minimizer. We also present a systematic way to construct ZGS algorithms, show that a subset of them converge exponentially, and obtain lower bounds on their convergence rates in terms of the convexity characteristics of the problem and the network topology, including its algebraic connectivity. Finally, we show that some of the well-studied Continuous-Time distributed consensus algorithms are special Cases of ZGS algorithms and discuss the ramifications.