The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

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

  • repetitive process based design and experimental verification of a dynamic iterative learning Control Law
    Control Engineering Practice, 2016
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, Weronika Nowicka, E Rogers
    Abstract:

    This paper gives new results on iterative learning Control (ILC) design and experimental verification using the stability theory of linear repetitive processes. Using this theory a Control Law can be designed in one step to force error convergence and produce acceptable transient dynamics. Previous research developed algorithms for the design of a static Control Law with supporting experimental verification. Should a static Law not give the required levels of performance one option is to allow the Control Law to have internal dynamics. This paper develops a procedure for the design of such a Control Law with supporting experimental verification on a gantry robot, including a comparative performance against a static Law applied to the same robot. The resulting ILC design is an efficient combination of linear matrix inequalities and optimization algorithms.

  • Control Law design for discrete linear repetitive processes with non local updating structures
    Multidimensional Systems and Signal Processing, 2013
    Co-Authors: Blazej Cichy, Krzysztof Galkowski, E Rogers, Anton Kummert
    Abstract:

    Repetitive processes are a class of 2D systems where information propagation in one direction is of finite duration. These processes make a series of sweeps, termed passes, through a set of dynamics and on completion of each pass resetting to the starting position occurs ready for the start of the next pass. The Control problem is that the previous pass output, termed the pass profile, acts as a forcing function on the current pass and can result in oscillations that increase in amplitude from pass-to-pass. In the case of discrete dynamics, these processes have structural links with 2D systems described by the well known Roesser and Fornasini–Marchesini state-space models but some applications require updating structures that cannot be represented by these models. This requirement arises either in adequately modeling the dynamics or as a result of the Control Law structure and requires the development of a systems theory for eventual use in applications. In this paper such a theory is advanced through the development of new Control Law design algorithms.

  • kyp lemma based stability and Control Law design for differential linear repetitive processes with applications
    Systems & Control Letters, 2013
    Co-Authors: Wojciech Paszke, E Rogers, Krzysztof Galkowski
    Abstract:

    Repetitive processes are a class of two-dimensional systems that have physical applications, including the design of iterative learning Control Laws where experimental validation results have been reported. This paper uses the Kalman–Yakubovich–Popov lemma to develop new stability tests for differential linear repetitive processes that are computationally less intensive than those currently available. These tests are then extended to allow Control Law design for stability and performance.

  • output information based iterative learning Control Law design with experimental verification
    Journal of Dynamic Systems Measurement and Control-transactions of The Asme, 2012
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers, Chris Freeman, Zhonglun Cai, P L Lewin
    Abstract:

    This paper considers iterative learning Control Law design using the theory of linear repetitive processes. This setting enables trial-to-trial error convergence and along-the-trial performance to be considered simultaneously in the design. It is also shown that this design extends naturally to include robustness to unmodeled plant dynamics. The results from experimental application of these Laws to a gantry robot performing a pick and place operation are given, together with a discussion of the positioning of this approach relative to alternatives and possible further research.

  • experimentally supported 2d systems based iterative learning Control Law design for error convergence and performance
    Control Engineering Practice, 2010
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers, Chris Freeman, P L Lewin
    Abstract:

    This paper considers iterative learning Control Law design for both trial-to-trial error convergence and along the trial performance. It is shown how a class of Control Laws can be designed using the theory of linear repetitive processes for this problem where the computations are in terms of linear matrix inequalities (LMIs). It is also shown how this setting extends to allow the design of robust Control Laws in the presence of uncertainty in the dynamics produced along the trials. Results from the experimental application of these Laws on a gantry robot performing a pick and place operation are also given.

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

  • repetitive process based design and experimental verification of a dynamic iterative learning Control Law
    Control Engineering Practice, 2016
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, Weronika Nowicka, E Rogers
    Abstract:

    This paper gives new results on iterative learning Control (ILC) design and experimental verification using the stability theory of linear repetitive processes. Using this theory a Control Law can be designed in one step to force error convergence and produce acceptable transient dynamics. Previous research developed algorithms for the design of a static Control Law with supporting experimental verification. Should a static Law not give the required levels of performance one option is to allow the Control Law to have internal dynamics. This paper develops a procedure for the design of such a Control Law with supporting experimental verification on a gantry robot, including a comparative performance against a static Law applied to the same robot. The resulting ILC design is an efficient combination of linear matrix inequalities and optimization algorithms.

  • Control Law design for discrete linear repetitive processes with non local updating structures
    Multidimensional Systems and Signal Processing, 2013
    Co-Authors: Blazej Cichy, Krzysztof Galkowski, E Rogers, Anton Kummert
    Abstract:

    Repetitive processes are a class of 2D systems where information propagation in one direction is of finite duration. These processes make a series of sweeps, termed passes, through a set of dynamics and on completion of each pass resetting to the starting position occurs ready for the start of the next pass. The Control problem is that the previous pass output, termed the pass profile, acts as a forcing function on the current pass and can result in oscillations that increase in amplitude from pass-to-pass. In the case of discrete dynamics, these processes have structural links with 2D systems described by the well known Roesser and Fornasini–Marchesini state-space models but some applications require updating structures that cannot be represented by these models. This requirement arises either in adequately modeling the dynamics or as a result of the Control Law structure and requires the development of a systems theory for eventual use in applications. In this paper such a theory is advanced through the development of new Control Law design algorithms.

