Small update to build, readme and docs

Author: mfalt

.travis.yml

@@ -1,7 +1,11 @@
 language: julia
 julia:
-  - 0.7
   - 1.0
+  - 1.1
+  - nightly
+matrix:
+  allow_failures:
+  - julia: nightly
 notifications:
     email: false
 after_success:

README.md

@@ -1,9 +1,10 @@
 # ControlSystems.jl
 
-[![ControlSystems](http://pkg.julialang.org/badges/ControlSystems_0.6.svg)](http://pkg.julialang.org/?pkg=ControlSystems)
 [![Build Status](https://travis-ci.org/JuliaControl/ControlSystems.jl.svg?branch=master)](https://travis-ci.org/JuliaControl/ControlSystems.jl)
-[![Coverage Status](https://coveralls.io/repos/github/JuliaControl/ControlSystems.jl/badge.svg?branch=master)](https://coveralls.io/github/JuliaControl/ControlSystems.jl?branch=master)
-[![Latest](https://img.shields.io/badge/docs-latest-blue.svg)](http://juliacontrol.github.io/ControlSystems.jl/latest/)
+[![Gitter](https://badges.gitter.im/JuliaControl/ControlSystems.jl.svg)](https://gitter.im/JuliaControl/ControlSystems.jl?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+
+[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliacontrol.github.io/ControlSystems.jl/stable)
+[![](https://img.shields.io/badge/docs-latest-blue.svg)](https://juliacontrol.github.io/ControlSystems.jl/latest)
 
 A control systems design toolbox for Julia.
 

docs/make.jl

@@ -3,8 +3,27 @@ import GR # Bug with world age in Plots.jl, see https://github.com/JuliaPlots/Pl
 
 include("src/makeplots.jl")
 
-makedocs(modules=[ControlSystems], format=:html, sitename="ControlSystems")
-
+makedocs(modules=[ControlSystems],
+    format=Documenter.HTML(),
+    sitename="ControlSystems.jl",
+    pages=[
+        "Home" => "index.md",
+        "Examples" => Any[
+            "Design" => "examples/example.md",
+        ],
+        "Guide" => Any[
+            "Introduction" => "man/introduction.md",
+            "Creating Transfer Functions" => "man/creatingtfs.md",
+        ],
+        "Functions" => Any[
+            "Constructors" => "lib/constructors.md",
+            "Analysis" => "lib/analysis.md",
+            "Synthesis" => "lib/synthesis.md",
+            "Time and Frequency response" => "lib/timefreqresponse.md",
+            "Plotting" => "lib/plotting.md",
+        ],
+    ]
+    )
 # If not running travis, generate the plots here, even if we are not deploying
 if get(ENV, "TRAVIS", "") == ""
     makePlots()

docs/src/assets/logo.png

@@ -17,14 +17,14 @@ Q       = I
 R       = I
 L       = dlqr(A,B,Q,R) # lqr(sys,Q,R) can also be used
 
-u(t,x)  = -L*x + 1.5(t>=2.5)# Form control law (u is a function of t and x), a constant input disturbance is affecting the system from t≧2.5
+u(x,t)  = -L*x .+ 1.5(t>=2.5)# Form control law (u is a function of t and x), a constant input disturbance is affecting the system from t≧2.5
 t       =0:h:5
 x0      = [1,0]
-y, t, x, uout = lsim(sys,u,t,x0)
+y, t, x, uout = lsim(sys,u,t,x0=x0)
 plot(t,x, lab=["Position", "Velocity"]', xlabel="Time [s]")
 ```
 
-![](../plots/lqrplot.svg)
+![](../../plots/lqrplot.svg)
 
 
 # LQR design
@@ -39,22 +39,22 @@ Q       = I
 R       = I
 L       = dlqr(A,B,Q,R) # lqr(sys,Q,R) can also be used
 
-u(t,x)  = -L*x + 1.5(t>=2.5)# Form control law (u is a function of t and x), a constant input disturbance is affecting the system from t≧2.5
+u(x,t)  = -L*x .+ 1.5(t>=2.5)# Form control law (u is a function of t and x), a constant input disturbance is affecting the system from t≧2.5
 t       =0:h:5
 x0      = [1,0]
-y, t, x, uout = lsim(sys,u,t,x0)
+y, t, x, uout = lsim(sys,u,t,x0=x0)
 plot(t,x, lab=["Position", "Velocity"]', xlabel="Time [s]")
 ```
 
