Class GainSchedModel

Simulatable gain-scheduled model.

A model for systems that cannot be adequately modelled by UnitModel,because they either have time constants or gains or both that vary signficantly depending on the value of one of the inputs.

One input is selected as the "scheduling varible" and one ore more thresholds are given for this scheduling variable. The thresholds can be set indepently for time-contant and linear gain.

Remember that with more thresholds defined, the higher the requirement for information content in data will be if the model is to be identified from it.

This should not be confused with "gain-scheduled" PID-control, which is a similar concept but applied to PID-control parameters.

See also: GainSchedParameters
Inheritance
object
ModelBaseClass
GainSchedModel
Implements
Inherited Members
Namespace: TimeSeriesAnalysis.Dynamic
Assembly: TimeSeriesAnalysis.dll
Syntax
public class GainSchedModel : ModelBaseClass, ISimulatableModel

Constructors

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GainSchedModel()

Empty constructor

Declaration
public GainSchedModel()
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GainSchedModel(GainSchedParameters, string)

Constructor

Declaration
[JsonConstructor]
public GainSchedModel(GainSchedParameters modelParameters, string ID = "not_named")
Parameters
Type Name Description
GainSchedParameters modelParameters

model parameter object
string ID

a unique string that identifies this model in larger process models

Fields

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modelParameters

Model parameters

Declaration
public GainSchedParameters modelParameters
Field Value
Type Description
GainSchedParameters

Methods

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Clone(string)

Create deep copy/clone

Declaration
public ISimulatableModel Clone(string IDexternal = null)
Parameters
Type Name Description
string IDexternal
Returns
Type Description
ISimulatableModel
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GetFittedDataSet()

Returns the fitted dataset

Declaration
public UnitDataSet GetFittedDataSet()
Returns
Type Description
UnitDataSet
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GetLengthOfInputVector()

Returns the number of external inputs U of the model. Note that this model may have an disturbance signal added to the output in addition to the other signals.

Declaration
public override int GetLengthOfInputVector()
Returns
Type Description
int
Overrides
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GetModelParameters()

Get the objet of model paramters contained in the model

Declaration
public GainSchedParameters GetModelParameters()
Returns
Type Description
GainSchedParameters

Model paramter object
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GetOutputSignalType()

Get the type of output signal

Declaration
public override SignalType GetOutputSignalType()
Returns
Type Description
SignalType
Overrides
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GetSteadyStateInput(double, int, double[])

Calcuate the steady-state input if the output and all-but-one input are known

Declaration
public double? GetSteadyStateInput(double x0, int inputIdx = 0, double[] givenInputs = null)
Parameters
Type Name Description
double x0

If no additive inputs y=x, otherwise subtract additive inputs from y to get x
int inputIdx

the index of the input
double[] givenInputs
Returns
Type Description
double?
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GetSteadyStateOutput(double[], double)

Get the steady state output y for a given input(including additive terms)

Declaration
public double? GetSteadyStateOutput(double[] u0, double badDataID = -9999)
Parameters
Type Name Description
double[] u0
double badDataID

optional special value that indicates "Nan"
Returns
Type Description
double?
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IsModelSimulatable(out string)

Answers if model is simulatable, i.e. has given inputs that are sensible and sufficent.

Declaration
public bool IsModelSimulatable(out string explainStr)
Parameters
Type Name Description
string explainStr
Returns
Type Description
bool
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Iterate(double[], double, double)

Iterates the process model state one time step, based on the inputs given

Declaration
public double[] Iterate(double[] inputs, double timeBase_s, double badValueIndicator = -9999)
Parameters
Type Name Description
double[] inputs

vector of inputs U. Optionally the output disturbance D can be added as the last value.
double timeBase_s

vector of inputs U.Optionally the output disturbance D can be added as the last value.
double badValueIndicator

value in U that is to be treated as NaN
Returns
Type Description
double[]

the updated process model state(x) - the output without any output noise or disturbance. NaN is returned if model was not able to be identfied, or if no good values U values yet have been given. If some data points in U inputsU are NaN or equal to badValueIndicator, the last good value is returned
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SetFittedDataSet(UnitDataSet)

Sets the fitted dataset

Declaration
public void SetFittedDataSet(UnitDataSet dataset)
Parameters
Type Name Description
UnitDataSet dataset
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SetModelParameters(GainSchedParameters)

Update the paramter object of the model

Declaration
public void SetModelParameters(GainSchedParameters parameters)
Parameters
Type Name Description
GainSchedParameters parameters
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ToString()

Create a nice human-readable summary of all the important data contained in the model object. This is especially useful for unit-testing and development.

Declaration
public override string ToString()
Returns
Type Description
string
Overrides
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WarmStart(double[], double)

Warm-starting

Declaration
public void WarmStart(double[] inputs = null, double output = 0)
Parameters
Type Name Description
double[] inputs

not used, leave as null
double output

not used, leave as null

Implements