Method for designing self-adaptive PID controller based on inverse dynamics model

Method for designing self-adaptive PID controller based on inverse dynamics model

  • CN 101,673,085 A
  • Filed: 09/21/2009
  • Published: 03/17/2010
  • Est. Priority Date: 09/21/2009
  • Status: Active Application
First Claim
Patent Images

1. based on the method for designing of the self-adaptive PID controller of inverse dynamics model, it is characterized in that this method for designing comprises the steps:

  • 1. construct controlling object inverse dynamics fuzzy rule model structure, and the structure of the inverse dynamics fuzzy rule model input vector of the controlling object corresponding with the PID controller;

    Foundation comprises the controlling object inverse dynamics fuzzy rule Model Distinguish sample set (X) of N group data;

    In the inverse dynamics fuzzy rule model structure of described controlling object, comprise c bar fuzzy rule, i bar fuzzy rule (R wherein i) be;

    Ri;

    ifx(k)is[x&

    OverBar;

    i
    ,&

    mu;

    i
    (k)]
    thenui(k)=&

    theta;

    iT
    (k)x(k)
    Wherein, i=1,2 ..., c X in this fuzzy rule (k) is the input vector of controlling object inverse dynamics fuzzy rule model at current time (k);

    x iIt is the cluster centre vector of i cluster subspace;

    μ

    i(k) ∈

    [0,1] is the degree of membership of this input vector (x (k)) for i cluster subspace;

    C is the fuzzy clustering number of identification sample set (X);

    u i(k) be the output center of gravity of the i bar fuzzy rule corresponding with this input vector (x (k));

    This cluster centre vector (x i) and degree of membership (μ

    i(k)) be the former piece parameter of inverse dynamics fuzzy rule model to be identified;

    θ

    i(k) be the consequent parameter vector of inverse dynamics fuzzy rule model to be identified;

    The structure of the input vector of this inverse dynamics fuzzy rule model (x (k)) is determined that by pid control algorithm its structural formula is;

    x(k)=

    [y(k),

    y(k-1),

    y(k-2)]Wherein, y (k), y (k-1) and y (k-2) are respectively the output valve of the controlling object in two moment (k-2) before the previous moment (k-1) of current time (k), current time and the current time;

    The identification sample set (X) of described controlling object inverse dynamics fuzzy rule model comprises controlling object in difference N group data constantly, and this sample set (X) is pressed the following formula structure;

    X={x (k-i), u (k-i-1) } wherein, and i=1,2 ..., N;

    2. to the identification sample set (X) of the controlling object inverse dynamics fuzzy rule model set up, the former piece with FCM algorithm identification controlling object inverse dynamics fuzzy rule model obtains described cluster centre vector (x i) and degree of membership (μ

    i(k)) value;

    3. according to the error (e (k-1)) of controlling object inverse dynamics fuzzy rule model, the consequent with RLS algorithm identification controlling object inverse dynamics fuzzy rule model obtains described consequent parameter vector (θ

    i(k)) value;

    The error of this controlling object inverse dynamics fuzzy rule model (e (k-1)) is determined by following formula;

View all claims
    ×
    ×

    Thank you for your feedback

    ×
    ×