Loop shaping design using Glover-McFarlane method
ncfsyn implements a method for designing controllers that
uses a combination of loop shaping and robust stabilization as proposed in -. The function computes the Glover-McFarlane H∞
normalized coprime factor loop-shaping controller K for a plant
G with pre-compensator and post-compensator weights
W2. The function assumes the positive feedback
configuration of the following illustration.
To specify negative feedback, replace G by –G. The
controller Ks stabilizes a family of systems given
by a ball of uncertainty in the normalized coprime factors of the shaped plant Gs =
W2GW1. The final controller K returned by
ncfsyn is obtained as K =
computes the Glover-McFarlane H∞ normalized
coprime factor loop-shaping controller
info] = ncfsyn(
K for the plant
G, with W1 =
W2 = I.
CL is the closed-loop system from the disturbances
w2 to the outputs
z2. The function also returns the
gamma, and a
structure containing additional information about the result.
ncfsyn to design a controller for the following plant.
G = tf([1 5],[1 2 10]);
Use weighting functions that yield a shaped plant
W1*G*W2 with high gain for disturbance attenuation below 0.1 rad/s, and low gain for good robust stability above about 5 rad/s. For this
G, a pre-compensator weight alone is sufficient.
W1 = tf(1,[1 0]); bodemag(W1*G) grid
Compute the controller.
[K,CL,gamma,info] = ncfsyn(G,W1);
The optimal cost
gamma is related to the normalized coprime stability margin of the system by
1/gamma = ncfmargin(Gs,-Ks). (The minus sign is needed because
ncfmargin assumes a negative-feedback loop, while
ncfsyn computes a positive-feedback controller.)
b = ncfmargin(info.Gs,-info.Ks); [gamma 1/b]
ans = 1×2 1.4521 1.4521
Compare the achieved and target loop shapes.
In the controller returned by
ncfsyn, some controller poles can become infinitely fast as the actual performance
gamma approaches the optimal achievable performance
gopt. To prevent this, check the controller poles and increase the
tol argument if some poles are undesirably fast.
tol sets how closely
gamma of the synthesized controller approximates
G, a fourth-order plant.
Use a weighting function that yields a shaped plant
W1*G with high gain for disturbance attenuation below about 10 rad/s, and low gain for good robust stability above about 1000 rad/s.
W1 = zpk(-130,0,0.6); bode(W1*G)
Design a controller using the default tolerance,
tol = 1e-3. Examine the poles of the resulting controller.
[K1,~,gamma1,info1] = ncfsyn(G,W1); damp(K1)
Pole Damping Frequency Time Constant (rad/seconds) (seconds) 0.00e+00 -1.00e+00 0.00e+00 Inf -1.48e+02 + 1.60e+01i 9.94e-01 1.49e+02 6.76e-03 -1.48e+02 - 1.60e+01i 9.94e-01 1.49e+02 6.76e-03 -7.90e+02 + 8.01e+02i 7.02e-01 1.13e+03 1.27e-03 -7.90e+02 - 8.01e+02i 7.02e-01 1.13e+03 1.27e-03 -1.50e+05 1.00e+00 1.50e+05 6.66e-06
This controller has a pole at around 150000 rad/s, much faster than any other dynamics in the controller. To reduce the frequency of this pole, try increasing the tolerance to 0.1.
tol = 0.1; [K2,~,gamma2,info2] = ncfsyn(G,W1,,tol); damp(K2)
Pole Damping Frequency Time Constant (rad/seconds) (seconds) 0.00e+00 -1.00e+00 0.00e+00 Inf -1.50e+02 + 6.85e+00i 9.99e-01 1.50e+02 6.68e-03 -1.50e+02 - 6.85e+00i 9.99e-01 1.50e+02 6.68e-03 -6.58e+02 + 9.45e+02i 5.71e-01 1.15e+03 1.52e-03 -6.58e+02 - 9.45e+02i 5.71e-01 1.15e+03 1.52e-03 -1.77e+03 1.00e+00 1.77e+03 5.65e-04
This time the highest-frequency pole is closer to the others. Compare the resulting performance values to confirm that the two controllers deliver similar performance.
gamma1 = 2.1157
gamma2 = 2.3250
Plant, specified as a dynamic system model such as a state-space
ss) model. If
G is a generalized
state-space model with uncertain or tunable control design blocks, then
ncfsyn uses the nominal or current value of those elements.
G must have the same number of inputs and outputs.
