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DiscreteInputOutputModel[{g0,g1,…,gn-1},u]

represents a discrete-time model with input u and output at sampling instant i.

DiscreteInputOutputModel[{g0,g1,…,gn-1},u,y]

can be used to specify outputs that also depend on the output variables y.

DiscreteInputOutputModel[…,{{u1,{…,u10}},…},{{y1,{…,y10}},…}]

specifies input and output values for each signal for instants k<=0.

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Basic Uses  
TransferFunctionModel  
ModelPredictiveController  
Properties  
Options  
MissingDataMethod  
ResamplingMethod  
SamplingPeriod  
Applications  
Properties & Relations  
Possible Issues  
See Also
Related Guides
History
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DiscreteInputOutputModel

DiscreteInputOutputModel[{g0,g1,…,gn-1},u]

represents a discrete-time model with input u and output at sampling instant i.

DiscreteInputOutputModel[{g0,g1,…,gn-1},u,y]

can be used to specify outputs that also depend on the output variables y.

DiscreteInputOutputModel[…,{{u1,{…,u10}},…},{{y1,{…,y10}},…}]

specifies input and output values for each signal for instants k<=0.

Details and Options

  • DiscreteInputOutputModel represents a system whose output at regularly spaced sampling instants is a function of the inputs and previous outputs of the system.
  • DiscreteInputOutputModel can be used to represent discrete-time systems in input-output form, including discrete-time TransferFunctionModel objects. It is typically used to represent the MPC controller computed in ModelPredictiveController.
  • The input-output model represents a moving window of length m of past input and output according to , for .
  • For k=n,…, this equation is periodically extended with and p=TemplateBox[{k, n}, Mod].
  • By default, the inputs and outputs for are assumed to be .
  • For model predictive control, two simple cases are often used:
  • state feedback controller
    state quasi feedback controller
  • The values between the sampling instances are assumed to be constant by default. It is essentially a zero-order hold (ZOH) with interpolation order .
  • The integer sampling instances ki and outputs can be explicitly specified as DiscreteInputOutputModel[{{k0,g0},…,{kn-1,gn-1}},…].
  • The time ti is related to the sampling instant ki by ti=τ ki, where τ is the sampling period specified as DiscreteInputOutputModel[…, SamplingPeriodτ].
  • DiscreteInputOutputModel["prop"] can be used to compute properties.
  • Properties related to the output specifications {ki,gi}:
  • "FirstInstant"first instant Min[{k0,k1,…}]
    "FirstValue"value at the first instant
    "Instants"sampling instants Sort[{k0,k1,…}]
    "LastInstant"last instant Max[{k0,k1,…}]
    "LastValue"value at the last instant
    "Path"instant-value pairs {{k0,g0},…}
    "PathComponent"first path component
    "PathComponents"all the paths split into univariate components
    "PathFunction"interpolated path function
    "PathLength"length of the path ("Horizon")
    "Values"output values {g0,g1,…}
  • Basic model properties:
  • "InputsCount"number of inputs
    "InputVariables"input variables u
    "OutputsCount"number of outputs
    "OutputVariables"output variables y
    "Type"the type of input-output relationship
    "SamplingPeriod"sampling period sp
  • Time series properties:
  • "FirstTime"time corresponding to the first instant
    "LastTime"time corresponding to the last instant
    "TemporalData"multipath TemporalData object
    "TimePath"instant-value pairs {{t0,g0},…}
    "Times"the times corresponding to the sampling instants
    "TimeSeries"TimeSeries object
    "TimeValues"the output values at the sampling times
  • DiscreteInputOutputModel takes the following options:
  • MissingDataMethod Nonemethod to use for missing values
    ResamplingMethod {"Interpolation", InterpolationOrder0}method to use for resampling paths
    SamplingPeriod Automaticsampling period

Examples

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Basic Examples  (3)

A model whose output depends only on the input:

