Control of F-14 Lateral Axis Using mu-Synthesis
In this demo we'll show how to use mu-analysis and synthesis tools in the Robust Control Toolbox™. Our example describes the design of a robust controller for the lateral-directional axis of an F-14 aircraft during powered approach to landing. The linearized F-14 model is found at an angle-of-attack (alpha) of 10.5 degrees and airspeed of 140 knots.
Contents
- Performance Specifications
- From Specs to Weighting Functions
- Nominal Aircraft Model
- Accounting for Modeling Errors
- Building an Uncertain Model of the Aircraft Dynamics
- Analyzing How Modeling Errors Affect Open-Loop Responses
- Designing the Lateral-Axis Controller
- Frequency-Domain Comparison of Controllers
- Time-Domain Validation of the Robust Controller
Performance Specifications
Let’s look at the illustration below showing a block diagram of the closed-loop system. The diagram includes the nominal aircraft model, the controller K, as well as elements capturing the model uncertainty and performance objectives (see next sections for details).
Figure 1: Robust Control Design for F-14 Lateral Axis
Our design goal is to have the "true" airplane respond effectively to the pilot's lateral stick and rudder pedal inputs. These performance specifications include:
- Decoupled responses from lateral stick to roll rate p and from rudder pedals to side-slip angle beta. The lateral stick and rudder pedals have a maximum deflection of +/- 1 inch.
- The aircraft handling quality (HQ) response from lateral stick to roll rate p should match the first-order response
hq_p = 5.0 * tf(2.0,[1 2.0]);
step(hq_p), title('Desired response from lateral stick to roll rate (Handling Quality)')

Figure 2: Desired response from lateral stick to roll rate.
The aircraft handling quality response from the rudder pedals to the side-slip angle beta should match the damped second-order response.
hq_beta = -2.5 * tf(1.25^2,[1 2.5 1.25^2]);
step(hq_beta), title('Desired response from rudder pedal to side-slip angle (Handling Quality)')

Figure 3: Desired response from rudder pedal to side-slip angle.
- The stabilizer actuators have +/- 20 degs and +/- 90 degs/sec limits on their deflection and deflection rate. The rudder actuators have +/- 30 degs and +/-125 degs/sec deflection and deflection rate limits.
- The three measurement signals ( roll rate p, yaw rate r, and lateral acceleration y_ac ) are filtered through second-order anti-aliasing filters:
freq = 12.5 * (2*pi); % 12.5 Hz zeta = 0.5; antia_filt_yaw = tf(freq^2,[1 2*zeta*freq freq^2]); antia_filt_lat = tf(freq^2,[1 2*zeta*freq freq^2]); freq = 4.1 * (2*pi); % 4.1 Hz zeta = 0.7; antia_filt_roll = tf(freq^2,[1 2*zeta*freq freq^2]); antia_filt = append(antia_filt_roll,antia_filt_yaw,antia_filt_lat);
From Specs to Weighting Functions
Note that H-infinity design algorithms seek to minimize the largest closed-loop gain across frequency (H-infinity norm). To apply these tools, we first must recast our design tradeoffs and frequency-dependent specifications as constraints on the closed-loop gains. We'll use weighting functions to "normalize" our specifications across frequency and to weight each requirement adequately.
We'll express the F14 specs in terms of weighting functions:
- To capture the limits on the actuator deflection magnitude and rate, pick a diagonal, constant weight, such as W_act, corresponding to the stabilizer and rudder deflection rate and deflection limits.
W_act = diag([1/90,1/20,1/125,1/30]);
- We can use a 3x3 diagonal, high-pass filter W_n to model the frequency content of the sensor noise in the roll rate, yaw rate, and lateral acceleration channels.
W_n = append(0.025,tf(0.0125*[1 1],[1 100]),0.025);
clf, bodemag(W_n(2,2)), title('Sensor noise power as a function of frequency')

Figure 4: Sensor noise power as a function of frequency
- The lateral stick-to-|p| and rudder pedal-to-|beta| responses should match the handling quality targets hq_p and hq_beta. This is a model-matching objective: to minimize the difference (peak gain) between the desired and actual closed-loop transfer functions. Performance is limited due to a right-half plane zero in the model at 0.002 rad/sec, so accurate tracking of sinusoids below 0.002 rad/sec isn't possible. Accordingly, we'll weight the first handling quality spec with a bandpass filter W_p that emphasizes the frequency range between 0.06 and 30 rad/sec. (We prefer a roll rate tracking error of less than 5%).
W_p = tf([0.05 2.9 105.93 6.17 0.16],[1 9.19 30.80 18.83 3.95]);
clf, bodemag(W_p), title('Weight on Handling Quality spec')