-![](../plots/lqrplot.svg)
+![](../../plots/lqrplot.svg)
 
 # PID design functions
 By plotting the gang of four under unit feedback for the process
 ```julia
 P = tf(1,[1,1])^4
 gangoffourplot(P,tf(1))
 ```
-![](../plots/pidgofplot.svg)
+![](../../plots/pidgofplot.svg)
 
 we notice that the sensitivity function is a bit too high at around frequencies ω = 0.8 rad/s. Since we want to control the process using a simple PI-controller, we utilize the
 function `loopshapingPI` and tell it that we want 60 degrees phase margin at this frequency. The resulting gang of four is plotted for both the constructed controller and for unit feedback.
@@ -63,8 +63,8 @@ function `loopshapingPI` and tell it that we want 60 degrees phase margin at thi
 ωp = 0.8
 kp,ki,C = loopshapingPI(P,ωp,phasemargin=60, doplot=true)
 ```
-![](../plots/pidgofplot2.svg)
-![](../plots/pidnyquistplot.svg)
+![](../../plots/pidgofplot2.svg)
+![](../../plots/pidnyquistplot.svg)
 
 
 We could also cosider a situation where we want to create a closed-loop system with the bandwidth ω = 2 rad/s, in which case we would write something like
@@ -74,8 +74,8 @@ kp,ki,C60 = loopshapingPI(P,ωp,rl=1,phasemargin=60, doplot=true)
 ```
 Here we specify that we want the Nyquist curve `L(iω) = P(iω)C(iω)` to pass the point `|L(iω)| = rl = 1,  arg(L(iω)) = -180 + phasemargin = -180 + 60`
 The gang of four tells us that we can indeed get a very robust and fast controller with this design method, but it will cost us significant control action to double the bandwidth of all four poles.
-![](../plots/pidgofplot3.svg)
-![](../plots/pidnyquistplot2.svg)
+![](../../plots/pidgofplot3.svg)
+![](../../plots/pidnyquistplot2.svg)
 
 # Advanced pole-zero placement
 This example illustrates how we can perform advanced pole-zero placement. The task is to make the process a bit faster and damp the poorly damped poles.
@@ -121,24 +121,24 @@ stepplot([P,Gcl]) # Visualize the open and closed loop responses.
 gangoffourplot(P, tf(-S,R)) # Plot the gang of four to check that all tranfer functions are OK
 ```
 
-![](../plots/ppstepplot.svg)
-![](../plots/ppgofplot.svg)
+![](../../plots/ppstepplot.svg)
+![](../../plots/ppgofplot.svg)
 
 
 # Stability boundary for PID controllers
 The stability boundary, where the transfer function `P(s)C(s) = -1`, can be plotted with the command `stabregionPID`. The process can be given in string form or as a regular LTIsystem.
 
 ```julia
-P1 = "exp(-sqrt(s))"
+P1 = s -> exp(-sqrt(s))
 f1 = stabregionPID(P1,exp10.(range(-5, stop=1, length=1000)))
-P2 = "100*(s+6).^2./(s.*(s+1).^2.*(s+50).^2)"
+P2 = s -> 100*(s+6).^2. /(s.*(s+1).^2. *(s+50).^2)
 f2 = stabregionPID(P2,exp10.(range(-5, stop=2, length=1000)))
 P3 = tf(1,[1,1])^4
 f3 = stabregionPID(P3,exp10.(range(-5, stop=0, length=1000)))
 ```
-![](../plots/stab1.svg)
-![](../plots/stab2.svg)
-![](../plots/stab3.svg)
+![](../../plots/stab1.svg)
+![](../../plots/stab2.svg)
+![](../../plots/stab3.svg)
 
 
 # PID plots
@@ -149,18 +149,18 @@ P = tf([1.],[1., 1])
 ζ = 0.5 # Desired damping
 
 ws = exp10.(range(-1, stop=2, length=8)) # A vector of closed-loop bandwidths
-kp = 2*ζ*ws-1 # Simple pole placement with PI given the closed-loop bandwidth, the poles are placed in a butterworth pattern
+kp = 2*ζ*ws .- 1 # Simple pole placement with PI given the closed-loop bandwidth, the poles are placed in a butterworth pattern
 ki = ws.^2
 pidplots(P,:nyquist,:gof;kps=kp,kis=ki, ω= exp10.(range(-2, stop=2, length=500))) # Request Nyquist and Gang-of-four plots (more plots are available, see ?pidplots )
 ```
-![](../plots/pidplotsnyquist1.svg)
-![](../plots/pidplotsgof1svg)
+![](../../plots/pidplotsnyquist1.svg)
+![](../../plots/pidplotsgof1svg)
 
