As an example of LQG design, consider the following regulation problem.
The goal is to regulate the plant output y around zero. The input disturbance d is low frequency with power spectral density (PSD) concentrated below 10 rad/s. For LQG design purposes, it is modeled as white noise driving a lowpass filter with a cutoff at 10 rad/s, shown in the following figure.
For simplicity, this noise is modeled as Gaussian white noise with variance of 1.
The following figure shows the Bode magnitude of the shaping filter.
Bode Magnitude of the Lowpass Filter
There is some measurement noise n, with noise intensity given by
Use the cost function
where (A,B,C) is a state-space realization of .
sys = ss(tf(100,[1 1 100])) % State-space plant model % Design LQ-optimal gain K K = lqry(sys,10,1) % u = -Kx minimizes J(u) % Separate control input u and disturbance input d P = sys(:,[1 1]); % input [u;d], output y % Design Kalman state estimator Kest. Kest = kalman(P,1,0.01) % Form LQG regulator = LQ gain + Kalman filter. F = lqgreg(Kest,K)
These commands returns a state-space model
F of the LQG regulator
lqgreg functions perform discrete-time LQG
design when you apply them to discrete plants.
To validate the design, close the loop with
feedback, create and add
the lowpass filter in series with the closed-loop system, and compare the open- and
closed-loop impulse responses by using the
% Close loop clsys = feedback(sys,F,+1) % Note positive feedback. % Create the lowpass filter and add it in series with clsys. s = tf('s'); lpf= 10/(s+10) ; clsys_fin = lpf*clsys; % Open- vs. closed-loop impulse responses impulse(sys,'r--',clsys_fin,'b-')
These commands produce the following figure, which compares the open- and closed-loop impulse responses for this example.
Comparison of Open- and Closed-Loop Impulse Response