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README.md

ControlSystems.jl

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A control systems design toolbox for Julia.

Installation

To install, in the Julia REPL:

using Pkg; Pkg.add("ControlSystems")

News

2020-10

  • lsimplot, stepplot, impulseplot now have the same signatures as the corresponding non-plotting function.
  • New function d2c for conversion from discrete to continuous.

2020-09-24

Release v0.7 introduces a new TimeEvolution type to handle Discrete/Continuous systems. See the release notes.

2019-11-03

  • Poles and zeros are "not sorted" as in Julia versions < 1.2, even on newer versions of Julia. This should imply that complex conjugates are kept together.

2019-05-28

Delay systems

  • We now support systems with time delays. Example:
sys = tf(1, [1,1])*delay(1)
stepplot(sys, 5) # Compilation time might be long for first simulation
nyquistplot(sys)

New examples

2019-05-22

New state-space type HeteroStateSpace that accepts matrices of heterogeneous types: example using StaticArrays.

2019-01-31

System identification using ControlSystemIdentification.jl is now available. The readme together with a series of notebooks serve as documentation.

Documentation

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

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

Some of the available commands are:

Constructing systems

ss, tf, zpk

Analysis

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

Synthesis

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

PID design

pid, stabregionPID, loopshapingPI, pidplots

Time and Frequency response

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

Plotting

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

Other

minreal, sminreal, c2d

Usage

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.

Example

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
# Any keyword arguments supported in Plots.jl can be supplied
stepplot(CLs, label=["Kp = 1" "Kp = 5" "Kp = 15"])

StepResponse

Additional examples

See the examples folder