## Robust Control Toolbox |

This example shows how to use Robust Control Toolbox™ to analyze the robustness of an uncertain system with only real parametric uncertainty. You compute the stability margins for a rigid body transport aircraft using an output feedback control law. For more information about the model, see "A Practical Approach to Robustness Analysis with Aeronautical Applications" by G. Ferreres. Stability analysis for systems with only real parametric uncertainty can cause numerical difficulties. In this example, you compare three methods for computing the stability margins for systems with only real parametric uncertainty.

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Creating An Uncertain Model for a Transport Aircraft Creating the Closed Loop System Stability Analysis: Power Iteration |

**Creating An Uncertain Model for a Transport Aircraft**

The rigid body model of a large transport aircraft has four states, two inputs, and four outputs. The states are [sideslip (beta); roll rate (p); yaw rate (r); roll angle (phi)]. The inputs are [rudder deflection (deltap); aileron deflection (deltar)]. The outputs are [lateral acceleration (ny); roll rate (p); yaw rate (r); roll angle(phi)]. The state equations depend on 14 aerodynamic coefficients, where each coefficient has 10 percent uncertainty.

Create the uncertainties for the aerodynamic coefficients.

rng(1) deg2rad = pi/180; % conversion factor from degs to radians rad2deg = 1/deg2rad; % conversion factor from radians to degs gV = 0.146418; % g/V tan_theta0 = 0.14; % tan(theta0) alpha0 = 8*deg2rad; % (rad) Ybeta = ureal('Ybeta',-0.082,'Percentage',10); Yp = ureal('Yp',0.010827,'Percentage',10); Yr = ureal('Yr',0.060268,'Percentage',10); Ydeltap = ureal('Ydeltap',0.002,'Percentage',10); Ydeltar = ureal('Ydeltar',0.0118,'Percentage',10); Lbeta = ureal('Lbeta',-0.84,'Percentage',10); Lp = ureal('Lp',-0.76,'Percentage',10); Lr = ureal('Lr',0.74,'Percentage',10); Ldeltap = ureal('Ldeltap',0.095,'Percentage',10); Ldeltar = ureal('Ldeltar',0.06,'Percentage',10); Nbeta = ureal('Nbeta',0.092,'Percentage',10); Np = ureal('Np',-0.23,'Percentage',10); Nr = ureal('Nr',-0.114,'Percentage',10); Ndeltar = ureal('Ndeltar',-0.151,'Percentage',10);

The state equations for the rigid body aircraft dynamics are:

```
A = [Ybeta (Yp+sin(alpha0)) (Yr-cos(alpha0)) gV; ...
Lbeta Lp Lr 0; Nbeta Np Nr 0; 0 1 tan_theta0 0];
B = [Ydeltap Ydeltar; Ldeltap Ldeltar; 0 Ndeltar; 0 0];
C = -1/gV*deg2rad*[Ybeta Yp Yr 0];
C = [C; zeros(3,1) eye(3)];
D = -1/gV*deg2rad*[Ydeltap Ydeltar];
D = [D; zeros(3,2)];
AIRCRAFT = ss(A,B,C,D);
```

The aircraft model has actuators for the rudder and the aileron. Each actuator is modeled using a second-order system and the resulting dynamics are added to the input of the rigid body model. |P| is the open-loop model of the aircraft and the actuators.

N1 = [-1.77, 399]; D1 = [1 48.2 399]; deltap_act = tf(N1,D1); N2 = [2.6 -1185 27350]; D2 = [1 77.7 3331 27350]; deltar_act = tf(N2,D2); P = AIRCRAFT*blkdiag(deltap_act,deltar_act);

**Creating the Closed Loop System**

A constant output feedback control law is used and the closed loop is created with the feedback command.

```
K = [-629.8858 11.5254 3.3110 9.4278; ...
285.9496 0.3693 -2.6301 -0.5489];
CLOOP = feedback(P,K);
```

**Stability Analysis: Power Iteration**

You can use `robuststab` to compute stability margins for this system. This example focuses on the methods to compute lower bounds on `mu`, which is equivalent to computing the upper bound on the stability margin. For details on robust stability analysis using `robuststab`, see the `mu` example. The default option for `robuststab` is to use a combination of power iteration and gain-based lower bounds. First, examine power iteration. The 'm' option in `robuststabOptions` is used to force `robuststab` to use power iteration.

ropt = robuststabOptions('Mussv','m5','Sensitivity','off'); [STABMARG1,DESTABUNC1,REPORT1,INFO1] = robuststab(CLOOP,ropt);

Power iteration is fast and typically provides good bounds for problems with complex uncertainty. However, it tends to perform very poorly for systems with only real parametric uncertainty. For this example, power iteration typically finds no lower bounds for `mu`. Thus, the stability margin upper bound provides no information.

REPORT1 opt = bodeoptions; opt.MagUnits = 'Abs'; opt.PhaseVisible = 'off'; bodeplot(INFO1.MussvBnds(:,1),INFO1.MussvBnds(:,2),opt)

REPORT1 = Uncertain system is robustly stable to modeled uncertainty. -- It can tolerate up to 423% of the modeled uncertainty. -- A destabilizing combination of 547% of the modeled uncertainty was found. -- This combination causes an instability at 0.619 rad/seconds.

**Figure 1:** Mu bounds for Aircraft using power iteration lower bound.

**Stability Analysis: Complexify the Real Uncertainty**

One way to regularize this robust stability problem is to add a small amount of complex uncertainty to the real parametric uncertainty using the `complexify` command. Increasing `alpha` increases the complexity of the problem.

alpha = 0.05; CLOOP_c = complexify(CLOOP,alpha);

There is a trade-off to adding complexity with `complexify`. Increasing complexity (`alpha`) improves the conditioning of the lower bound power iteration, thus increasing the chance of convergence. The `icomplexify` command extracts approximately-destabilizing real values of uncertainty from the destabilizing complexified version. However, if you choose `alpha` that is too large, then the complexity changes the problem and decreases the chance that the perturbation (returned by `icomplexify`) is approximately destabilizing.

[STABMARG2,DESTABUNC2,REPORT2,INFO2] = robuststab(CLOOP_c,ropt);

The plot shows the upper/lower `mu` bounds for the 'complexified' problem. Complexification greatly improves the conditioning of the lower bound. The upper bound is relatively unchanged by the complexification, and therefore complexification does not change the problem significantly.

bodeplot(INFO2.MussvBnds(:,1),INFO2.MussvBnds(:,2),opt)

**Figure 2:** Mu bounds for Aircraft using power iteration + complexify lower bound.

**Stability Analysis: Gain-based Lower Bound**

For some problems, the amount of complexity necessary to regularize the lower bound changes the problem significantly and you should use a gain-based lower bound. Set 'g' in `robopt` to force `robuststab` to use the gain-based lower bound and solve the original problem using a coordinate-wise optimization. However, this approach is computationally slower compared to using power iteration and complexifying.

ropt = robuststabOptions('Mussv','g','Sensitivity','off'); [STABMARG3,DESTABUNC3,REPORT3,INFO3] = robuststab(CLOOP,ropt); INFO3 bodeplot(INFO3.MussvBnds(:,1),INFO3.MussvBnds(:,2),opt)

INFO3 = Sensitivity: [1x1 struct] Frequency: [94x1 double] BadUncertainValues: [94x1 struct] MussvBnds: [1x2 frd] MussvInfo: [1x1 struct]

**Figure 3:** Mu bounds for Aircraft using gain-based lower bound.