Wolfram Language code: DiscreteInputOutputModel[{u[0], (u[0] + u[1]) / 2, (u[0] + u[1] + u[2]) / 3}, u]

Its response to a random input sequence:

Wolfram Language code: ListStepPlot[OutputResponse[%, RandomInteger[{-5, 5}, 50]]]

A model whose output depends on the input and output:

Wolfram Language code: DiscreteInputOutputModel[{u[0], u[1] - y[0], u[2] - y[1]}, u, y]

Its response to a sinusoidal input:

Wolfram Language code: ListStepPlot[OutputResponse[%, Table[Sin[t], {t, 0, 4π, 0.1}]]]

A model with nonzero initial conditions:

Wolfram Language code: DiscreteInputOutputModel[{u[0] + y[-1] + y[-2], u[1] + u[0] + u[-1]}, {{u, {-2, -1, 1}}}, {{y, {-1, -1, -1}}}]

Its response to a ramp-like input sequence:

Wolfram Language code: or = OutputResponse[%, Range[-20, 20]]; ListStepPlot[or, DataRange -> {0, 40}]

The response with zero initial conditions is different:

Wolfram Language code: DiscreteInputOutputModel[{u[0] + y[-1] + y[-2], u[1] + u[0] + u[-1]}, u, y]; OutputResponse[%, Range[-20, 20]] == or

Scope  (30)

Basic Uses  (8)

A system whose output is the current input value:

Wolfram Language code: diom = DiscreteInputOutputModel[{u[0], u[1], u[2]}, u]

Its output is the same as the input:

Wolfram Language code: OutputResponse[diom, {a, b, c}]

For longer input sequences, the output is computed assuming periodicity:

Wolfram Language code: OutputResponse[diom, {a, b, c, d, e}]

A system whose output is the average of the previous and current inputs:

Wolfram Language code: diom = DiscreteInputOutputModel[{(u[-1] + u[0]) / 2}, u]
Wolfram Language code: OutputResponse[diom, {a, b, c, d, e}]

A system whose output is the average of the previous output and current input:

Wolfram Language code: diom = DiscreteInputOutputModel[{(y[-1] + u[0]) / 2}, u, y]
Wolfram Language code: OutputResponse[diom, {a, b, c, d, e}]

A system that computes the running maximum value:

Wolfram Language code: diom = DiscreteInputOutputModel[{Max[y[-1], u[0]]}, u, y]
Wolfram Language code: inp = RandomInteger[{-20, 20}, 20]; OutputResponse[diom, inp]

Obtain the same result using FoldList:

Wolfram Language code: Rest[FoldList[Max, 0, inp]]

A multi-output system that computes the running maximum and minimum values:

Wolfram Language code: diom = DiscreteInputOutputModel[{{Max[y1[-1], u[0]], Min[y2[-1], u[0]]}}, u, {y1, y2}]
Wolfram Language code: inp = RandomInteger[{-20, 20}, 20]; OutputResponse[diom, inp]

Obtain the same result using FoldList:

Wolfram Language code: {Rest[FoldList[Max, 0, inp]], Rest[FoldList[Min, 0, inp]]}

Specify nonzero initial values:

Wolfram Language code: diom = DiscreteInputOutputModel[{{Max[y1[-1], u[0]], Min[y2[-1], u[0]]}}, u, {{y1, {-20, -20}}, {y2, {20, 20}}}]

The response to a set of negative inputs:

Wolfram Language code: inp1 = RandomInteger[{-20, -10}, 5]; OutputResponse[diom, inp1]

The response to a set of positive inputs:

Wolfram Language code: inp2 = RandomInteger[{10, 20}, 5]; OutputResponse[diom, inp2]

The model with default zero values:

Wolfram Language code: diomD = DiscreteInputOutputModel[{{Max[y1[-1], u[0]], Min[y2[-1], u[0]]}}, u, {y1, y2}]

The maximum values are erroneously always 0 for negative inputs:

Wolfram Language code: OutputResponse[diomD, inp1]

