The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

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

  • stable Multi Input Multi output adaptive fuzzy neural control
    IEEE Transactions on Fuzzy Systems, 1999
    Co-Authors: Raul Ordonez, K.m. Passino
    Abstract:

    In this letter, stable direct and indirect adaptive controllers are presented that use Takagi-Sugeno (T-S) fuzzy systems (1985), conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time Multi-Input Multi-output (MIMO) square nonlinear plants with poorly understood dynamics. The direct adaptive scheme allows for the inclusion of a priori knowledge about the control Input in terms of exact mathematical equations or linguistics, while the indirect adaptive controller permits the explicit use of equations to represent portions of the plant dynamics. We prove that with or without such knowledge the adaptive schemes can "learn" how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of the reference Inputs. We do not impose any initialization conditions on the controllers and guarantee convergence of the tracking error to zero.

  • Stable Multi-Input Multi-output direct adaptive fuzzy control
    Proceedings of the 1997 American Control Conference (Cat. No.97CH36041), 1997
    Co-Authors: R. Ordonaz, K.m. Passino
    Abstract:

    A stable direct adaptive controller is presented which uses fuzzy systems or a neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time Multi-Input Multi-output (MIMO) square nonlinear plants.

  • Stable Multi-Input Multi-output adaptive fuzzy control
    Proceedings of 35th IEEE Conference on Decision and Control, 1
    Co-Authors: Raul Ordonez, J.t. Spooner, K.m. Passino
    Abstract:

    A stable indirect adaptive controller is presented which uses Takagi-Sugeno fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time Multi-Input Multi-output (MIMO) square nonlinear plants with poorly understood dynamics. The adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of exact mathematical equations or linguistics. We prove that with or without such knowledge the adaptive scheme can "learn" how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of the reference Inputs. We do not impose any initialization conditions on the controller, and guarantee convergence of the tracking error to zero.

Dimitri Lefebvre - One of the best experts on this subject based on the ideXlab platform.

  • ECC - An adaptive neural controller based on neural emulator for single-Input Multi-output nonlinear systems
    2015 European Control Conference (ECC), 2015
    Co-Authors: Nesrine Bahri, Fabrice Druaux, Asma Atig, Ridha Ben Abdennour, Dimitri Lefebvre
    Abstract:

    This paper deals with the adaptive control of single-Input Multi-output (SIMO) underactuated nonlinear systems. The restriction of the control authority for these systems causes major difficulties in control design. In this work, we propose an adaptive neural controller based on neural emulator to solve the control problems for a class of SIMO nonlinear systems. This controller is built by a set of partial neural controllers. New formulas are proposed to compute the validity degrees which manage the generation of the global control law. Simulations are carried out on a single-Input Multi-output nonlinear system. The obtained results show that the suggested control scheme provides a very good tracking and regulation performance.

Raul Ordonez - One of the best experts on this subject based on the ideXlab platform.

  • stable Multi Input Multi output adaptive fuzzy neural control
    IEEE Transactions on Fuzzy Systems, 1999
    Co-Authors: Raul Ordonez, K.m. Passino
    Abstract:

    In this letter, stable direct and indirect adaptive controllers are presented that use Takagi-Sugeno (T-S) fuzzy systems (1985), conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time Multi-Input Multi-output (MIMO) square nonlinear plants with poorly understood dynamics. The direct adaptive scheme allows for the inclusion of a priori knowledge about the control Input in terms of exact mathematical equations or linguistics, while the indirect adaptive controller permits the explicit use of equations to represent portions of the plant dynamics. We prove that with or without such knowledge the adaptive schemes can "learn" how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of the reference Inputs. We do not impose any initialization conditions on the controllers and guarantee convergence of the tracking error to zero.

  • Stable Multi-Input Multi-output adaptive fuzzy control
    Proceedings of 35th IEEE Conference on Decision and Control, 1
    Co-Authors: Raul Ordonez, J.t. Spooner, K.m. Passino
    Abstract:

    A stable indirect adaptive controller is presented which uses Takagi-Sugeno fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time Multi-Input Multi-output (MIMO) square nonlinear plants with poorly understood dynamics. The adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of exact mathematical equations or linguistics. We prove that with or without such knowledge the adaptive scheme can "learn" how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of the reference Inputs. We do not impose any initialization conditions on the controller, and guarantee convergence of the tracking error to zero.

Nesrine Bahri - One of the best experts on this subject based on the ideXlab platform.

  • ECC - An adaptive neural controller based on neural emulator for single-Input Multi-output nonlinear systems
    2015 European Control Conference (ECC), 2015
    Co-Authors: Nesrine Bahri, Fabrice Druaux, Asma Atig, Ridha Ben Abdennour, Dimitri Lefebvre
    Abstract:

    This paper deals with the adaptive control of single-Input Multi-output (SIMO) underactuated nonlinear systems. The restriction of the control authority for these systems causes major difficulties in control design. In this work, we propose an adaptive neural controller based on neural emulator to solve the control problems for a class of SIMO nonlinear systems. This controller is built by a set of partial neural controllers. New formulas are proposed to compute the validity degrees which manage the generation of the global control law. Simulations are carried out on a single-Input Multi-output nonlinear system. The obtained results show that the suggested control scheme provides a very good tracking and regulation performance.

Ridha Ben Abdennour - One of the best experts on this subject based on the ideXlab platform.

  • ECC - An adaptive neural controller based on neural emulator for single-Input Multi-output nonlinear systems
    2015 European Control Conference (ECC), 2015
    Co-Authors: Nesrine Bahri, Fabrice Druaux, Asma Atig, Ridha Ben Abdennour, Dimitri Lefebvre
    Abstract:

    This paper deals with the adaptive control of single-Input Multi-output (SIMO) underactuated nonlinear systems. The restriction of the control authority for these systems causes major difficulties in control design. In this work, we propose an adaptive neural controller based on neural emulator to solve the control problems for a class of SIMO nonlinear systems. This controller is built by a set of partial neural controllers. New formulas are proposed to compute the validity degrees which manage the generation of the global control law. Simulations are carried out on a single-Input Multi-output nonlinear system. The obtained results show that the suggested control scheme provides a very good tracking and regulation performance.