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Form Linear-Quadratic-Gaussian (LQG) servo controller


C = lqgtrack(kest,k)
C = lqgtrack(kest,k,'2dof')
C = lqgtrack(kest,k,'1dof')
C = lqgtrack(kest,k,...CONTROLS)


lqgtrack forms a Linear-Quadratic-Gaussian (LQG) servo controller with integral action for the loop shown in the following figure. This compensator ensures that the output y tracks the reference command r and rejects process disturbances w and measurement noise v. lqgtrack assumes that r and y have the same length.


Always use positive feedback to connect the LQG servo controller C to the plant output y.

C = lqgtrack(kest,k) forms a two-degree-of-freedom LQG servo controller C by connecting the Kalman estimator kest and the state-feedback gain k, as shown in the following figure. C has inputs [r;y] and generates the command u=K[x^;xi], wherex^ is the Kalman estimate of the plant state, and xi is the integrator output.

The size of the gain matrix k determines the length of xi. xi, y, and r all have the same length.

The two-degree-of-freedom LQG servo controller state-space equations are



The syntax C = lqgtrack(kest,k,'2dof') is equivalent to C = lqgtrack(kest,k).

C = lqgtrack(kest,k,'1dof') forms a one-degree-of-freedom LQG servo controller C that takes the tracking error e = ry as input instead of [r ; y], as shown in the following figure.

The one-degree-of-freedom LQG servo controller state-space equations are


C = lqgtrack(kest,k,...CONTROLS) forms an LQG servo controller C when the Kalman estimator kest has access to additional known (deterministic) commands Ud of the plant. In the index vector CONTROLS, specify which inputs of kest are the control channels u. The resulting compensator C has inputs

  • [Ud ; r ; y] in the two-degree-of-freedom case

  • [Ud ; e] in the one-degree-of-freedom case

The corresponding compensator structure for the two-degree-of-freedom cases appears in the following figure.


See the example Design an LQG Servo Controller.


You can use lqgtrack for both continuous- and discrete-time systems.

In discrete-time systems, integrators are based on forward Euler (see lqi for details). The state estimate x^ is either x[n|n] or x[n|n–1], depending on the type of estimator (see kalman for details).

For a discrete-time plant with equations:


connecting the "current" Kalman estimator to the LQR gain is optimal only when E(w[n]v[n])=0 and y[n] does not depend on w[n] (H = 0). If these conditions are not satisfied, compute the optimal LQG controller using lqg.

Version History

Introduced in R2008b

See Also

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