W1— Pre-compensator weight
eye(N)(default) | LTI model
Pre-compensator weight, specified as:
N is the
number of inputs or outputs in
SISO minimum-phase LTI model. In this case,
ncfsyn uses the
same weight for every loop channel.
MIMO minimum-phase LTI model of the same I/O dimensions as
Select pre-compensator and post-compensator weights W1 and W2 such that the gain of the shaped plant Gs = W2GW1 is sufficiently high at frequencies where good disturbance attenuation is required, and sufficiently low at frequencies where good robust stability is required.
W2— Post-compensator weight
eye(N)(default) | LTI model
Post-compensator weight, specified as the identity matrix
or a SISO or MIMO LTI model. The considerations for specifying
are the same as those for
W1. To omit the post-compensator weight,
W2 = .
tol— Near-optimality tolerance
0.001(default) | positive scalar
Near-optimality tolerance, specified as a positive scalar value.
ncfsyn returns a controller
gamma that satisfies
gopt is the optimal performance (returned
tol to adjust the acceptable gap
tol value that is too small can cause numerical difficulties and
introduce fast poles in
usually eliminates both issues. For an example, see Reduce Undesirable Fast Dynamics in Controller.
CL— Optimal closed-loop system
Optimal closed-loop system from the disturbances w1 and w2 to the outputs z1 and z2, returned as a state-space model. The closed-loop system is given by:
gamma— H∞ performance
H∞ performance achieved with the controller
K, returned as a positive scalar value greater than 1. The
H∞ performance is
hinfnorm(CL). The optimal controller
Ks is such that the singular-value plot of
the shaped loop Ls =
W2GW1Ks optimally matches the target loop shape
Gs to within a factor of
gamma. However, for numerical reasons,
ncfsyn generally returns a controller with slightly larger
H∞ performance than optimal. For the optimal
achievable performance, see the
info output argument.
gamma is related to the normalized coprime stability margin of
the system by
gamma = 1/ncfmargin(Gs,-Ks). Thus,
gamma gives a good indication of robustness of stability to a
wide class of unstructured plant variations, with values in the range 1 <
gamma < 3 corresponding to satisfactory stability margins for
most typical control system designs.
info— Additional information
Additional information about the controller synthesis, returned as a structure containing the following fields.
gopt — Optimal performance achievable by
H∞ synthesis for the shaped plant. For
ncfsyn generally returns a controller with
slightly larger H∞ performance, which is
gamma. To adjust how tightly
ncfsyn aims to make
gopt, use the
tol input argument. See
Reduce Undesirable Fast Dynamics in Controller.
nugap robustness metric,
emax = 1/gopt (see
Gs — Shaped plant Gs =
Ks — Optimal controller for shaped plant
Gs. The final controller is K =
W1KsW2. See Algorithms for
ncfmargin assumes a negative-feedback
ncfsyn command designs a controller for a positive-feedback
loop. Therefore, to compute the margin using a controller designed with
The returned controller K = W1KsW2, where Ks is an optimal H∞ controller that minimizes the H∞ cost
The optimal performance is the minimal cost
Suppose that Gs=NM–1, where N(jω)*N(jω) + M(jω)*M(jω) = I, is a normalized coprime factorization (NCF) of the weighted plant model Gs. Then, theory ensures that the control system remains robustly stable for any perturbation to Gs of the form
where Δ1, Δ2 are a stable pair satisfying
The closed-loop H∞-norm objective has the standard signal gain interpretation. Finally it can be shown that the controller, Ks, does not substantially affect the loop shape in frequencies where the gain of W2GW1 is either high or low, and will guarantee satisfactory stability margins in the frequency region of gain cross-over. In the regulator set-up, the final controller to be implemented is K=W1KsW2.
Not recommended starting in R2020b
ncfsyn(G,W1,W2,'ref') is not recommended.
 McFarlane, Duncan C., and Keith Glover, eds. Robust Controller Design using Normalized Coprime Factor Plant Descriptions. Vol. 138. Lecture Notes in Control and Information Sciences. Berlin/Heidelberg: Springer-Verlag, 1990. https://doi.org/10.1007/BFb0043199.
 McFarlane, D., and K. Glover, “A Loop Shaping Design Procedure using H∞ Synthesis,” IEEE Transactions on Automatic Control, no. 6 (June 1992): pp. 759–69. https://doi.org/10.1109/9.256330.
 Vinnicombe, Glenn. “Measuring Robustness of Feedback Systems.” PhD Dissertation, University of Cambridge, 1992.
 Zhou, Kemin, and John Comstock Doyle. Essentials of Robust Control. Upper Saddle River, NJ: Prentice-Hall, 1998.