Figure 5: Weight on handling quality spec.
Similarly, let's pick W_beta=2*W_p for the second handling quality spec
W_beta = 2*W_p;
Here we scaled the weights W_act, W_n, W_p, and W_beta so the closed-loop gain between all external inputs and all weighted outputs is less than 1 at all frequencies.
Nominal Aircraft Model
A pilot can command the lateral-directional response of the aircraft with the lateral stick and rudder pedals. The aircraft has the following attributes:
- Two control inputs: differential stabilizer deflection (delta_dstab, degrees) and rudder deflection (delta_rud, degrees).
- Three measured outputs: roll rate (p, deg/sec), yaw rate (r, degs/sec), and lateral acceleration (y_ac, g's).
- One calculated output: side-slip angle (beta).
The nominal lateral directional F-14 model, the F14nominal, has four states:
- Lateral velocity (v)
- Yaw rate (r)
- Roll rate (p)
- Roll angle (phi)
These variables are related by the state space equations:
where x = [v; r; p; phi], u = [delta_dstab; delta_rud], and y = [beta; p; r; y_ac].
load F14nominal
F14nominal
a = Lateral Velo Yaw Rate (r Roll Rate (p Roll Angle ( Lateral Velo -0.116 -227.3 43.02 31.63 Yaw Rate (r 0.00265 -0.259 -0.1445 0 Roll Rate (p -0.02114 0.6703 -1.365 0 Roll Angle ( 0 0.1853 1 0 b = Differential Rudder (deg) Lateral Velo 0.0622 0.1013 Yaw Rate (r -0.005252 -0.01121 Roll Rate (p -0.04666 0.003644 Roll Angle ( 0 0 c = Lateral Velo Yaw Rate (r Roll Rate (p Roll Angle ( Sideslip (be 0.2469 0 0 0 Roll Rate (p 0 0 57.3 0 Yaw Rate (r 0 57.3 0 0 lateral acce -0.002827 -0.007877 0.05106 0 d = Differential Rudder (deg) Sideslip (be 0 0 Roll Rate (p 0 0 Yaw Rate (r 0 0 lateral acce 0.002886 0.002273 Continuous-time model.
The complete airframe model also includes actuators models A_S and A_R. The actuator outputs are their respective rates and deflections. The actuator rates are used to penalize the actuation effort.
A_S = [tf([25 0],[1 25]); tf(25,[1 25])]; A_R = A_S;
Accounting for Modeling Errors
The nominal F14 model only approximates true airplane behavior. To account for unmodeled dynamics, you can introduce a relative introduce a relative term or multiplicative uncertainty W_in*Delta_G at the plant input, where the error dynamics Delta_G have gain less than 1 across frequencies, and the weighting function W_in reflects the frequency ranges in which the model is more or less accurate. There are typically more modeling errors at high frequencies so W_in is high pass.
% Normalized error dynamics Delta_G = ultidyn('Delta_G',[2 2],'Bound',1.0); % Frequency shaping of error dynamics w_1 = tf(2.0*[1 4],[1 160]); w_2 = tf(1.5*[1 20],[1 200]); W_in = append(w_1,w_2); bodemag(w_1,'-',w_2,'--') title('Relative error on nominal F14 model as a function of frequency') legend('stabilizer','rudder','Location','NorthWest');

