The file *Demo_009.m* is found in IQClabâ€™s folder *demos*; it performs a robust controller synthesis with the function *fRobsyn*. This demo consists of the 2DoF control design for a plant with parametric uncertainty. The plant, , is a simple oscillator model, which is interconnected as shown in the following figure.

Here is defined by the transfer function

where is the time-constant and the damping factor. Here is a fixed constant, while is assumed to be an uncertain parameter with and . The bode diagram for various samples of is shown in the following figure.

The goal is to design a 2DoF controller, , which stabilizes the plant for all , and which renders the induced -gain on the weighted performance channels satisfied. Here the aim is to enforce good tracking at low frequencies, while penalizing control at high frequencies.

This is accomplished with the performance weights

The generalized plant can be constructed as usual (see code in *Demo_009.m*). Subsequently, you can perform a nominal and robust controller synthesis by means of *hinfsyn/fHinfsyn* and *fRobsyn* respectively. The corresponding code is given next.

- Length of the basis function: 3
- Solution check: ‘on’
- Enforce strictness of the LMIs:

% Define the uncertain plant model k0 = 1; d = 0.1; delta = ureal('delta',0,'Range',[-1,1],'AutoSimplify','full'); k = k0*(1+0.4*delta); G = tf(1,[k^2,2*d*k,1]); [H,De] = lftdata(G); H = ssbal(H); % Define the open-loop generalized plant systemnames = 'H'; inputvar = '[p{2};r;u]'; outputvar = '[H(1:2);r-H(3);u;r;H(3)]'; input_to_H = '[p(1:2);u]'; cleanupsysic = 'yes'; olic = sysic; % Define weights Wperf = 1/makeweight(1e-3,1,2); Wcmd = makeweight(1e-1,10,10); Wout = blkdiag(1,1,Wperf,Wcmd,1,1); wolic = Wout*olic; % Perform a nominal controller synthesis [Knom,~,ga_nom] = fHinfsyn(wolic(3:end,3:end),2,1,'method','lmi'); % Define iqc uncertainty block de = iqcdelta('de','InputChannel',1:2, 'OutputChannel',1:2,'Bounds',[-1,1]); de = iqcassign(de,'ultis','Length',2); % perform a robust controller synthesis options.maxiter = 5; options.subopt = 1.01; options.constants = 1e-6*ones(1,3); options.Pi11const = 1e-5; options.FeasbRad = 1e5; [K,ga] = fRobsyn(wolic,de,[2,2,2],[2,1,1],options);

For the nominal controller, , we obtain an -norm of approximately 1. However, when performing an IQC-analysis, the worst-case performance is in fact about 10 times larger. The robust controller, , on the other hand, guarantees that the worst-case performance is not larger than 1.37, which is much closer to nominal performance level. This is illustrated in the following two figures. As can be seen in the first figure, the function *fRobsyn* quickly converges after four iterations to a worst-case performance of 1.37. This is confirmed by the second figure, where the sigma plots of the weighted closed loop system are shown for various samples of for both the nominal controller, as well as the robust controller .

To continue, the following figure shows the singular value plots of the sensitivity and control sensitivity function and respectively, for various samples of versus the inverse weighting functions that were used in the controller synthesis. Again, we conclude that the robust controller, is better capable of guaranteeing performance in the presence of the uncertainty. This is confirmed by the time domain simulation shown in the last figure.