www.gusucode.com > mpc_featured 案例源码 matlab代码程序 > mpc_featured/mpconlinetuning.m
%% Tuning Controller Weights % This example shows how to vary the weights on outputs, inputs, and ECR % slack variable for soft constraints in real-time. % % The weights specified in the MPC object are overridden by the weights % supplied to the MPC Controller block. If a weight signal is not connected % to the MPC Controller block, then the corresponding weight is the one % specified in the MPC object. % Copyright 1990-2014 The MathWorks, Inc. %% Define Plant Model % Define a multivariable discrete-time linear system with no direct I/O % feedthrough, and assume input #4 is a measured disturbance and output #4 % is unmeasured. Ts = 0.1; % sampling time plant = tf({1,[1 1],5,2;3,[1 5],1,0;0,0,1,[1 1];2,[1 -1],0,0},... {[1 1 1],[1 3 4 5],[1 10],[1 5]; [1 1],[1 2],[1 2 8],[1 1]; [1 2 1],[1 3 1 1],[1 1],[1 2]; [1 1],[1 3 10 10],[1 10],[1 1]}); plant = c2d(ss(plant),Ts); plant.D = 0; %% Design MPC Controller % Specify input and output signal types. plant = setmpcsignals(plant,'MD',4,'UO',4); % Create the controller object with sampling period, prediction and control % horizons: p = 20; % Prediction horizon m = 3; % Control horizon mpcobj = mpc(plant,Ts,p,m); %% % Specify MV constraints. mpcobj.MV(1).Min = -6; mpcobj.MV(1).Max = 6; mpcobj.MV(2).Min = -6; mpcobj.MV(2).Max = 6; mpcobj.MV(3).Min = -6; mpcobj.MV(3).Max = 6; %% Simulate Using Simulink(R) % To run this example, Simulink(R) is required. if ~mpcchecktoolboxinstalled('simulink') disp('Simulink(R) is required to run this example.') return end % Define reference signal. Tstop = 10; ref = [1 0 3 1]; r = struct('time',(0:Ts:Tstop)'); N = numel(r.time); r.signals.values=ones(N,1)*ref; %% % Define measured disturbance. v = 0.5; %% % OV weights are linearly increasing with time, except for output #2 % that is not weighted. ywt.time = r.time; ywt.signals.values = (1:N)'*[.1 0 .1 .1]; %% % MVRate weights are decreasing linearly with time. duwt.time = r.time; duwt.signals.values = (1-(1:N)/2/N)'*[.1 .1 .1]; %% % ECR weight increases exponentially with time. ECRwt.time = r.time; ECRwt.signals.values = 10.^(2+(1:N)'/N); %% % Start simulation. mdl = 'mpc_onlinetuning'; open_system(mdl); % Open Simulink(R) Model sim(mdl); % Start Simulation %% Simulate Using MPCMOVE Command % Define real plant and MPC state object. [A,B,C,D] = ssdata(plant); x = zeros(size(plant.B,1),1); % Initial state of the plant xmpc = mpcstate(mpcobj); % Initial state of the MPC controller %% % Store the closed-loop MPC trajectories in arrays YY,UU,XX. YY = []; UU = []; XX = []; %% % Use MPCMOVEOPT object to provide weights at run-time. options = mpcmoveopt; %% % Start simulation. for t = 0:N-1, % Store states XX = [XX,x]; %#ok<*AGROW> % Compute plant output (no feedthrough from MV to Y) y = C*x+D(:,4)*v; YY = [YY;y']; % Obtain reference signal ref = r.signals.values(t+1,:)'; % Update MPCMOVEOPT object with run-time weights options.MVRateWeight = duwt.signals.values(t+1,:); options.OutputWeight = ywt.signals.values(t+1,:); options.ECRWeight = ECRwt.signals.values(t+1,:); % Compute control action u = mpcmove(mpcobj,xmpc,y(1:3),ref,v,options); UU = [UU;u']; % Update plant states x = A*x + B(:,1:3)*u + B(:,4)*v; end %% % Plot and Compare Simulation Results figure(1); clf; subplot(121) plot(0:Ts:Tstop,[YY ysim]) grid title('output') subplot(122) plot(0:Ts:Tstop,[UU usim]) grid title('input') %% % Simulation results are the same. fprintf('\n\nDifference between MPC Simulink block and MPCMOVE simulations: %g',norm(UU-usim)+norm(YY-ysim)); %% bdclose(mdl);