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Adaptive Control Systems

The Experts below are selected from a list of 219 Experts worldwide ranked by ideXlab platform

Hiromitsu Ohmori – 1st expert on this subject based on the ideXlab platform

  • Design of model reference Adaptive Control Systems with fractional order Adaptive law and its lyapunov stability
    Proceedings of the 33rd Chinese Control Conference, 2014
    Co-Authors: Takahiro Takamatsu, Hiromitsu Ohmori

    Abstract:

    Model reference Adaptive Control Systems (MRACS) is the one of the useful Systems to Control the plant containing unknown variable parameters. However, to design MRACS, it has been needed to satisfy model matching condition. In this paper, to improve the response in the case unsatisfying model matching condition, we design an Adaptive law containing fractional order integrator in Adaptive law, and prove the system’s Lyapnov stability.

  • Design of model reference Adaptive Control Systems based on virtual error method
    SICE Annual Conference 2011, 2011
    Co-Authors: Yuta Kochiyama, Hiromitsu Ohmori

    Abstract:

    Systems that are Controlled as following to the ideal response hoped for it are called Model Reference Adaptive Control Systems (MRACS). The design method of MRACS for discrete-time Systems under ideal condition has been already proposed, and a variety of application examples has been presented. These method require the accurate order of error transfer functions for the stability. This paper proposes a design of the Adaptive Control Systems based on virtual error approach for the discrete-time plants. This method enables the proof of the stability without the accurate order of the error transfer function.

  • CDC – A design method of discrete-time Adaptive Control Systems based on immersion and invariance
    2008 47th IEEE Conference on Decision and Control, 2008
    Co-Authors: Yuta Katakura, Hiromitsu Ohmori

    Abstract:

    Adaptive Control Systems are designed to achieve desired Control performance when plant parameters gains are unknown, or possibly slowly changing. Highly calculation technology is developed, more important discrete-time Adaptive Control structure is. Because the relative degree condition for strict positive realness (SPR) of the discrete-time error transfer function is different from the condition of the continuous time case, it is hard to prove the stability of the discretized continuous-time Adaptive Control Systems. The main contribution of this paper is the extension of an immersion and invariance (I&I)-based Adaptive Control algorithm from continuous to discrete time. The theoretical stability of the proposed discrete time I&I-based Adaptive Control system is proved. In order to show the effectiveness of the proposed method, numerical simulations are shown.

C. Shao – 2nd expert on this subject based on the ideXlab platform

  • A sufficient condition for robust stability analysis of Adaptive Control Systems
    [1992] Proceedings of the 31st IEEE Conference on Decision and Control, 1992
    Co-Authors: X.y. Gu, C. Shao

    Abstract:

    A sufficient condition is proposed to ensure the uniform boundedness of the solutions of a class of discrete-time dynamic Systems which result from the robust stability analysis of Adaptive Control Systems in the presence of unmodeled dynamics, bounded disturbances and time-varying parameters.

V. Sreeram – 3rd expert on this subject based on the ideXlab platform

  • Robust Control design of nonlinear Adaptive Control Systems with unmodelled dynamics
    Proceedings of the 36th IEEE Conference on Decision and Control, 1997
    Co-Authors: V. Sreeram

    Abstract:

    In this paper, the robust Adaptive Control problem for a class nonlinear Systems is studied in which the unknown system parameters are not required to enter the system linearly. A new design scheme for the construction of Control for the nonlinear Adaptive Control problem is devised by using the backstepping and variable structure techniques. The Control obtained will suppress the unmodeled dynamics while regulate the corresponding closed loop system to a small neighborhood of the origin globally.