www.gusucode.com > rctobsolete 工具箱 matlab源码程序 > rctobsolete/robust/ltrdata2.m
% ABOUT LTRDEMO2: % % This demo provides a SISO closed loop control system designed % by using the Robust Control Toolbox. Use the pulldown menus to run % the simulation. Double-click the blocks on the bottom for % more functions. % % All of the parameters are read in from MATLAB workspace variables. % The plant model is [a,b,c,d], Q and R are the LQ regulator % design weighting matrices, and Th and Xi are loop % transfer recovery weighting matrices. % % The designed controller is given by [ae,be,ce,de]. In the simulation % both system perturbation (additive perturbation and multiplicative % perturbation) and measurement noise are added. % % By changing the plant and the weighting function parameters, you % can convert the example to solve a problem of your own. % % Re-Load Data % Re-load data from file. This refreshes the data in the workspace. % % Re-Design % After changing the workspace parameters, you should redesign the % controller to fit your data. % % In the design, the following command in the Robust Control Toolbox % is used: ltry --- LQG loop transfer recovery controller design % % A MIMO control system can be designed using a similar structure. % A simpler simulation is shown in LTRDEMO1 % % Copyright 1988-2004 The MathWorks, Inc. % pre-calculated data for ltrdemo2 disp('loading LTR data...') a = [ -1.0285 0.9853 -0.9413 0. -1.2903 -1.0957 2.8689 1.5 0.1871 -3.8184 -2.0788 -.2 0.4069 -4.1636 2.5407 3.23]; b = [ 0 6.6389 0 0]; c = [ -1.7786 1.1390 0 -1.0294]; d = 0.000001; Q = 0.1*c'*c; R = eye(1,1); Xi = 0.1 * b * b'; Th = eye(1,1); ae= 1.0e+03 *... [ -0.2500 0.1798 -0.0240 -0.2120 2.6920 -1.9609 0.2792 2.3823 3.3233 -2.3905 0.3057 2.8303 -5.0789 3.6439 -0.4679 -4.3232]; be=[-0.1217 -0.2869 1.6246 -2.4833]; ce=1.0e+04 * [-4.9402 3.5863 -0.4981 -4.3384]; de=[ 0]; disp('Done')