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LimPIDTest.md

LimPIDTest ​

Test bench for a limited PID controller connected to a plant model with step input.

This test component connects a limited PID controller to a plant model and applies a step input as setpoint and a constant feedforward signal. The PID controller includes derivative, integral, and proportional actions with anti-windup and output limitations. The system response can be observed through the plant output and controller signals.

Usage ​

LimPIDTest()

Behavior ​

julia
using BlockComponents #hide
using ModelingToolkit #hide
@named sys = LimPIDTest() #hide
full_equations(sys) #hide
<< @example-block not executed in draft mode >>

Source ​

dyad
# Test bench for a limited PID controller connected to a plant model with step input.
#
# This test component connects a limited PID controller to a plant model and applies a step input as
# setpoint and a constant feedforward signal. The PID controller includes derivative, integral, and
# proportional actions with anti-windup and output limitations. The system response can be observed
# through the plant output and controller signals.
test component LimPIDTest
  # Limited PID controller with configurable parameters
  pid = LimPID(Td=0.1, Ti=0.5, y_max=1, y_min=-1, wp=1, wd=0, Nd=10, Ni=0.9, k_ff=1)
  # Plant model to be controlled
  plant = Plant()
  # Step input signal used as setpoint for the controller
  signal = Step(height=1)
  # Constant signal for feedforward control
  signal_ff = Constant(k=1)
relations
  # Initial condition for the first state of the plant
  initial plant.x1 = 0
  # Initial condition for the plant output
  initial plant.y = 0
  # Connect step signal to controller setpoint input
  connect(signal.y, pid.u_s)
  # Connect plant output to controller measurement input
  connect(plant.y, pid.u_m)
  # Connect controller output to plant input
  connect(pid.y, plant.u)
  # Connect feedforward signal to controller feedforward input
  connect(pid.u_ff, signal_ff.y)
metadata {
  "Dyad": {"tests": {"case1": {"stop": 10, "expect": {"signals": ["plant.y", "pid.y"]}}}}
}
end
Flattened Source
dyad
# Test bench for a limited PID controller connected to a plant model with step input.
#
# This test component connects a limited PID controller to a plant model and applies a step input as
# setpoint and a constant feedforward signal. The PID controller includes derivative, integral, and
# proportional actions with anti-windup and output limitations. The system response can be observed
# through the plant output and controller signals.
test component LimPIDTest
  # Limited PID controller with configurable parameters
  pid = LimPID(Td=0.1, Ti=0.5, y_max=1, y_min=-1, wp=1, wd=0, Nd=10, Ni=0.9, k_ff=1)
  # Plant model to be controlled
  plant = Plant()
  # Step input signal used as setpoint for the controller
  signal = Step(height=1)
  # Constant signal for feedforward control
  signal_ff = Constant(k=1)
relations
  # Initial condition for the first state of the plant
  initial plant.x1 = 0
  # Initial condition for the plant output
  initial plant.y = 0
  # Connect step signal to controller setpoint input
  connect(signal.y, pid.u_s)
  # Connect plant output to controller measurement input
  connect(plant.y, pid.u_m)
  # Connect controller output to plant input
  connect(pid.y, plant.u)
  # Connect feedforward signal to controller feedforward input
  connect(pid.u_ff, signal_ff.y)
metadata {
  "Dyad": {"tests": {"case1": {"stop": 10, "expect": {"signals": ["plant.y", "pid.y"]}}}}
}
end


Test Cases ​

julia
using BlockComponents
using ModelingToolkit, OrdinaryDiffEqDefault
using Plots
using CSV, DataFrames

snapshotsdir = joinpath(dirname(dirname(pathof(BlockComponents))), "test", "snapshots")
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Test Case case1 ​

julia
@mtkbuild model_case1 = LimPIDTest()
u0_case1 = []
prob_case1 = ODEProblem(model_case1, u0_case1, (0, 10))
sol_case1 = solve(prob_case1)
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julia
df_case1 = DataFrame(:t => sol_case1[:t], :actual => sol_case1[model_case1.plant.y])
dfr_case1 = try CSV.read(joinpath(snapshotsdir, "LimPIDTest_case1_sig0.ref"), DataFrame); catch e; nothing; end
plt = plot(sol_case1, idxs=[model_case1.plant.y], width=2, label="Actual value of plant.y")
if !isnothing(dfr_case1)
  scatter!(plt, dfr_case1.t, dfr_case1.expected, mc=:red, ms=3, label="Expected value of plant.y")
end
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julia
plt
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julia
df_case1 = DataFrame(:t => sol_case1[:t], :actual => sol_case1[model_case1.pid.y])
dfr_case1 = try CSV.read(joinpath(snapshotsdir, "LimPIDTest_case1_sig1.ref"), DataFrame); catch e; nothing; end
plt = plot(sol_case1, idxs=[model_case1.pid.y], width=2, label="Actual value of pid.y")
if !isnothing(dfr_case1)
  scatter!(plt, dfr_case1.t, dfr_case1.expected, mc=:red, ms=3, label="Expected value of pid.y")
end
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julia
plt
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