基于Matlab和Simulink实现热交换器的PI与DMC控制仿真模型（源码+数据）.rar

function [sys,x0,str,ts] = dmcsfun(t,x,u,flag,varargin) %DMCSFUN S-Function for Dynamic Matrix Control. % % persistent K switch flag, %%%%%%%%%%%%%%%%%% % Initialization % %%%%%%%%%%%%%%%%%% case 0, Ts=varargin{1}; % Sampling time K.sr=varargin{2}; % Step Response Model K.p=varargin{3}; % P, prediction horizon K.m=varargin{4}; % M, moving horizon K.v=[]; % History of control K.la=varargin{5}; % input weight, i.e. J = ||r-y|| + p.la*||du|| if numel(varargin)>5 K.a=varargin{6}; % reference smooth factor else K.a=0; end K.y=[]; % Initialization DMC K=dmc(K); sizes = simsizes; sizes.NumContStates = 0; sizes.NumDiscStates = 0; sizes.NumOutputs = 1; sizes.NumInputs = 2; % measured output and setpoint sizes.DirFeedthrough = 1; sizes.NumSampleTimes = 1; sys = simsizes(sizes); str = []; x0 = zeros(0,1); ts = [Ts 0]; %%%%%%%%%% % Update % %%%%%%%%%% case 2, sys = x; %%%%%%%%%% % Output % %%%%%%%%%% case 3, % if t>50 % u; % end K.y = u(1); % measurement K.r = u(2); % setpoint K = dmc(K); sys = K.u; %%%%%%%%%%%%% % Terminate % %%%%%%%%%%%%% case 9, sys = []; % do nothing %%%%%%%%%%%%%%%%%%%% % Unexpected flags % %%%%%%%%%%%%%%%%%%%% otherwise error(['unhandled flag = ',num2str(flag)]); end function p=dmc(p) % DMC Dynamic Matrix Control % P=DMC(P) determines the dynamic matrix control (input change) based on % the plant model (step response) and current measurement stored in the % structure P. % Input: % P.sr - unit step response data % P.u - current input, initially 0 % P.v - past input, initially empty % P.G - dynamic matrix, set by the initial call % P.F - matrix to calculate free response, set by the initial call % P.k - DMC gain, set by the initial call % P.r - reference (set point) % P.a - reference smooth factor % P.p - prediction horizon % P.m - moving horizon % P.y - current mrasurement % P.la - performance criterion weight, i.e. J = ||r-y|| + p.la*||du|| % where du is the input change % Output: % P.u - new input for next step % P.f - updated free response % P.G - dynamic matrix, if it is the first step. % P.k - DMC gain, if it is the first step % % See Also: mpc % Version 1.0 created by Yi Cao at Cranfield University on 6th April 2008. % Example: %{ p.sr=filter([0 0 0.2713],[1 -0.8351],ones(50,1)); p.p=10; p.m=5; p.y=0; p.v=[]; u=zeros(1,3); N=120; Y=zeros(N,1); U=zeros(N,1); R=zeros(N,1); R([1:30 61:90])=1; p.la=1; for k=1:120 p.a=0; p.r=R(k:min(N,k+p.p)); if k>60 p.a=0.7; end p=dmc(p); Y(k)=p.y; U(k)=p.u; u=[u(2:3) p.u]; p.y=0.8351*p.y+0.2713*u(1); end subplot(211) plot(1:N,Y,'b-',1:N,R,'r--',[60 60],[-0.5 1.5],':','linewidth',2) title('solid: output, dashed: reference') text(35,1,'\alpha=0') text(95,1,'\alpha=0.7') axis([0 120 -0.5 1.5]) subplot(212) [xx,yy]=stairs(1:N,U); plot(xx,yy,'-',[60 60],[-0.5 1.5],':','linewidth',2) axis([0 120 -0.5 1.5]) title('input') xlabel('time, min') %} % Input and output check error(nargchk(1,1,nargin)); error(nargoutchk(0,1,nargout)); % length of step response N=numel(p.sr); P=p.p; % initial setup if isempty(p.v) % number of past inputs to keep n=N-P; % storage for past input p.v=zeros(n,1); % matrix to calculate free response from past input x=p.sr(1:n); p.F=hankel(p.sr(2:P+1),p.sr(P+1:N))-repmat(x(:)',P,1); % dynamic matrix p.G=toeplitz(p.sr(1:P),p.sr(1)*eye(1,p.m)); % calculate DMC gain R=chol(p.G'*p.G+p.la*eye(p.m)); K=R\(R'\p.G'); % only the first input will be used p.k=K(1,:); p.u=0; end if isempty(p.y) return end % free response f=p.F*p.v+p.y; % smooth reference nr=numel(p.r); if nr>=P ref=p.r(1:P); else ref=[p.r(:);p.r(end)+zeros(P-nr,1)]; end w=filter([0 (1-p.a)],[1 -p.a],ref,p.y); % DMC input change u=p.k*(w-f); % past input change for next step p.v=[u;p.v(1:end-1)]; % next input p.u=p.u+u(1);