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Airframe Trim and Linearize

This example shows how to trim and linearize an airframe in the Simulink® environment using the Control System Toolbox™ software

Designing an autopilot with classical design techniques requires linear models of the airframe pitch dynamics for several trimmed flight conditions. The MATLAB® technical computing environment can determine the trim conditions and derive linear state-space models directly from the nonlinear Simulink and Aerospace Blockset™ model. This step saves time and helps to validate the model. The Control System Toolbox functions allow you to visualize the motion of the airframe in terms of open-loop frequency or time responses.

Initialize Guidance Model

The first problem is to find the elevator deflection, and the resulting trimmed body rate (q), which will generate a given incidence value when the missile is traveling at a set speed. Once the trim condition is found, a linear model can be derived for the dynamics of the perturbations in the states around the trim condition.

open_system('aeroblk_guidance_airframe');

Define State Values

h_ini     = 10000/m2ft;      % Trim Height [m]
M_ini     = 3;               % Trim Mach Number
alpha_ini = -10*d2r;         % Trim Incidence [rad]
theta_ini = 0*d2r;           % Trim Flightpath Angle [rad]
v_ini = M_ini*(340+(295-340)*h_ini/11000); 	% Total Velocity [m/s]

q_ini = 0;               % Initial pitch Body Rate [rad/sec]

Find Names and Ordering of States from Simulink® Model

[sizes,x0,names]=aeroblk_guidance_airframe([],[],[],0);

state_names = cell(1,numel(names));
for i = 1:numel(names)
  n = max(strfind(names{i},'/'));
  state_names{i}=names{i}(n+1:end);
end

Specify Which States to Trim and Which States Remain Fixed

fixed_states            = [{'U,w'} {'Theta'} {'Position'}];
fixed_derivatives       = [{'U,w'} {'q'}];        % w and q
fixed_outputs           = [];                     % Velocity
fixed_inputs            = [];

n_states=[];n_deriv=[];
for i = 1:length(fixed_states)
  n_states=[n_states find(strcmp(fixed_states{i},state_names))]; %#ok<AGROW>
end
for i = 1:length(fixed_derivatives)
  n_deriv=[n_deriv find(strcmp(fixed_derivatives{i},state_names))]; %#ok<AGROW>
end
n_deriv=n_deriv(2:end);                          % Ignore U

Trim Model

[X_trim,U_trim,Y_trim,DX]=trim('aeroblk_guidance_airframe',x0,0,[0 0 v_ini]', ...
                               n_states,fixed_inputs,fixed_outputs, ...
                               [],n_deriv)  %#ok<NOPTS>
X_trim =

   1.0e+03 *

   -0.0002
    0.9677
   -0.1706
         0
         0
   -3.0480


U_trim =

    0.1362


Y_trim =

   -0.2160
  199.2481


DX =

         0
  -14.0977
    0.0000
   -0.2160
  967.6649
 -170.6254

Derive Linear Model and View Frequency Response

[A,B,C,D]=linmod('aeroblk_guidance_airframe',X_trim,U_trim);
if exist('control','dir')
  airframe = ss(A(n_deriv,n_deriv),B(n_deriv,:),C([2 1],n_deriv),D([2 1],:));
  set(airframe,'StateName',state_names(n_deriv), ...
               'InputName',{'Elevator'}, ...
               'OutputName',[{'az'} {'q'}]);

  zpk(airframe)
  ltiview('bode',airframe)
end
ans =
 
  From input "Elevator" to output...
        -170.45 (s-28.61) (s+56.96)
   az:  ---------------------------
          (s^2 + 30.04s + 288.9)
 
         -194.66 (s+1.475)
   q:  ----------------------
       (s^2 + 30.04s + 288.9)
 
Continuous-time zero/pole/gain model.

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