tp_dem_c.m

FISMAT accommodates different arithmetic operators, fuzzification and defuzzification algorithm, imp

function do = tp_dem_c(e,x,Ts)

% do = tp_dem_c(e,x,Ts)
%
% Temperature control demo with the fuzzy toolbox.
%
% This controller relates to the fuzdemo3 of the fuzzy-toolbox.
% The corresponding Simulink block diagram: tp_dem_s.m
%
% FSTB - Fuzzy Systems Toolbox for MATLAB
% Copyright (c) 1993-1996 by Olaf Wolkenhauer
% Control Systems Centre at UMIST
% Manchester M60 1QD, UK
%
% 22-April-1994
 
global E ERROR  R RATE P MOMENTS AREAS

% Fuzzification: (e(1)=error e(2)=rate of change
Ae  = match(E,e(1),ERROR);
Ade = match(R,e(2),RATE);
% Ae is a row vector containing fit-values to which e(1) fits to the sets.

NB=1; NM=2; NS=3; ZE=4; PS=5; PM=6; PB=7;

% e ->    NB NM NS ZE PS PM PB
FAMbank= [0  0  0  PB 0  0  0   ; % NB  | rate of error
          0  0  0  PM 0  0  0   ; % NM  v
          0  0  0  PS 0  0  0   ; % NS
          PB PM PS ZE NS NM NB  ; % ZE
          0  0  0  NS 0  0  0   ; % PS
          0  0  0  NM 0  0  0   ; % PM
          0  0  0  NB 0  0  0  ]; % PB

aggop='t2ap';
  % algebraic-product

A=aggregat(Ae,Ade,FAMbank,aggop);
% A = [a1, a2,...,aj,...,am] with aj=aggop(aj1,aj2,...,aji,...,ajn).

% At this stage each rule could be weighted by a certainty factor cj between
% 0 and 1. This is realized by a multiplication or aj*=min(aj,cj). 
% An adaptive fuzzy system could use this factor to change the structure.

% Using the algebraic-product for the inference and a summation of the 
% outputs. Center Of Gravity method:

do = apsumcog(FAMbank,A,MOMENTS,AREAS);
% Alternative - Height Method:
% do=apsumhei(FAMbank,A,MOMENTS,AREAS);