Estimated MIMO feedback control in Simulink

I've used the full-state Luenberger observer successfully in my control problems, using Digital Control of Dynamic Systems, 2nd edition, Franklin et. al., chapter 6 as my reference.  The key block diagram that allows for control commands and current estimation is this:

Although this structure does NOT have error integral portion and may therefore have a non-zero steady-state error, the later part of the above mentioned chapter does give the modified solution, and besides in my experience, integral control ALWAYS requires anti-windup correction, so the simpler feedback gain formulation given above is a good first attempt at a non-full-state-feedback MIMO (multi-input, multi-output) control problem.

I in fact learned about the observers from Control System Design Guide, George Ellis, who to his credit, tried to make a dent against the perversely pedantic approach to control engineering, by creating an excellent control system simulation software (for nearly free!).  In my opinion, for SISO (single-input-single-output) design, the Visual Model Q is superior to Matlab/Simulink because it helps (forces?) me to understand what is going on under the hood.

Unfortunately, I have to solve MIMO control, estimation, and optimization problems, and Matlab toolboxes are a faster way to tackle those problems.  And after avoiding Simulink all these years, I finally bought into it recently.  Part of the entry fee into the Simulink was implementing the above block diagram in Simulink.  Here is my first attempt (where problem specific--proprietary--labels are erased):

The predictor that is driven by U (control input into the openloop model) and Y (measurement, or observation) is fairly standard, with the predictor's system matrix A modified to (A - Lp C), and the B matrix B modified to [B Lp], where Lp is the predictor feedback gain.  The 2 inputs on the left is the R vector to follow, the 2 independent commands in this problem.

But there are some important differences:

• I did not put in Nx, because in this problem, Nx = I.  But adding a matrix gain between R and the difference junction (circle above) is easy; its gain parameter will just be the Nx matrix, available in Matlab workspace.
• Feedback is calculated only on the subset of the full Xest, so I use another gain (a 2x5 matrix consisting of only 2 rows from a 5x5 identity matrix).
• Because controller saturation is so important, I explicitly modeled it.  In fact, when the saturation limit varies as a function of the states, I modeled that relationship as well, and feed Simulink's dynamic saturation block.

Future work

Note that I don't deal with the system or the measurement noise, by using the Kalman filter, for example.  The full-state predictor/estimator in fact averages out noise nicely, but the Kalman filter will dynamically adjust the optimal weight for the current vs. past estimates.