Figure 6: Relative error on nominal F-14 model as a function of frequency.
Building an Uncertain Model of the Aircraft Dynamics
Now that we have quantified modeling errors, we can build an uncertain model of the aircraft dynamics corresponding to the dashed box in the figure 7 (same as figure 1):
Figure 7: Aircraft dynamics.
We'll use the sysic function to combine the nominal airframe model F14nominal, the actuator models A_S and A_R, and the modeling error description W_in*Delta_G into a single uncertain model mapping [delta_dstab; delta_rud] to the plant and actuator outputs:
systemnames = 'F14nominal A_S A_R W_in Delta_G'; inputvar = '[delta_dstab; delta_rud]'; outputvar = '[A_S; A_R; F14nominal]'; input_to_F14nominal = '[A_S(2); A_R(2)]'; input_to_A_S = '[delta_dstab + W_in(1)]'; input_to_A_R = '[delta_rud + W_in(2)]'; input_to_W_in = '[Delta_G]'; input_to_Delta_G = '[delta_dstab; delta_rud]'; sysoutname = 'F14_unc'; cleanupsysic = 'yes'; sysic;
This produces an uncertain state-space (USS) model F14_unc of the aircraft:
F14_unc
USS: 8 States, 8 Outputs, 2 Inputs, Continuous System Delta_G: 2x2 LTI, max. gain = 1, 1 occurrence
Analyzing How Modeling Errors Affect Open-Loop Responses
We can analyze the effect of modeling uncertainty by picking random samples of the unmodeled dynamics Delta_G and plotting the nominal and perturbed time responses (Monte Carlo analysis). For example, for the differential stabilizer channel, the uncertainty weight w_1 implies a 5% modeling error at low frequency, increasing to 100% after 93 rad/sec, as confirmed by the Bode diagram below.
% Pick 10 random samples F14_unc_sampl = usample(F14_unc,10); % Look at response from differential stabilizer to beta clf subplot(211), step(F14_unc.Nominal(5,1),'r+',F14_unc_sampl(5,1),'b-',10) legend('Nominal','Perturbed'),ylabel('Beta (degrees)') subplot(212), bodemag(F14_unc.Nominal(5,1),'r+',F14_unc_sampl(5,1),'b-',{0.001,1e3}) legend('Nominal','Perturbed')

Figure 8: Step response and Bode diagram.
Designing the Lateral-Axis Controller
Now we can proceed with designing a controller that robustly achieves the specifications, where robustly means for any perturbed aircraft model consistent with the modeling error bounds W_in.
First we'll build an open-loop model F14IC mapping the external input signals to the performance-related outputs (see figure below).
Figure 9: Open-loop model mapping external input signals to performance-related outputs.
Again we can use sysic to build F14IC:
systemnames = 'F14_unc antia_filt hq_p hq_beta '; systemnames = [systemnames ' W_act W_n W_p W_beta']; inputvar = '[sn_nois{3}; roll_cmd; beta_cmd; delta_dstab; delta_rud]'; outputvar = '[ W_p; W_beta; W_act; roll_cmd; beta_cmd; antia_filt + W_n ]'; input_to_F14_unc = '[ delta_dstab; delta_rud ]'; input_to_antia_filt = '[ F14_unc(6:8) ]'; input_to_hq_beta = '[ beta_cmd ]'; input_to_hq_p = '[ roll_cmd ]'; input_to_W_act = '[ F14_unc(1:4) ]'; input_to_W_beta = '[ hq_beta - F14_unc(5) ]'; input_to_W_p = '[ hq_p - F14_unc(6) ]'; input_to_W_n = '[ sn_nois ]'; sysoutname = 'F14IC'; cleanupsysic = 'yes'; sysic
This produces the uncertain state-space model
F14IC
USS: 26 States, 11 Outputs, 7 Inputs, Continuous System Delta_G: 2x2 LTI, max. gain = 1, 1 occurrence
Recall that by construction of the weighting functions, a controller meets the specs whenever the closed-loop gain is less than 1 at all frequencies and for any I/O directions. We can first design an H-infinity controller that minimizes the closed-loop gain for the nominal aircraft model:
nmeas = 5; % number of measurements nctrls = 2; % number of controls [kinf,ginf,gammainf] = hinfsyn(F14IC.NominalValue,nmeas,nctrls); gammainf
gammainf = 0.6668
Here hinfsyn computed a controller kinf that keeps the closed-loop gain below gammainf = 0.67 < 1, so the specs can be met for the nominal aircraft model.
Next, we perform a mu-synthesis to see if the specs can be met robustly when taking into account the modeling errors (uncertainty Delta_G). We use the command dksyn to perform the synthesis and set the frequency grid used for mu-analysis and the number of D-K iterations with dkitopts.
fmu = logspace(-2,2,60); opt = dkitopt('FrequencyVector',fmu,'NumberofAutoIterations',5); [kmu,clpmu,bnd] = dksyn(F14IC,nmeas,nctrls,opt); bnd
bnd = 0.9514
Here the best controller kmu can only keep the closed-loop gain below bnd = 1.23 for the specified model uncertainty, indicating that the specs can be nearly but not fully met for the family of aircraft models under consideration.
Frequency-Domain Comparison of Controllers
Let's compare the performance and robustness of the H-infinity controller kinf and mu controller kmu. Recall that the performance specs are achieved when the closed loop gain is less than 1 for every frequency.
First use the lft function to close the loop around each controller:
clinf = lft(F14IC,kinf); clmu = lft(F14IC,kmu);
To facilitate the analysis, we sample the frequency response of the uncertain closed-loop model over the frequency grid fmu, thus creating an uncertain frequency response or UFRD:
clinfg = frd(clinf,fmu) clmug = frd(clmu,fmu)
UFRD: 6 Outputs, 5 Inputs, Continuous System, 60 Frequency points Delta_G: 2x2 LTI, max. gain = 1, 1 occurrence UFRD: 6 Outputs, 5 Inputs, Continuous System, 60 Frequency points Delta_G: 2x2 LTI, max. gain = 1, 1 occurrence
What is the worst-case performance (in terms of closed-loop gain) of each controller for modeling errors bounded by W_in? The wcgain command helps you answer this difficult question directly without need for extensive gridding and simulation.
opt=wcgopt('FreqPtWise',1); % options for WCGAIN % Compute worst-case gain (as a function of frequency) for kinf [mginf,wcuinf] = wcgain(clinfg,opt); mginf
mginf = LowerBound: [1x1 frd] UpperBound: [1x1 frd] CriticalFrequency: 1.2638
% Compute worst-case gain for kinf
[mgmu,wcumu,infomu] = wcgain(clmug,opt);
mgmu
mgmu = LowerBound: [1x1 frd] UpperBound: [1x1 frd] CriticalFrequency: 0.0137
You can now compare the nominal and worst-case performance (peak gain<1) for each controller:
clf subplot(211) semilogx(fnorm(clinfg.NominalValue),'r',mginf.UpperBound,'b'); title('Performance analysis for kinf') xlabel('Frequency (rad/sec)') ylabel('Closed-loop gain'); legend('Nominal Plant','Worst-Case','Location','NorthWest'); subplot(212) semilogx(fnorm(clmug.NominalValue),'r',mgmu.UpperBound,'b'); title('Performance analysis for kmu') xlabel('Frequency (rad/sec)') ylabel('Closed-loop gain'); legend('Nominal Plant','Worst-Case','Location','SouthWest');