The minimum values are erroneously always 0 for positive inputs:

Wolfram Language code: OutputResponse[diomD, inp2]

A two-output system with periodicity 3:

Wolfram Language code: DiscreteInputOutputModel[{{u[0], u[0]}, {u[0] - u[1], u[0] + u[1]}, {u[0] - u[1] - u[2], u[0] + u[1] + u[2]}}, u]

Its response to a ramp input:

Wolfram Language code: ListLinePlot[OutputResponse[%, {Range[-50, 50]}], DataRange -> {-20, 20}]

A system with a series of piecewise outputs:

Wolfram Language code: DiscreteInputOutputModel[{Piecewise[{{1, u[0] > 0}, {-1, u[0] <= 0}}], Piecewise[{{1, u[1] > 1}, {-1, u[1] <= 1}}], Piecewise[{{1, u[2] > 2}, {-1, u[2] <= 2}}], Piecewise[{{1, u[3] > 3}, {-1, u[3] <= 3}}]}, u]

Its response to random inputs:

Wolfram Language code: ListStepPlot[OutputResponse[%, RandomReal[{-5, 5}, 50]]]

TransferFunctionModel  (4)

The TransferFunctionModel of a single-input single-output (SISO) system:

Wolfram Language code: tfm = TransferFunctionModel[(z^-1/0.1 z^-1 + 1), z^-1, SamplingPeriod -> 1]

The equivalent DiscreteInputOutputModel:

Wolfram Language code: diom = DiscreteInputOutputModel[{ u[-1] - 0.1 y[-1]}, u, y ]

The responses are the same:

Wolfram Language code: inp = Table[Sin[t], {t, 0, 2 π, 0.1}]; GraphicsRow[Table[ListStepPlot[OutputResponse[sys, inp], DataRange -> {0, 2π}], {sys, {tfm, diom}}]]

The TransferFunctionModel of a multiple-input single-output (MISO) system:

Wolfram Language code: tfm = TransferFunctionModel[{{(z^-1/0.1 z^-1 + 1), (1/0.2 z^-1 + 1)}}, z^-1, SamplingPeriod -> 1]

The equivalent DiscreteInputOutputModel:

Wolfram Language code: diom = DiscreteInputOutputModel[{0.2 Subscript[u, 1][-2] + Subscript[u, 1][-1] + 0.1 Subscript[u, 2][-1] + Subscript[u, 2][0] - 0.02 y[-2] - 0.3 y[-1]}, {Subscript[u, 1], Subscript[u, 2]}, y]

The responses are the same:

Wolfram Language code: inp = Table[{Sin[t], Cos[t]}, {t, 0, 2 π, 0.1}]; GraphicsRow[Table[ListStepPlot[OutputResponse[sys, inp], DataRange -> {0, 2π}], {sys, {tfm, diom}}]]

The TransferFunctionModel of a single-input multiple-output (SIMO) system:

Wolfram Language code: tfm = TransferFunctionModel[{{(z^-1/0.1 z^-1 + 1)}, {(1/0.2 z^-1 + 1)}}, z^-1, SamplingPeriod -> 1]

The equivalent DiscreteInputOutputModel:

Wolfram Language code: diom = DiscreteInputOutputModel[{{ u[-1] - 0.1 Subscript[y, 1][-1], u[0] - 0.2 Subscript[y, 2][-1]}}, u, {Subscript[y, 1], Subscript[y, 2]}]

The responses are the same:

Wolfram Language code: inp = Table[Sin[t], {t, 0, 2 π, 0.1}]; GraphicsRow[Table[ListStepPlot[OutputResponse[sys, inp], DataRange -> {0, 2π}], {sys, {tfm, diom}}]]

The TransferFunctionModel of a multiple-input multiple-output (MIMO) system:

Wolfram Language code: tfm = TransferFunctionModel[{{(z^-1/0.1 z^-1 + 1), (2 z^-1/0.1 z^-1 + 1)}, {(1/0.2 z^-1 + 1), (z^-1/0.2 z^-1 + 1)}}, z^-1, SamplingPeriod -> 1]

