A control systems design toolbox for Julia.

To install, in the Julia REPL:

Pkg.add("ControlSystems")

Note that this package requires Julia 0.4.

All functions have docstrings, which can be viewed from the REPL, using for example ?tf .

A documentation website under developement is available at http://juliacontrol.github.io/ControlSystems.jl/latest/.

Some of the available commands are:

ss, tf, zpk, ss2tf

pole, tzero, norm, ctrb, obsv, gangoffour, margin, markovparam, damp, dampreport, zpkdata, dcgain, covar, gram, sigma, sisomargin

care, dare, dlyap, lqr, dlqr, place, pid, leadlink, laglink, leadlinkat, rstd, rstc, dab

step, impulse, lsim, freqresp, evalfr, bode, nyquist

lsimplot, stepplot, impulseplot, bodeplot, nyquistplot, sigmaplot, marginplot, gangoffourplot, pidplots, pzmap, nicholsplot, pidplots, rlocus, leadlinkcurve

minreal, sminreal, c2d

This toolbox works similar to that of other major computer-aided control systems design (CACSD) toolboxes. Systems can be created in either a transfer function or a state space representation. These systems can then be combined into larger architectures, simulated in both time and frequency domain, and analyzed for stability/performance properties.

Here we create a simple position controller for an electric motor with an inertial load.

using ControlSystems

# Motor parameters
J = 2.0
b = 0.04
K = 1.0
R = 0.08
L = 1e-4

# Create the model transfer function
s = tf("s")
P = K/(s*((J*s + b)*(L*s + R) + K^2))
# This generates the system
# TransferFunction:
#                1.0
# ---------------------------------
# 0.0002s^3 + 0.160004s^2 + 1.0032s
#
#Continuous-time transfer function model

# Create an array of closed loop systems for different values of Kp
CLs = TransferFunction[kp*P/(1 + kp*P) for kp = [1, 5, 15]];

# Plot the step response of the controllers
stepplot(CLs);
# Add legend. This is specific to the PyPlot back-end, see documentation for more info.
PyPlot.legend(["Kp = 1", "Kp = 5", "Kp = 15"]);