Functions: Simulations and Plots
This page documents the functions for quick plotting of open- and closed-loop simulations. They are generic to SimModel
, StateEstimator
and PredictiveController
types. A SimResult
instance must be created first with its constructor or by calling sim!
. The results are then visualized with plot
function from Plots.jl
.
Quick Simulations
ModelPredictiveControl.sim!
— Functionsim!(plant::SimModel, N::Int, u=plant.uop.+1, d=plant.dop; x_0=plant.xop) -> res
Open-loop simulation of plant
for N
time steps, default to unit bump test on all inputs.
The manipulated inputs $\mathbf{u}$ and measured disturbances $\mathbf{d}$ are held constant at u
and d
values, respectively. The plant initial state $\mathbf{x}(0)$ is specified by x_0
keyword arguments. The function returns SimResult
instances that can be visualized by calling plot
on them. Note that the method mutates plant
internal states.
Examples
julia> plant = NonLinModel((x,u,d,_)->0.1x+u+d, (x,_,_)->2x, 5, 1, 1, 1, 1, solver=nothing);
julia> res = sim!(plant, 15, [0], [0], x_0=[1])
Simulation results of NonLinModel with 15 time steps.
sim!(
estim::StateEstimator,
N::Int,
u = estim.model.uop .+ 1,
d = estim.model.dop;
<keyword arguments>
) -> res
Closed-loop simulation of estim
estimator for N
steps, default to input bumps.
See Arguments for the available options. The noises are provided as standard deviations σ vectors. The simulated sensor and process noises of plant
are specified by y_noise
and x_noise
arguments, respectively.
Arguments
Keyword arguments with emphasis
are non-Unicode alternatives.
estim::StateEstimator
: state estimator to simulateN::Int
: simulation length in time stepsu = estim.model.uop .+ 1
: manipulated input $\mathbf{u}$ valued = estim.model.dop
: plant measured disturbance $\mathbf{d}$ valueplant::SimModel = estim.model
: simulated plant modelu_step = zeros(plant.nu)
: step load disturbance on plant inputs $\mathbf{u}$u_noise = zeros(plant.nu)
: gaussian load disturbance on plant inputs $\mathbf{u}$y_step = zeros(plant.ny)
: step disturbance on plant outputs $\mathbf{y}$y_noise = zeros(plant.ny)
: additive gaussian noise on plant outputs $\mathbf{y}$d_step = zeros(plant.nd)
: step on measured disturbances $\mathbf{d}$d_noise = zeros(plant.nd)
: additive gaussian noise on measured dist. $\mathbf{d}$x_noise = zeros(plant.nx)
: additive gaussian noise on plant states $\mathbf{x}$x_0 = plant.xop
: plant initial state $\mathbf{x}(0)$x̂_0 = nothing
orxhat_0
: initial estimate $\mathbf{x̂}(0)$,initstate!
is used ifnothing
lastu = plant.uop
: last plant input $\mathbf{u}$ for $\mathbf{x̂}$ initialization
Examples
julia> model = LinModel(tf(3, [30, 1]), 0.5);
julia> estim = KalmanFilter(model, σR=[0.5], σQ=[0.25], σQint_ym=[0.01], σPint_ym_0=[0.1]);
julia> res = sim!(estim, 50, [0], y_noise=[0.5], x_noise=[0.25], x_0=[-10], x̂_0=[0, 0])
Simulation results of KalmanFilter with 50 time steps.
sim!(
mpc::PredictiveController,
N::Int,
ry = mpc.estim.model.yop .+ 1,
d = mpc.estim.model.dop,
ru = mpc.estim.model.uop;
<keyword arguments>
) -> res
Closed-loop simulation of mpc
controller for N
steps, default to output setpoint bumps.
The output and manipulated input setpoints are held constant at ry
and ru
, respectively. The keyword arguments are identical to sim!(::StateEstimator, ::Int)
.
Examples
julia> model = LinModel([tf(3, [30, 1]); tf(2, [5, 1])], 4);
julia> mpc = setconstraint!(LinMPC(model, Mwt=[0, 1], Nwt=[0.01], Hp=30), ymin=[0, -Inf]);
julia> res = sim!(mpc, 25, [0, 0], y_noise=[0.1], y_step=[-10, 0])
Simulation results of LinMPC with 25 time steps.
Simulation Results
ModelPredictiveControl.SimResult
— TypeSimResult(obj::SimModel, U_data, Y_data, D_data=[]; <keyword arguments>)
SimResult(obj::StateEstimator, U_data, Y_data, D_data=[]; <keyword arguments>)
SimResult(obj::PredictiveController, U_data, Y_data, D_data=[]; <keyword arguments>)
Manually construct a SimResult
to quickly plot obj
simulations.
