LQT_LS refined

matlab/LQT_recursive.m

@@ -79,7 +79,10 @@ for n=1:2
 			x = x + xn; %Simulated noise on the state
 		end
 		Ka = (B' * P(:,:,t) * B + Ra) \ B' * P(:,:,t) * A; %Feedback gain
-		u = -Ka * x; %Feedback control on augmented state (resulting in feedback and feedforward terms on state)
+		u = -Ka * x; %Feedback control on augmented state (resulting in feedforward part and feedback on state)
+%		u_ff = -Ka(:,end); %Feedforward part on state
+%		u = u_ff - Ka(:,1:end-1) * x(1:end-1); %Feedforward and feedback part on state
+		
 		x = A * x + B * u; %Update of state vector
 		r(n).x(:,t) = x; %Log data
 	end

matlab/LQT_recursive_LS.m

@@ -97,9 +97,9 @@ for n=1:2
 		if t==25 && n==2
 			x = x + xn; %Simulated noise on the state
 		end
-%		u = -Ka(:,:,t) * x; %Feedback control on augmented state (resulting in feedforward part and feedback on state)
-		u_ff = -Ka(:,end,t); %Feedforward part on state
-		u = u_ff - Ka(:,1:end-1,t) * x(1:end-1); %Feedforward and feedback part on state
+		u = -Ka(:,:,t) * x; %Feedback control on augmented state (resulting in feedforward part and feedback on state)
+%		u_ff = -Ka(:,end,t); %Feedforward part on state
+%		u = u_ff - Ka(:,1:end-1,t) * x(1:end-1); %Feedforward and feedback part on state
 
 		x = A * x + B * u; %Update of state vector
 		r(n).x(:,t) = x; %Log data