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细菌觅食算法

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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%

% This program implements an indirect

% adaptive controller based on an E. coli chemotactic

% foraging strategy for the surge tank example.

%

% Kevin Passino

% Version: 9/27/00

%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Initialize variables

clear

rand('state',0) % Reset the random number generator so each time you re-

run

% the program with no changes you will get the same

results.

% We assume that the parameters of the tank are:

abar=0.01; % Parameter characterizing tank shape (nominal value is

0.01)

bbar=0.2; % Parameter characterizing tank shape (nominal value is 0.2)

cbar=1; % Clogging factor representing dirty filter in pump

(nominally, 1)

dbar=1; % Related to diameter of output pipe

g=9.8; % Gravity

T=0.1; % Sampling rate

beta0=0.25; % Set known lower bound on betahat

beta1=0.5; % and the upper bound

% Set the length of the simulation (here, also the number of chemotactic

steps)

Nnc=1000;

% As a reference input, we use a square wave (define one extra

% point since at the last time we need the reference value at

% the last time plus one)

timeref=1:Nnc+1;

r(timeref)=3.25-3*square((2*pi/150)*timeref); % A square wave input

%r(timeref)=3.25*ones(1,Nnc+1)-3*(2*rand(1,Nnc+1)-ones(1,Nnc+1)); % A

noise input

ref=r(1:Nnc); % Then, use this one for plotting

time=1:Nnc;

time=T*time; % Next, make the vector real time

% Next, set plant initial conditions

h(1)=1; % Initial liquid level

e(1)=r(1)-h(1); % Initial error

A(1)=abs(abar*h(1)+bbar);

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