1 files changed, 10 insertions, 8 deletions
diff --git a/uav_model.m b/uav_model.m
index 3b691f4..0af0818 100644
--- a/uav_model.m
+++ b/uav_model.m
@@ -223,7 +223,7 @@ D = zeros(12, 5);
 % Create state space object
 T = params.measurements.SensorFusionDelay;
 n = params.linearization.PadeApproxOrder;
-sys = minreal(pade(ss(A, B, C, D, 'OutputDelay', T), n));
+sys = minreal(pade(ss(A, B, C, D, 'OutputDelay', T), n), [], false); % slient
 
 % Save linearized dynamics (numerical)
 model.linear = struct(...
@@ -241,7 +241,7 @@ eigvals = eig(A);
 % Check system controllability / stabilizability
 Wc = ctrb(sys);
 if rank(Wc) < nx
-  fprintf('Linearized system has %d uncontrollable states!\n', ...
+  fprintf(' - Linearized system has %d uncontrollable states!\n', ...
     (nx - rank(Wc)));
 
   % Is the system at least stabilizable?
@@ -257,17 +257,17 @@ if rank(Wc) < nx
     end
   end
   if unstabilizable > 0
-    fprintf('Linearized system has %d unstabilizable modes!\n', ...
+    fprintf(' - Linearized system has %d unstabilizable modes!\n', ...
       unstabilizable);
   else
-    fprintf('However, it is stabilizable.\n');
+    fprintf('   However, it is stabilizable.\n');
   end
 end
 
 % Check system observability / detectability
 Wo = obsv(sys);
 if rank(Wo) < nx
-  fprintf('Linearized system has %d unobservable states!\n', ...
+  fprintf(' - Linearized system has %d unobservable states!\n', ...
     (nx - rank(Wo)));
   % is the system at least detectable?
   undetectable = 0;
@@ -281,10 +281,10 @@ if rank(Wo) < nx
     end
   end
   if undetectable > 0
-    fprintf('Linearized system has %d undetectable modes!\n', ...
+    fprintf(' - Linearized system has %d undetectable modes!\n', ...
       undetectable);
   else
-    fprintf('However, it is detectable.\n')
+    fprintf('   However, it is detectable.\n')
   end
 end
 
@@ -367,10 +367,12 @@ idx = model.uncertain.index;
 
 % Number of inputs
 model.uncertain.Nv = max(size(idx.InputUncertain));
-model.uncertain.Nr = max(size(idx.InputReference));
 model.uncertain.Nw = max(size(idx.InputExogenous));
 model.uncertain.Nu = max(size(idx.InputNominal));
 
+model.uncertain.Nr = max(size(idx.InputReference));
+% size of noise is (Nw - Nr)
+
 % Number of outputs
 model.uncertain.Nz = max(size(idx.OutputUncertain));
 model.uncertain.Ne = max(size(idx.OutputError));