System realization from Markov parameters (discrete impulse response data).
Ho-Kalman / Eigensystem Realization Algorithm (ERA) [2][3].
Parameters:
Y (array) – Markov parameters. dimensions: \((p,q,nt)\), where \(p\) = number of outputs,
\(q\) = number of inputs, and \(nt\) = number of Markov parameters.
horizon (int) – (optional) number of block rows in Hankel matrix = order of observability matrix.
default: \(\min(150, (nt-1)/2)\)
nc (int) – (optional) number of block columns in Hankel matrix = order of controllability matrix.
default: \(\min(150, max(nt-1-\)horizon\(, (nt-1)/2))\)
order (int) – (optional) model order. default: \(\min(20,\)horizon\(/2)\)
Returns:
realization in the form of state space coefficients (A,B,C,D)
System realization from Markov parameters (discrete impulse response data).
Eigensystem Realization Algorithm with Data Correlations (ERA/DC) [4].
Parameters:
Y (array) – Markov parameters. dimensions: \((p,q,nt)\), where \(p\) = number of outputs,
\(q\) = number of inputs, and \(nt\) = number of Markov parameters.
horizon (int) – (optional) number of block rows in Hankel matrix = order of observability matrix.
default: \(\min(150, (nt-1)/2)\)
nc (int) – (optional) number of block columns in Hankel matrix = order of controllability matrix.
default: \(\min(150, max(nt-1-\)horizon\(, (nt-1)/2))\)
order (int) – (optional) model order. default: \(\min(20,\)horizon\(/2)\)
a (int) – (optional) \((\\alpha)\) number of block rows in Hankel of correlation matrix. default: 0
b (int) – (optional) \((\\beta)\) number of block columns in Hankel of correlation matrix. default: 0
l (int) – (optional) initial lag for data correlations. default: 0
g (int) – (optional) lags (gap) between correlation matrices. default: 1
Returns:
realization in the form of state space coefficients (A,B,C,D)