The Experts below are selected from a list of 19047 Experts worldwide ranked by ideXlab platform

E Rogers - One of the best experts on this subject based on the ideXlab platform.

  • constrained observer based iterative learning control design in the repetitive process setting
    Conference on Decision and Control, 2019
    Co-Authors: Julia Emelianova, Pavel Pakshin, Krzysztof Galkowski, E Rogers
    Abstract:

    Iterative learning control is a well established method applicable to systems that repeat the same Finite Duration task over and over again. The mechanism is to use information from the previous repetition to update the control input for the next repetition and thereby sequentially improve performance. Given that it directly regulates the control input, there may be cases where the levels of control action breach the safe operating range of the actuators used. This paper develops a new design for the case when a limit is placed on the control action allowed. The analysis represents the dynamics in a 2D systems setting and uses the stability theory for the particular case of repetitive processes as a basis for analysis and design.

  • passivity based iterative learning control design in the discrete repetitive process setting
    European Control Conference, 2018
    Co-Authors: Pavel Pakshin, Krzysztof Galkowski, Julia Emelianova, Mikhail Emelianov, E Rogers
    Abstract:

    Repetitive processes are important class of 2D systems with engineering applications and also the stability theory for them provides a setting for iterative learning control design. The application area for this form of control is systems that execute the same Finite Duration task over and over again, with resetting to the starting location one each execution is complete. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified. This paper applies the recently developed passivity theory for discrete repetitive process to iterative learning control design. Based on this theory, a parametric description of a class of stabilizing controllers is obtained and a new design is developed that enhances the convergence properties of the implemented control law. An example using the model of a flexible link is given to demonstrate the application of the new design.

  • further results on dynamic iterative learning control law design using repetitive process stability theory
    2017 10th International Workshop on Multidimensional (nD) Systems (nDS), 2017
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers
    Abstract:

    Iterative learning control can be applied to systems that execute the same Finite Duration task over and over again. This method control has been applied to many engineering systems, such as gantry robots and electrical motors. This paper gives further results on the design of dynamic iterative learning control laws using the repetitive process setting using a model of a 3D crane. Of particular interest is to compensate for the effect of noise. The eventual aim is to experimentally test this form of control law on the crane.

  • higher order iterative learning control law design using linear repetitive process theory convergence and robustness
    IFAC-PapersOnLine, 2017
    Co-Authors: Xuan Wang, Bing Chu, E Rogers
    Abstract:

    Abstract Iterative learning control has been developed for processes or systems that complete the same Finite Duration task over and over again. The mode of operation is that after each execution is complete the system resets to the starting location, the next execution is completed and so on. Each execution is known as a trial and its Duration is termed the trial length. Once each trial is complete the information generated is available for use in computing the control input for the next trial. This paper uses the repetitive process setting to develop new results on the design of higher-order ILC control laws for discrete dynamics. The new results include conditions that guarantee error convergence and design in the presence of model uncertainty.

  • iterative learning control design based on feedback linearization and nonlinear repetitive process stability theory
    Conference on Decision and Control, 2016
    Co-Authors: Pavel Pakshin, Krzysztof Galkowski, Julia Emelianova, Mikhail Emelianov, E Rogers
    Abstract:

    Iterative learning control laws can be applied to systems that execute the same Finite Duration task over and over again. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified. This paper applies recently developed stability theory for nonlinear repetitive processes to differential dynamics that can be feedback linearized. The design in then completed by applying the stability theory for linear dynamics. An example using the model of a single-link flexible joint is used to illustrate the new design.

A Antoniou - One of the best experts on this subject based on the ideXlab platform.

  • an improved method for the design of fir quadrature mirror image filter banks
    IEEE Transactions on Signal Processing, 1998
    Co-Authors: A Antoniou
    Abstract:

    A new method for the design of general Finite-Duration impulse response (FIR) quadrature mirror-image filter (QMF) banks that eliminates the computation of large matrices is proposed. The design problem is formulated to include low-delay QMF banks, which are highly desirable in some applications. The paper concludes with design results and comparisons that show that conventional QMF banks can be designed with only a fraction of the computational effort required by a method due to Chen and Lee (1992). On the other hand, in the case of low-delay QMF banks, the proposed method can increase the stopband attenuation substantially compared with what can be achieved by existing methods.

  • A new method for the design of FIR quadrature mirror-image filter banks
    IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 1998
    Co-Authors: Wu-sheng Lu, Hua Xu, A Antoniou
    Abstract:

    A new algebraic method for the design of two-channel Finite-Duration impulse response quadrature mirror-image filter (FIR QMF) banks is proposed. The method uses a self-convolution technique to reformulate a fourth-order objective function whose minimization lends to the design of QMF banks. It is shown that the reformulated optimization problem can be solved by an iterative technique in which the major part of each iteration is carried out in terms of a closed-form formula. This leads to improved computational efficiency relative to that in several existing design methods. The method is then extended to the design of QMF banks with low reconstruction delay. Two examples are included which show that the proposed design method leads to filter banks with improved performance.

Krzysztof Galkowski - One of the best experts on this subject based on the ideXlab platform.

  • constrained observer based iterative learning control design in the repetitive process setting
    Conference on Decision and Control, 2019
    Co-Authors: Julia Emelianova, Pavel Pakshin, Krzysztof Galkowski, E Rogers
    Abstract:

    Iterative learning control is a well established method applicable to systems that repeat the same Finite Duration task over and over again. The mechanism is to use information from the previous repetition to update the control input for the next repetition and thereby sequentially improve performance. Given that it directly regulates the control input, there may be cases where the levels of control action breach the safe operating range of the actuators used. This paper develops a new design for the case when a limit is placed on the control action allowed. The analysis represents the dynamics in a 2D systems setting and uses the stability theory for the particular case of repetitive processes as a basis for analysis and design.

