N4SID and MOESP subspace identification methods

• Published in: 2013 IEEE 9th International Colloquium on Signal Processing and its Applications pp. 140 - 145
• Main Authors: Jamaludin, I. W., Wahab, N. A., Khalid, N. S., Sahlan, S., Ibrahim, Z., Rahmat, M. F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2013
• Subjects: Algorithm design and analysis; Computational modeling; Hankel matrices; Matrix decomposition; MOESP; N4SID; Observability; QR decomposition; Signal processing; Signal processing algorithms; Singular value decomposition; subspace identification
• ISBN: 9781467356084; 1467356085
• DOI: 10.1109/CSPA.2013.6530030

Multivariable Output Error State Space (MOESP) and Numerical algorithms for Subspace State Space System Identification (N4SID) algorithms are two well known subspace identification techniques discussed in this paper. Due to the use of robust numerical tools such as QR decomposition and singular value decomposition (SVD), these identification techniques are often implemented for multivariable systems. Subspace identification algorithms are attractive since the state space form is highly suitable to estimate, predict, filters as well as for control design. In literature, there are several simulation studies for MOESP and N4SID algorithms performed in offline and online mode. In this paper, order selection, validity and the stability for both algorithms for model identification of a glass tube manufacturing process system is considered. The weighting factor α, used in online identification is obtained from trial and error and particle swarm optimization (PSO). Utilizing PSO, the value of α is determined in the online identification and a more accurate result with lower computation time is obtained.