Except for obj
, all the arguments should be matrices of N
columns, where N
is the number of time steps. SimResult
objects allow to quickly plot simulation results. Simply call plot
on them.
Arguments
Keyword arguments with emphasis
are non-Unicode alternatives.
obj
: simulatedSimModel
/StateEstimator
/PredictiveController
U_data
: manipulated inputsY_data
: plant outputsD_data=[]
: measured disturbancesX_data=nothing
: plant statesX̂_data=nothing
orXhat_data
: estimated statesŶ_data=nothing
orYhat_data
: estimated outputsRy_data=nothing
: plant output setpointsRu_data=nothing
: manipulated input setpointsplant=get_model(obj)
: simulated plant model, default toobj
internal plant model
Examples
julia> model = LinModel(tf(1, [1, 1]), 1.0);
julia> N = 5; U_data = fill(1.0, 1, N); Y_data = zeros(1, N);
julia> for i=1:N; updatestate!(model, U_data[:, i]); Y_data[:, i] = model(); end; Y_data
1×5 Matrix{Float64}:
0.632121 0.864665 0.950213 0.981684 0.993262
julia> res = SimResult(model, U_data, Y_data)
Simulation results of LinModel with 5 time steps.
Plotting Results
The plot
methods are based on Plots.jl
package. To install it run using Pkg; Pkg.add("Plots")
in the Julia REPL.
ModelPredictiveControl.plot
— Functionplot(res::SimResult{<:Real, <:SimModel}; <keyword arguments>)
Plot the simulation results of a SimModel
.
Arguments
The keyword arguments can be Bool
s, index ranges (2:4
) or vectors ([1, 3]
), to select the variables to plot.
res::SimResult{<:Real, <:SimModel}
: simulation results to plotploty=true
: plot plant outputs $\mathbf{y}$plotu=true
: plot manipulated inputs $\mathbf{u}$plotd=true
: plot measured disturbances $\mathbf{d}$ if applicableplotx=false
: plot plant states $\mathbf{x}$
Examples
julia> res = sim!(LinModel(tf(2, [10, 1]), 2.0), 25);
julia> using Plots; plot(res, plotu=false)
plot(res::SimResult{<:Real, <:StateEstimator}; <keyword arguments>)
Plot the simulation results of a StateEstimator
.
Arguments
The keyword arguments can be Bool
s, index ranges (2:4
) or vectors ([1, 3]
), to select the variables to plot. Keywords in emphasis
are non-Unicode alternatives.
res::SimResult{<:Real, <:StateEstimator}
: simulation results to plotplotŷ=true
orplotyhat
: plot estimated outputs $\mathbf{ŷ}$plotx̂=false
orplotxhat
: plot estimated states $\mathbf{x̂}$plotxwithx̂=false
orplotxwithxhat
: plot plant states $\mathbf{x}$ and estimated states $\mathbf{x̂}$ togetherplotx̂min=true
orplotxhatmin
: plot estimated state lower bounds $\mathbf{x̂_{min}}$ if applicableplotx̂max=true
orplotxhatmax
: plot estimated state upper bounds $\mathbf{x̂_{max}}$ if applicable<keyword arguments>
ofplot(::SimResult{<:Real, <:SimModel})
Examples
julia> res = sim!(KalmanFilter(LinModel(tf(3, [2.0, 1]), 1.0)), 25, [0], y_step=[1]);
julia> using Plots; plot(res, plotu=false, plotŷ=true, plotxwithx̂=true)
plot(res::SimResult{<:Real, <:PredictiveController}; <keyword arguments>)
Plot the simulation results of a PredictiveController
.
Arguments
The keyword arguments can be Bool
s, index ranges (2:4
) or vectors ([1, 3]
), to select the variables to plot.
res::SimResult{<:Real, <:PredictiveController}
: simulation results to plotplotry=true
: plot plant output setpoints $\mathbf{r_y}$ if applicableplotymin=true
: plot predicted output lower bounds $\mathbf{y_{min}}$ if applicableplotymax=true
: plot predicted output upper bounds $\mathbf{y_{max}}$ if applicableplotru=true
: plot manipulated input setpoints $\mathbf{r_u}$ if applicableplotumin=true
: plot manipulated input lower bounds $\mathbf{u_{min}}$ if applicableplotumax=true
: plot manipulated input upper bounds $\mathbf{u_{max}}$ if applicable<keyword arguments>
ofplot(::SimResult{<:Real, <:SimModel})
<keyword arguments>
ofplot(::SimResult{<:Real, <:StateEstimator})
Examples
julia> model = LinModel(tf(2, [5.0, 1]), 1.0);
julia> res = sim!(setconstraint!(LinMPC(model), umax=[1.0]), 25, [0], u_step=[-1]);
julia> using Plots; plot(res, plotŷ=true, plotry=true, plotumax=true, plotx̂=[2])