  • passivity based iterative learning control design in the discrete repetitive process setting
    European Control Conference, 2018
    Co-Authors: Pavel Pakshin, Krzysztof Galkowski, Julia Emelianova, Mikhail Emelianov, E Rogers
    Abstract:

    Repetitive processes are important class of 2D systems with engineering applications and also the stability theory for them provides a setting for iterative learning control design. The application area for this form of control is systems that execute the same Finite Duration task over and over again, with resetting to the starting location one each execution is complete. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified. This paper applies the recently developed passivity theory for discrete repetitive process to iterative learning control design. Based on this theory, a parametric description of a class of stabilizing controllers is obtained and a new design is developed that enhances the convergence properties of the implemented control law. An example using the model of a flexible link is given to demonstrate the application of the new design.

  • further results on dynamic iterative learning control law design using repetitive process stability theory
    2017 10th International Workshop on Multidimensional (nD) Systems (nDS), 2017
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers
    Abstract:

    Iterative learning control can be applied to systems that execute the same Finite Duration task over and over again. This method control has been applied to many engineering systems, such as gantry robots and electrical motors. This paper gives further results on the design of dynamic iterative learning control laws using the repetitive process setting using a model of a 3D crane. Of particular interest is to compensate for the effect of noise. The eventual aim is to experimentally test this form of control law on the crane.

  • iterative learning control design based on feedback linearization and nonlinear repetitive process stability theory
    Conference on Decision and Control, 2016
    Co-Authors: Pavel Pakshin, Krzysztof Galkowski, Julia Emelianova, Mikhail Emelianov, E Rogers
    Abstract:

    Iterative learning control laws can be applied to systems that execute the same Finite Duration task over and over again. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified. This paper applies recently developed stability theory for nonlinear repetitive processes to differential dynamics that can be feedback linearized. The design in then completed by applying the stability theory for linear dynamics. An example using the model of a single-link flexible joint is used to illustrate the new design.

  • reducing conservativeness in robust iterative learning control ilc design using parameter dependent lyapunov functions
    Advances in Computing and Communications, 2015
    Co-Authors: Blazej Cichy, Krzysztof Galkowski, E Rogers
    Abstract:

    Iterative learning control was developed for systems that repeat the same task over a Finite Duration with resetting to the starting point once each execution is complete. The distinguishing feature of this form of control is the use of information from previous executions of the task to update the control signal to be applied on the next execution and thereby sequentially improve performance. Once an execution is complete, all information generated is available for use in control design and how to best use such information is a dominant issue. In applications, robust control is also a critical feature and the new results in this paper use parameter dependent Lyapunov functions to enlarge the uncertainty range allowed for successful design. These results are developed by treating ILC in a repetitive process setting.

Pavel Pakshin - One of the best experts on this subject based on the ideXlab platform.

  • constrained observer based iterative learning control design in the repetitive process setting
    Conference on Decision and Control, 2019
    Co-Authors: Julia Emelianova, Pavel Pakshin, Krzysztof Galkowski, E Rogers
    Abstract:

    Iterative learning control is a well established method applicable to systems that repeat the same Finite Duration task over and over again. The mechanism is to use information from the previous repetition to update the control input for the next repetition and thereby sequentially improve performance. Given that it directly regulates the control input, there may be cases where the levels of control action breach the safe operating range of the actuators used. This paper develops a new design for the case when a limit is placed on the control action allowed. The analysis represents the dynamics in a 2D systems setting and uses the stability theory for the particular case of repetitive processes as a basis for analysis and design.

  • passivity based iterative learning control design in the discrete repetitive process setting
    European Control Conference, 2018
    Co-Authors: Pavel Pakshin, Krzysztof Galkowski, Julia Emelianova, Mikhail Emelianov, E Rogers
    Abstract:

    Repetitive processes are important class of 2D systems with engineering applications and also the stability theory for them provides a setting for iterative learning control design. The application area for this form of control is systems that execute the same Finite Duration task over and over again, with resetting to the starting location one each execution is complete. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified. This paper applies the recently developed passivity theory for discrete repetitive process to iterative learning control design. Based on this theory, a parametric description of a class of stabilizing controllers is obtained and a new design is developed that enhances the convergence properties of the implemented control law. An example using the model of a flexible link is given to demonstrate the application of the new design.

  • iterative learning control design based on feedback linearization and nonlinear repetitive process stability theory
    Conference on Decision and Control, 2016
    Co-Authors: Pavel Pakshin, Krzysztof Galkowski, Julia Emelianova, Mikhail Emelianov, E Rogers
    Abstract:

    Iterative learning control laws can be applied to systems that execute the same Finite Duration task over and over again. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified. This paper applies recently developed stability theory for nonlinear repetitive processes to differential dynamics that can be feedback linearized. The design in then completed by applying the stability theory for linear dynamics. An example using the model of a single-link flexible joint is used to illustrate the new design.

Chunsu Park - One of the best experts on this subject based on the ideXlab platform.

  • the hopping discrete fourier transform sp tips tricks
    IEEE Signal Processing Magazine, 2014
    Co-Authors: Chunsu Park
    Abstract:

    The discrete Fourier transform (DFT) produces a Fourier representation for Finite-Duration data sequences. In addition to its theoretical importance, the DFT plays a key role in the implementation of a variety of digital signal-?processing algorithms. Several algorithms including the fast Fourier transform (FFT) and the Goertzel algorithm have been introduced for the fast implementation of the DFT [1], [2].