The equivalent DiscreteInputOutputModel:

Wolfram Language code: diom = DiscreteInputOutputModel[{{Subscript[u, 1][-1] + 2 Subscript[u, 2][-1] - 0.1 Subscript[y, 1][-1], Subscript[u, 1][0] + Subscript[u, 2][-1] - 0.2 Subscript[y, 2][-1]}}, {Subscript[u, 1], Subscript[u, 2]}, {Subscript[y, 1], Subscript[y, 2]}]

The responses are the same:

Wolfram Language code: inp = Table[{Sin[t], Cos[t]}, {t, 0, 2 π, 0.1}];GraphicsRow[Table[ListStepPlot[OutputResponse[sys, inp], DataRange -> {0, 2π}], {sys, {tfm, diom}}]]

ModelPredictiveController  (2)

The feedback gains model of a ModelPredictiveController design:

Wolfram Language code: ssm = StateSpaceModel[{{{1, 1}, {0, 1}}, {{0}, {1}}}, {x1, x2}, u, SamplingPeriod -> 1];
Wolfram Language code: cost = <|"Horizon" -> 3, "StateWeight" -> {{1, 0}, {0, 1}}, "InputWeight" -> {{0.1}}|>;
Wolfram Language code: cons = {-1 ≤ x1 ≤ 1, -3 ≤ x2 ≤ 3, -1 ≤ u ≤ 1};
Wolfram Language code: fgm = ModelPredictiveController[ssm, cost, cons, "FeedbackGainsModel"]

The feedback gains are a series of piecewise functions of the states:

Wolfram Language code: TabView[Table[i -> fgm[i], {i, 0, 2}]]

The quasi feedback gains model is also a DiscreteInputOutputModel:

Wolfram Language code: olgm = ModelPredictiveController[ssm, cost, cons, "QuasiFeedbackGainsModel"]

It is a series of piecewise functions of the states at time 0:

Wolfram Language code: TabView[Table[i -> olgm[i], {i, 0, 2}]]

The feedback gains model of a multi-input ModelPredictiveController design:

Wolfram Language code: ssm = StateSpaceModel[{{{1, 1}, {0, 1}}, {{0, 1}, {1, 1}}}, {x1, x2}, {u1, u2}, SamplingPeriod -> 1];
Wolfram Language code: cost = <|"Horizon" -> 3, "StateWeight" -> DiagonalMatrix[{1, 1}], "InputWeight" -> DiagonalMatrix[{0.1, 0.2}]|>;
Wolfram Language code: cons = {-2 ≤ x1 ≤ 2, -4 ≤ x2 ≤ 4, -1 ≤ u1 ≤ 1, -1 ≤ u2 ≤ 1};
Wolfram Language code: fgm = ModelPredictiveController[ssm, cost, cons, "FeedbackGainsModel"]

The values of the piecewise functions are of length 2, corresponding to the two inputs:

Wolfram Language code: TabView[Table[i -> Pane[fgm[i], {500, 500}, Scrollbars -> {False, True}], {i, 0, 2}]]

Properties  (16)

Obtain all the values in the model:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["Values"]

The first value:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["FirstValue"]

The last value:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["LastValue"]

The value at a specific instant:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}][1]

All the instants:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["Instants"]

The first instant:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["FirstInstant"]

The last instant:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["LastInstant"]

The list of instant-values pairs:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["Path"]

Split a multivariable path into univariate components:

Wolfram Language code: pc = DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["PathComponents"]

The path of the first component:

Wolfram Language code: pc[[1]]["Path"]

The path of the second component:

Wolfram Language code: pc[[2]]["Path"]

"PathComponent" gives the first component:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["PathComponent"] %["Path"]

Get the second component:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}][{"PathComponent", 2}] %["Path"]

Get several components:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}][{"PathComponent", {1, 2}}] %["Path"]