 Now try a different strategy, where we have specified a gain crossover frequency of 0.1 rad/s
 ```julia
 kp = range(-1, stop=1, length=8) #
-ki = sqrt(1-kp.^2)/10
+ki = sqrt.(1 .- kp.^2)/10
 pidplots(P,:nyquist,:gof;kps=kp,kis=ki)
 ```
-![](../plots/pidplotsnyquist2.svg)
-![](../plots/pidplotsgof2.svg)
+![](../../plots/pidplotsnyquist2.svg)
+![](../../plots/pidplotsgof2.svg)

docs/src/examples/example.md

@@ -7,7 +7,7 @@ CurrentModule = ControlSystems
 ## Examples
 ```@contents
 Pages = ["examples/example.md"]
-Depth = 1
+Depth = 2
 ```
 
 ## Guide
@@ -20,12 +20,6 @@ Depth = 1
 ## Functions
 
 ```@contents
-Pages = ["lib/constructors.md", "lib/plotting.md"]
-```
-
-## Documentation Index
-
-```@index
-Pages = ["lib/constructors.md", "lib/plotting.md", "lib/syntheis.md", "lib/timefreqresponse.md", "lib/analysis.md"]
+Pages = ["lib/constructors.md",  "lib/analysis.md", "lib/syntheis.md", "lib/timefreqresponse.md", "lib/plotting.md"]
 Depth = 1
 ```

docs/src/index.md

@@ -13,14 +13,15 @@ tf(num, den, Ts=0)
 where `num` and `den` are the polinomial coefficients of the numerator and denominator of the polynomial and `Ts` is the sample time.
 ### Example:
 ```julia
-tf([1],[1,2,1])
+tf([1.0],[1,2,1])
 
 # output
 
-TransferFunction:
-      1.0
-----------------
-s^2 + 2.0s + 1.0
+TransferFunction{ControlSystems.SisoRational{Float64}}
+         1.0
+---------------------
+1.0*s^2 + 2.0*s + 1.0
+
 
 Continuous-time transfer function model
 ```
@@ -35,14 +36,14 @@ zpk(zeros, poles, gain, Ts=0)
 where `zeros` and `poles` are `Vectors` of the zeros and poles for the system and `gain` is a gain coefficient.
 ### Example
 ```julia
-zpk([-1,1], [-5, -10], 2)
+zpk([-1.0,1], [-5, -10], 2)
 
 # output
 
-TransferFunction:
-   (s - 1.0)(s + 1.0)
-2.0-------------------
-   (s + 10.0)(s + 5.0)
+TransferFunction{ControlSystems.SisoZpk{Float64,Float64}}
+   (1.0*s + 1.0)(1.0*s - 1.0)
+2.0---------------------------
+   (1.0*s + 5.0)(1.0*s + 10.0)
 
 Continuous-time transfer function model
 ```
@@ -56,10 +57,10 @@ tf(zpk([-1], [1], 2, 0.1))
 
 # output
 
-TransferFunction:
-2.0z + 2.0
-----------
- z - 1.0
+TransferFunction{ControlSystems.SisoRational{Int64}}
+2*z + 2
+-------
+1z - 1
 
 Sample Time: 0.1 (seconds)
 Discrete-time transfer function model

docs/src/man/creatingtfs.md

@@ -18,15 +18,15 @@ Transfer functions can easily be created using the function `tf(num, den, Ts=0)`
 
 Example:
 ```julia
-P = tf([1],[1,1])
+P = tf([1.0],[1,1])
 T = P/(1+P)
 
 # output
 
-TransferFunction:
-    s + 1.0
-----------------
-s^2 + 3.0s + 2.0
+TransferFunction{ControlSystems.SisoRational{Float64}}
+     1.0*s + 1.0
+---------------------
+1.0*s^2 + 3.0*s + 2.0
 
 Continuous-time transfer function model
 ```
@@ -37,10 +37,10 @@ minreal(T)
 
 # output
 
-TransferFunction:
-  1.0
--------
-s + 2.0
+TransferFunction{ControlSystems.SisoRational{Float64}}
+    1.0
+-----------
+1.0*s + 2.0
 
 Continuous-time transfer function model
 ```