FlightSims

FlightSims.jl is a general-purpose numerical simulator supporting nested environments and convenient macro-based data logging.

Road map

  • ROS2 compatibility (not urgent)

NEWS

APIs

Main APIs can be found in FSimBase.jl. In FlightSims.jl, the default differential equation (DE) solver is Tsit5() for ordinary DE (ODE).

Features

If you want more functionality, please feel free to report an issue!

Nested Environments and Zoo

  • Environments usually stand for dynamical systems but also include other utilities, for example, controllers.
  • One can generate user-defined nested environments using provided APIs. Also, some predefined environments are provided for reusability. Take a look at FSimZoo.jl.

Utilities

  • Some utilities are also provided for dynamical system simulation.
  • Examples include
    • Simulation rendering
      • See FSimPlots.jl. Note that FSimPlots.jl is not exported in the default setting to reduce precompilation time.

Examples

Basic: minimal examples

  • For minimal examples of FlightSims.jl, see FSimBase.jl.

Optimal control and reinforcement learning

  • For an example of infinite-horizon continuous-time linear quadratic regulator (LQR), see the following example code (test/environments/basics/lqr.jl).
using FlightSims
const FS = FlightSims
using DifferentialEquations
using LinearAlgebra
using Plots
using Test
using Transducers


function test()
    # linear system
    A = [0 1;
         0 0]  # 2 x 2
    B = [0 1]'  # 2 x 1
    n, m = 2, 1
    env = LinearSystem(A, B)  # exported from FlightSims
    x0 = State(env)([1.0, 2.0])
    p0 = zero.(x0)  # auxiliary parameter
    # optimal control
    Q = Matrix(I, n, n)
    R = Matrix(I, m, m)
    lqr = LQR(A, B, Q, R)  # exported from FlightSims
    u_lqr = Command(lqr)  # (x, p, t) -> -K*x; minimise J = ∫ (x' Q x + u' R u) from 0 to ∞

    # simulation
    tf = 10.0
    Δt = 0.01
    affect!(integrator) = integrator.p = copy(integrator.u)  # auxiliary callback funciton
    cb = PeriodicCallback(affect!, Δt; initial_affect=true)  # auxiliary callback
    @Loggable function dynamics!(dx, x, p, t)
        @onlylog p  # activate this line only when logging data
        u = u_lqr(x)
        @log x, u
        @nested_log Dynamics!(env)(dx, x, p, t; u=u)  # exported `state` and `input` from `Dynamics!(env)`
    end
    prob, df = sim(
                   x0,  # initial condition
                   dynamics!,  # dynamics with input of LQR
                   p0;
                   tf=tf,  # final time
                   callback=cb,
                   savestep=Δt,
                  )
    ts = df.time
    xs = df.sol |> Map(datum -> datum.x) |> collect
    us = df.sol |> Map(datum -> datum.u) |> collect
    ps = df.sol |> Map(datum -> datum.p) |> collect
    states = df.sol |> Map(datum -> datum.state) |> collect
    inputs = df.sol |> Map(datum -> datum.input) |> collect
    @test xs == states
    @test us == inputs
    p_x = plot(ts, hcat(states...)';
               title="state variable", label=["x1" "x2"], color=[:black :black], lw=1.5,
              )  # Plots
    plot!(p_x, ts, hcat(ps...)';
          ls=:dash, label="param", color=[:red :orange], lw=1.5
         )
    savefig("figures/x_lqr.png")
    plot(ts, hcat(inputs...)'; title="control input", label="u")  # Plots
    savefig("figures/u_lqr.png")
    df
end

julia> test()
1001×2 DataFrame
  Row │ time     sol
      │ Float64  NamedTup…
──────┼────────────────────────────────────────────
    1 │    0.0   (p = [1.01978, 1.95564], state =…
    2 │    0.01  (p = [1.01978, 1.95564], state =…
    3 │    0.02  (p = [1.03911, 1.91186], state =…
    4 │    0.03  (p = [1.05802, 1.86863], state =…
    5 │    0.04  (p = [1.07649, 1.82596], state =…
  ⋮   │    ⋮                     ⋮
  998 │    9.97  (p = [-0.00093419, 0.00103198], …
  999 │    9.98  (p = [-0.000923913, 0.00102347],…
 1000 │    9.99  (p = [-0.00091372, 0.001015], st…
 1001 │   10.0   (p = [-0.00091372, 0.001015], st…
                                   992 rows omitted

ex_screenshot ex_screenshot

Multicopter position control

  • For an example of backstepping position tracking controller for quadcopters, see test/environments/integrated_environments/backstepping_position_controller_static_allocator_multicopter_env.jl.

Missile guidance with interactive visualisation

  • See test/pluto_guidance.jl (thanks to @nhcho91).

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Multicopter rendering

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FSim family

  • FSimBase.jl is the lightweight base package for numerical simulation supporting nested dynamical systems and macro-based data logger. For more functionality, see FlightSims.jl.
  • FSimZoo.jl contains predefined environments and controllers for FlightSims.jl.
  • FSimPlots.jl is the plotting package for predefined environments exported from FlightSims.jl

Packages using FlightSims.jl

  • FaultTolerantControl.jl: fault tolerant control (FTC) with various models and algorithms of faults, fault detection and isolation (FDI), and reconfiguration (R) control.
  • FlightGNC.jl (@nhcho91): FlightGNC.jl is a Julia package containing GNC algorithms for autonomous systems.

Useful packages

Trouble shootings

sim produces an empty Dataframe

  • Please check whether you put @Loggable in front of the dynamics function in a proper way, e.g.,
function Dynamics!(env::MyEnv)
    @Loggable function dynamics!(dx, x, p, t; u)
    # return @Loggable dynamics!(dx, x, p, t; u)  # This would not work
        # blahblah...
    end
end