The interpolated path function:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["PathFunction"]

The path length:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["PathLength"]

Basic model properties:

Wolfram Language code: props = {"InputVariables", "InputsCount", "OutputVariables", "OutputsCount", "Type", "StatesCount", "SamplingPeriod"};
Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}][props]; Dataset[Thread[{props, %}], Alignment -> Left]

Properties specified in terms of time:

Wolfram Language code: props = {"TimeValues", "Times", "FirstTime", "LastTime", "TimePath", "TimeSeries", "TemporalData"};
Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}][props]; Grid[Thread[{props, %}], IconizedObject[«gridOpts»]]

Obtain multiple properties as a list:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}][{"Values", "TimeValues"}]

Obtain all properties as an Association:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["PropertyAssociation"]

Obtain all properties as a Dataset:

Wolfram Language code: DiscreteInputOutputModel[Association["SampledSeries" -> TemporalData[TimeSeries, {{{{u[-1] + u[0] + Subscript[y, 1][-1], u[-2] + Subscript[y, 2][-2]}, {u[1] + Subscript[y, 1][-2], u[0] + u[1] + Subscript[y, 2][0]}, {u[2], u[2] ... "LastValue", "OutputsCount", "OutputVariables", "Path", "PathComponent", "PathComponents", "PathFunction", "PathLength", "SamplingPeriod", "StatesCount", "TemporalData", "TimePath", "Times", "TimeSeries", "TimeValues", "Type", "Values"}]["Dataset"]

Options  (3)

MissingDataMethod  (1)

Specify how to handle missing values:

Wolfram Language code: diom = DiscreteInputOutputModel[{u[0], u[1] + u[0], Missing[], u[3] + u[2]}, u, MissingDataMethod -> {"Interpolation", InterpolationOrder -> 1}]
Wolfram Language code: diom[2]//Simplify

ResamplingMethod  (1)

The default resampling method has interpolation order 0:

Wolfram Language code: diom1 = DiscreteInputOutputModel[{u[0], u[1], u[2]}, u]
Wolfram Language code: {diom1[1], diom1[1.5]}

Specify an interpolation order of 1:

Wolfram Language code: diom2 = DiscreteInputOutputModel[{u[0], u[1], u[2]}, u, ResamplingMethod -> {"Interpolation", InterpolationOrder -> 1}]

The values with interpolation order 1:

Wolfram Language code: {diom2[1], diom2[1.5]}//Simplify

SamplingPeriod  (1)

The default sampling period is 1:

Wolfram Language code: diom1 = DiscreteInputOutputModel[{u[0], u[1], u[2]}, u]

Since the sampling instants and times coincide, "Path" and "TimePath" give the same result:

Wolfram Language code: diom1["Path"]
Wolfram Language code: diom1["TimePath"]

The "TimePath" can also be obtained from the "Path" of the "TimeSeries":

Wolfram Language code: diom1["TimeSeries"]["Path"]

A system with sampling period 2:

Wolfram Language code: diom2 = DiscreteInputOutputModel[{u[0], u[1], u[2]}, u, SamplingPeriod -> 2]

The "Path" is in terms of sampling instants, while "TimePath" is in terms of time values:

Wolfram Language code: diom2["Path"]
Wolfram Language code: diom2["TimePath"] diom2["TimeSeries"]["Path"]

Applications  (3)

Compute a lowpass FIR kernel:

Wolfram Language code: ker = LeastSquaresFilterKernel[{"Lowpass", 1}, 10]

Assemble it is as a DiscreteInputOutputModel:

Wolfram Language code: diom = DiscreteInputOutputModel[{ker.Table[u[i], {i, -9, 0}]}, u]

Compute its response to a noisy sinusoid:

Wolfram Language code: inp = Table[Sin[t] + Sin [15t], {t, 0, 2 π, 0.1}]; or1 = OutputResponse[diom, inp][[1]];
Wolfram Language code: ListStepPlot[{inp, or1}, DataRange -> {0, 2π}]