  • kyp lemma based stability and Control Law design for differential linear repetitive processes with applications
    Systems & Control Letters, 2013
    Co-Authors: Wojciech Paszke, E Rogers, Krzysztof Galkowski
    Abstract:

    Repetitive processes are a class of two-dimensional systems that have physical applications, including the design of iterative learning Control Laws where experimental validation results have been reported. This paper uses the Kalman–Yakubovich–Popov lemma to develop new stability tests for differential linear repetitive processes that are computationally less intensive than those currently available. These tests are then extended to allow Control Law design for stability and performance.

  • output information based iterative learning Control Law design with experimental verification
    Journal of Dynamic Systems Measurement and Control-transactions of The Asme, 2012
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers, Chris Freeman, Zhonglun Cai, P L Lewin
    Abstract:

    This paper considers iterative learning Control Law design using the theory of linear repetitive processes. This setting enables trial-to-trial error convergence and along-the-trial performance to be considered simultaneously in the design. It is also shown that this design extends naturally to include robustness to unmodeled plant dynamics. The results from experimental application of these Laws to a gantry robot performing a pick and place operation are given, together with a discussion of the positioning of this approach relative to alternatives and possible further research.

  • experimentally supported 2d systems based iterative learning Control Law design for error convergence and performance
    Control Engineering Practice, 2010
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers, Chris Freeman, P L Lewin
    Abstract:

    This paper considers iterative learning Control Law design for both trial-to-trial error convergence and along the trial performance. It is shown how a class of Control Laws can be designed using the theory of linear repetitive processes for this problem where the computations are in terms of linear matrix inequalities (LMIs). It is also shown how this setting extends to allow the design of robust Control Laws in the presence of uncertainty in the dynamics produced along the trials. Results from the experimental application of these Laws on a gantry robot performing a pick and place operation are also given.

Lukasz Hladowski - One of the best experts on this subject based on the ideXlab platform.

  • repetitive process based design and experimental verification of a dynamic iterative learning Control Law
    Control Engineering Practice, 2016
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, Weronika Nowicka, E Rogers
    Abstract:

    This paper gives new results on iterative learning Control (ILC) design and experimental verification using the stability theory of linear repetitive processes. Using this theory a Control Law can be designed in one step to force error convergence and produce acceptable transient dynamics. Previous research developed algorithms for the design of a static Control Law with supporting experimental verification. Should a static Law not give the required levels of performance one option is to allow the Control Law to have internal dynamics. This paper develops a procedure for the design of such a Control Law with supporting experimental verification on a gantry robot, including a comparative performance against a static Law applied to the same robot. The resulting ILC design is an efficient combination of linear matrix inequalities and optimization algorithms.

  • output information based iterative learning Control Law design with experimental verification
    Journal of Dynamic Systems Measurement and Control-transactions of The Asme, 2012
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers, Chris Freeman, Zhonglun Cai, P L Lewin
    Abstract:

    This paper considers iterative learning Control Law design using the theory of linear repetitive processes. This setting enables trial-to-trial error convergence and along-the-trial performance to be considered simultaneously in the design. It is also shown that this design extends naturally to include robustness to unmodeled plant dynamics. The results from experimental application of these Laws to a gantry robot performing a pick and place operation are given, together with a discussion of the positioning of this approach relative to alternatives and possible further research.

  • experimentally supported 2d systems based iterative learning Control Law design for error convergence and performance
    Control Engineering Practice, 2010
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers, Chris Freeman, P L Lewin
    Abstract:

    This paper considers iterative learning Control Law design for both trial-to-trial error convergence and along the trial performance. It is shown how a class of Control Laws can be designed using the theory of linear repetitive processes for this problem where the computations are in terms of linear matrix inequalities (LMIs). It is also shown how this setting extends to allow the design of robust Control Laws in the presence of uncertainty in the dynamics produced along the trials. Results from the experimental application of these Laws on a gantry robot performing a pick and place operation are also given.

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

  • an iterative learning Control Law for dynamical systems
    Automatica, 1991
    Co-Authors: Toshiharu Sugie
    Abstract:

    Abstract This paper is concerned with an iterative learning Control Law which enables us to find a Control input that generates the desired output exactly over a finite time interval through the repetition of trials. We derive a sufficient condition for nonlinear systems to achieve the desired output by the iterative learning Control. Based on this result, we show that the direct transmission term of the plant plays a crucial role in the error convergence of the learning process. Further, we identify the class of plants to which the learning Control Law is applicable.

P L Lewin - One of the best experts on this subject based on the ideXlab platform.

  • output information based iterative learning Control Law design with experimental verification
    Journal of Dynamic Systems Measurement and Control-transactions of The Asme, 2012
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers, Chris Freeman, Zhonglun Cai, P L Lewin
    Abstract:

    This paper considers iterative learning Control Law design using the theory of linear repetitive processes. This setting enables trial-to-trial error convergence and along-the-trial performance to be considered simultaneously in the design. It is also shown that this design extends naturally to include robustness to unmodeled plant dynamics. The results from experimental application of these Laws to a gantry robot performing a pick and place operation are given, together with a discussion of the positioning of this approach relative to alternatives and possible further research.

  • experimentally supported 2d systems based iterative learning Control Law design for error convergence and performance
    Control Engineering Practice, 2010
    Co-Authors: Lukasz Hladowski, Krzysztof Galkowski, E Rogers, Chris Freeman, P L Lewin
    Abstract:

    This paper considers iterative learning Control Law design for both trial-to-trial error convergence and along the trial performance. It is shown how a class of Control Laws can be designed using the theory of linear repetitive processes for this problem where the computations are in terms of linear matrix inequalities (LMIs). It is also shown how this setting extends to allow the design of robust Control Laws in the presence of uncertainty in the dynamics produced along the trials. Results from the experimental application of these Laws on a gantry robot performing a pick and place operation are also given.