Identification data

iddata(y, u, x, Ts = nothing)

Returns the appropriate IdData object, depending on the input.

Arguments

• y::AbstractArray: output data (required)
• u::AbstractArray: input data (if available)
• x::AbstractArray: state data (if available)
• Ts::Union{Real,Nothing} = nothing: optional sample time

If the time-series are multivariate, time is in the last dimension.

Operations on iddata

• prefilter
• resample
• append two along the time dimension [d1 d2]
• index time series d[output_index, input_index]
• index the time axis with indices d[time_indices]
• index the time axis with seconds d[3Sec:12Sec] (using ControlSystemIdentification: Sec)
• access number of inputs, outputs and sample time: d.nu, d.ny, d.Ts
• access the time time vector d.t
• premultiply to scale outputs C * d
• postmultiply to scale inputs d * B
• writedlm
• ramp_in, ramp_out

Examples

julia> iddata(randn(10))
Output data of length 10 with 1 outputs

julia> iddata(randn(10), randn(10), 1)
InputOutput data of length 10 with 1 outputs and 1 inputs

julia> d = iddata(randn(2, 10), randn(3, 10), 0.1)
InputOutput data of length 10 with 2 outputs and 3 inputs

julia> [d d] # Concatenate along time
InputOutput data of length 20 with 2 outputs and 3 inputs

julia> d[1:3]
InputOutput data of length 3 with 2 outputs and 3 inputs

julia> d.nu
3

julia> d.t # access time vector
0.0:0.1:0.9

predictiondata(d::AbstractIdData)

Add the output y to the input u_new = [u; y]