The same result can be obtained using ListConvolve:

Wolfram Language code: or2 = ListConvolve[ker, inp, 1, 0]; ListStepPlot[{inp, or2}, DataRange -> {0, 2π}]

The two responses are indeed the same:

Wolfram Language code: AllTrue[Chop[or1 - or2], # == 0.&]

Compute the discrete approximation of an IIR filter:

Wolfram Language code: bfm = ButterworthFilterModel[2]; ToDiscreteTimeModel[%, 0.1]//TransferFunctionExpand

Assemble it as a DiscreteInputOutputModel:

Wolfram Language code: diom = DiscreteInputOutputModel[{0.01 u[0] + 0.02 u[-1] + 0.01 u[-2] + 7.978 y[-1] - 3.72716 y[-2]} / 4.2928, u, y]

Compute its response to a noisy sinusoidal input:

Wolfram Language code: inp = Sin[0.5 t] + Sin[10 t];p1 = ListStepPlot[OutputResponse[diom, Table[inp, {t, 0, 25, 0.1}]], DataRange -> {0, 25}, PlotStyle -> RGBColor[0.880722, 0.611041, 0.142051]];Legended[Show[Plot[inp, {t, 0, 25}, PlotStyle -> RGBColor[0.368417, 0.506779, 0.709798]], p1], LineLegend[{RGBColor[0.368417, 0.506779, 0.709798], RGBColor[0.880722, 0.611041, 0.142051]}, {"noisy input", "diom response"}]]

The analog TransferFunctionModel representation gives almost the same response:

Wolfram Language code: Legended[Show[p1, Plot[OutputResponse[bfm, inp, {t, 0, 25}], {t, 0, 25}, PlotStyle -> RGBColor[0.560181, 0.691569, 0.194885]]], LineLegend[{RGBColor[0.880722, 0.611041, 0.142051], RGBColor[0.560181, 0.691569, 0.194885]}, {"diom response", "bfm response"}]]

Compute the feedback gains model and closed-loop system for an MPC design:

Wolfram Language code: ssm = StateSpaceModel[{{{0, 1.}, {-5., -4.5}}, {{0}, {1.}}, {{0., 5.}}, {{0}}}, {Subscript[x, 1], Subscript[x, 2]}, {u}, SamplingPeriod -> 1, SystemsModelLabels -> None];
Wolfram Language code: cost = <|"Horizon" -> 3, "StateWeight" -> IdentityMatrix[2], "InputWeight" -> {{1}}|>;
Wolfram Language code: cons = -2{Subscript[x, 1], Subscript[x, 2], u}2;
Wolfram Language code: {fgm, csys} = ModelPredictiveController[ssm, cost, cons, {"FeedbackGainsModel", "ClosedLoopSystem"}]

The feedback gains model is a discrete input-output model and part of the closed-loop system:

Wolfram Language code: csys[[1]]

Simulate the closed-loop system:

Wolfram Language code: or = OutputResponse[csys, PadRight[{0, 0.05, -0.05}, 30]]; ListStepPlot[or, PlotRange -> All]
Wolfram Language code:

Properties & Relations  (4)

Form a convolution:

Wolfram Language code: OutputResponse[DiscreteInputOutputModel[{Subscript[c, -1] u[-1] + Subscript[c, 1] u[0]}, u], Array[a, 5]][[1]]

ListConvolve gives the same result:

Wolfram Language code: ListConvolve[{Subscript[c, 1], Subscript[c, -1]}, Array[a, 5], 1, 0]
Wolfram Language code: % == %%

A linear recurrence equation:

Wolfram Language code: Array[α, 3].Reverse[Array[y, 3, -2]] == Array[β, 3].Reverse[Array[u, 3, -2]]

Represent it using a DiscreteInputOutputModel:

Wolfram Language code: y[0] /. Solve[%, y[0]] diom = DiscreteInputOutputModel[%, u, y]

Its response to an input sequence:

