MinimaxAdaptiveControl.evaluateMethod
evaluate(v::AbstractPerformanceMetric{T}) where T<:Real

Evaluate the value of a performance metric.

Arguments

  • v::AbstractPerformanceMetric{T}: The performance metric to be evaluated.

Returns

  • T: The value of the performance metric.
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MinimaxAdaptiveControl.observeMethod
observe(plant::SSPlant{T}, measurementDisturbance::AbstractVector{T}) where T<:Real

Observe the output of a state-space plant given measurement disturbances.

Arguments

  • plant::SSPlant{T}: The state-space plant.
  • measurementDisturbance::AbstractVector{T}: The measurement disturbance vector.

Returns

  • Vector{T}: The observed output vector.
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MinimaxAdaptiveControl.observeMethod
observe(plant::OFPlant{T}, measurementDisturbance::AbstractVector{T}) where T<:Real

Observe the output of an output-feedback plant given measurement disturbances.

Arguments

  • plant::OFPlant{T}: The output-feedback plant.
  • measurementDisturbance::AbstractVector{T}: The measurement disturbance vector.

Returns

  • Vector{T}: The observed output vector.

Example

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MinimaxAdaptiveControl.simulate!Function
simulate!(states::AbstractMatrix{T}, outputs::AbstractMatrix{T}, controls::AbstractMatrix{T},
          processDisturbances::AbstractMatrix{T}, measurementDisturbances::AbstractMatrix{T},
          metricResults::AbstractMatrix{X}, metrics::AbstractVector{M}, plant::AbstractPlant{T},
          controller::AbstractController{T}, duration::N) where T<:Real where N<:Integer where M <: AbstractPerformanceMetric where X

Simulate the control system for a given duration, updating states, outputs, controls, and metrics.

Arguments

  • states::AbstractMatrix{T}: Matrix to store the state vectors over time.
  • outputs::AbstractMatrix{T}: Matrix to store the output vectors over time.
  • controls::AbstractMatrix{T}: Matrix to store the control vectors over time.
  • processDisturbances::AbstractMatrix{T}: Matrix of process disturbance vectors.
  • measurementDisturbances::AbstractMatrix{T}: Matrix of measurement disturbance vectors.
  • metricResults::AbstractMatrix{X}: Matrix to store metric evaluation results.
  • metrics::AbstractVector{M}: Vector of performance metrics to be evaluated.
  • plant::AbstractPlant{T}: The plant model being controlled.
  • controller::AbstractController{T}: The controller used to control the plant.
  • duration::N: The duration for which the simulation runs.

Description

This function simulates the control system over the specified duration. It updates the plant states, controller, and performance metrics at each time step.

Example

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MinimaxAdaptiveControl.update!Method
update!(plant::SSPlant{T}, controls::AbstractVector{T}, processDisturbances::AbstractVector{T}) where T<:Real

Update the state of a state-space plant given control inputs and process disturbances.

Arguments

  • plant::SSPlant{T}: The state-space plant to be updated.
  • controls::AbstractVector{T}: The control input vector.
  • processDisturbances::AbstractVector{T}: The process disturbance vector.

Returns

  • Vector{T}: The updated state vector.
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MinimaxAdaptiveControl.update!Method
update!(plant::OFPlant{T}, controls::AbstractVector{T}, processDisturbances::AbstractVector{T}) where T<:Real

Update the state of an output-feedback plant given control inputs and process disturbances.

Arguments

  • plant::OFPlant{T}: The output-feedback plant to be updated.
  • controls::AbstractVector{T}: The control input vector.
  • processDisturbances::AbstractVector{T}: The process disturbance vector.

Returns

  • Vector{T}: The updated state vector.
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MinimaxAdaptiveControl.updateMethod
update(id::InducedlpGain{T, N}, input::AbstractVector{T}, output::AbstractVector{T}) where T<:Real where N<:Integer

Update the induced l_p gain metric with new input and output data.

Arguments

  • id::InducedlpGain{T, N}: The induced l_p gain metric to be updated.
  • input::AbstractVector{T}: The input vector.
  • output::AbstractVector{T}: The output vector.

Returns

  • InducedlpGain{T, N}: The updated induced l_p gain metric.
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MinimaxAdaptiveControl.updateMethod
update(v::ValueFunction{T}, input::AbstractVector{T}, output::AbstractVector{T}) where T<:Real

Update the value function with new input and output data.

Arguments

  • v::ValueFunction{T}: The value function to be updated.
  • input::AbstractVector{T}: The input vector.
  • output::AbstractVector{T}: The output vector.

Returns

  • ValueFunction{T}: The updated value function.
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