The first plot shows that while the H-infinity controller kinf meets the performance specs for the nominal plant model, its performance can sharply deteriorate (peak gain near 15) for some perturbed model within our modeling error bounds.
In contrast, the mu controller kmu has slightly worse performance for the nominal plant when compared to kinf, but it maintains this performance consistently for all perturbed models (worst-case gain near 1.25). The mu controller is therefore more robust to modeling errors.
Time-Domain Validation of the Robust Controller
To further test the robustness of the mu controller kmu in the time domain, you can compare the time responses of the nominal and worst-case closed-loop models with the ideal "Handling Quality" response. To do this, first construct the "true" closed-loop model F14SIM where all weighting functions and HQ reference models have been removed:
systemnames = 'F14_unc antia_filt kmu'; inputvar = '[roll_cmd; beta_cmd]'; outputvar = '[F14_unc(6); F14_unc(5)]'; input_to_F14_unc = '[ kmu ]'; input_to_antia_filt = '[ F14_unc(6:8) ]'; input_to_kmu = '[ roll_cmd; beta_cmd; antia_filt ]'; sysoutname = 'F14SIM'; cleanupsysic = 'yes'; sysic
Next, create the test signals u_stick and u_pedal shown below
time = 0:0.02:15; u_stick = (time>=9 & time<12); u_pedal = (time>=1 & time<4) - (time>=4 & time<7); clf subplot(211), plot(time,u_stick), axis([0 14 -2 2]), title('Lateral stick command') subplot(212), plot(time,u_pedal), axis([0 14 -2 2]), title('Rudder pedal command')

You can now compute and plot the ideal, nominal, and worst-case responses to the test commands u_stick and u_pedal
% Ideal behavior F14ideal = append(hq_p,hq_beta); % Find frequency where worst-case gain occurs % and apply corresponding worst-case perturbation wcfreqidx = find(mgmu.CriticalFrequency==infomu.Frequency); F14wc = usubs(F14SIM,wcumu{wcfreqidx}); % Compare responses clf lsim(F14ideal,'g',F14SIM.NominalValue,'r',F14wc,'b--',[u_stick ; u_pedal],time) legend('ideal','nominal','perturbed','Location','SouthEast'); title('Closed-loop responses with mu controller KMU')

The closed-loop response is nearly identical for the nominal and worst-case closed-loop systems. Note that the roll-rate response of the F-14 tracks the roll-rate command well initially and then departs from this command. This is due to a right-half plane zero in the aircraft model at 0.024 rad/sec.