Wolfram Language code: or = OutputResponse[%, Array[γ, 4]][[1]]//Simplify

RecurrenceFilter gives the same response:

Wolfram Language code: RecurrenceFilter[{Array[α, 3], Array[β, 3]}, Array[γ, 4]] == or//Simplify

A process with a discrete TransferFunctionModel representation:

Wolfram Language code: tfm = TransferFunctionModel[MAProcess[{a, b, c}, σ^2], z]

It also has a DiscreteInputOutputModel representation:

Wolfram Language code: diom = DiscreteInputOutputModel[{{1, a, b, c}.Table[u[i], {i, 0, -3, -1}]}, u]

The responses are the same:

Wolfram Language code: OutputResponse[tfm, Array[r, 4]] OutputResponse[diom, Array[r, 4]] % == %%

Without input and output variables, DiscreteInputOutputModel is essentially a TimeSeries:

Wolfram Language code: diom = DiscreteInputOutputModel[Range[0, 4], {}, {}] %["Path"]

The equivalent TimeSeries object:

Wolfram Language code: TimeSeries[Table[{i, {i}}, {i, 0, 4}]]

Its path:

Wolfram Language code: %["Path"]

Obtain the time series as a property:

Wolfram Language code: diom["TimeSeries"]

It has the same path:

Wolfram Language code: %["Path"]

The time path can also be obtained directly:

Wolfram Language code: diom["TimePath"]

Possible Issues  (2)

Obtaining the time series could change the specification from a sampling instant to time stamps:

Wolfram Language code: diom = DiscreteInputOutputModel[{u[0], u[1], u[2]}, u, SamplingPeriod -> 0.1]

The value at sampling instant 1:

Wolfram Language code: diom[1]

Compute the equivalent time series:

Wolfram Language code: ts = diom["TimeSeries"]

The time series is not in terms of the sampling instants:

Wolfram Language code: ts[1]

Use the equivalent time stamp:

Wolfram Language code: ts[1 0.1]

The time series path:

Wolfram Language code: ts["Path"]

The time series path from the original model is the same:

Wolfram Language code: diom["TimePath"]

However, the path of the original model is in terms of sampling instants:

Wolfram Language code: diom["Path"]

The initial values and are not used in computing the response of the system:

Wolfram Language code: DiscreteInputOutputModel[{u[0] + y[-2], u[1] + y[0] + u[-1]}, {{u, {-2, -1, 1}}}, {{y, {-1, -1, -1}}}]; OutputResponse[%, Range[7]]
Wolfram Language code: DiscreteInputOutputModel[{u[0] + y[-2], u[1] + y[0] + u[-1]}, {{u, {-2, -1, Missing[]}}}, {{y, {-1, -1, Missing[]}}}]; OutputResponse[%, Range[7]]
Wolfram Research (2022), DiscreteInputOutputModel, Wolfram Language function, https://reference.wolfram.com/language/ref/DiscreteInputOutputModel.html.

Text

Wolfram Research (2022), DiscreteInputOutputModel, Wolfram Language function, https://reference.wolfram.com/language/ref/DiscreteInputOutputModel.html.

CMS

Wolfram Language. 2022. "DiscreteInputOutputModel." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/DiscreteInputOutputModel.html.

APA

Wolfram Language. (2022). DiscreteInputOutputModel. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/DiscreteInputOutputModel.html

BibTeX

@misc{reference.wolfram_2026_discreteinputoutputmodel, author="Wolfram Research", title="{DiscreteInputOutputModel}", year="2022", howpublished="\url{https://reference.wolfram.com/language/ref/DiscreteInputOutputModel.html}", note=[Accessed: 04-October-2026]}

BibLaTeX

@online{reference.wolfram_2026_discreteinputoutputmodel, organization={Wolfram Research}, title={DiscreteInputOutputModel}, year={2022}, url={https://reference.wolfram.com/language/ref/DiscreteInputOutputModel.html}, note=[Accessed: 04-October-2026]}