A new prediction error identification method for multi-input-multi-output processes

A new prediction error identification method is proposed to identify multi-input-multi-output (MIMO) processes and the initial state. It linearizes the model output with respect to the model parameters so that optimal model parameters can be obtained in an analytic way. And, it has potential to provide a faster convergence rate and a better local minimum in solving the nonlinear optimization problem.

[1]  Lennart Ljung,et al.  Closed-loop identification revisited , 1999, Autom..

[2]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[3]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..

[4]  Michel Verhaegen,et al.  Subspace Algorithms for the Identification of Multivariable Dynamic Errors-in-Variables Models , 1997, Autom..

[5]  Dynamic trend analysis for ultrasonic irradiation in control of suspension polymerization process , 2009, 2009 ICCAS-SICE.

[6]  Mats Viberg,et al.  Subspace-based methods for the identification of linear time-invariant systems , 1995, Autom..

[7]  Lennart Ljung,et al.  Subspace identification from closed loop data , 1996, Signal Process..

[8]  Wallace E. Larimore,et al.  Statistical optimality and canonical variate analysis system identification , 1996, Signal Process..

[9]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[10]  Manfred Deistler,et al.  Consistency and Relative Efficiency of Subspace Methods , 1994 .

[11]  Su Whan Sung,et al.  Relay feedback methods for noisy processes , 2009, 2009 ICCAS-SICE.

[12]  Bart De Moor,et al.  A unifying theorem for three subspace system identification algorithms , 1995, Autom..

[13]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[14]  Dietmar Bauer,et al.  Consistency and asymptotic normality of some subspace algorithms for systems without observed inputs , 1999, Autom..

[15]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.