Recursive Subspace Identification Algorithm using the Propagator Based Method

Jamaludin, Irma Wani; Wahab, Norhaliza Abdul

doi:10.11591/ijeecs.v6.i1.pp172-179

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, there are many tools that have been utilized to identify the Hammerstein model. There are the iterative method [12][13][14], the subspace method [15][16][17], the least square method [18], the blind approach [19] and the parametric instrumental variables method [20]. Moreover, many also consider the optimization tools for Hammerstein model, such as Bacterial Foraging algorithm [21], Cuckoo search algorithm [22], Particle Swarm optimization [23], and Genetic algorithm [24].…”

Section: Introductionmentioning

confidence: 99%

Grey wolf optimizer for identification of liquid slosh behavior using continuous-time hammerstein model

et al. 2020

View full text Add to dashboard Cite

This paper presents the identification of liquid slosh plant using the Hammerstein model based on Grey Wolf Optimizer (GWO) method. A remote car that carrying a container of liquid is considered as the liquid slosh experimental rig. In contrast to other research works, this paper consider a piece-wise affine function in the nonlinear function of the Hammerstein model, which is more generalized function. Moreover, a continuous-time transfer function is utilized in the Hammerstein model, which is more suitable to represent a real system. The GWO method is used to tune both coefficients in the nonlinear function and transfer function of the Hammerstein model such that the error between the identified output and the real experimental output is minimized. The effectiveness of the proposed framework is assessed in terms of the convergence curve response, output response, and the stability of the identified model through the bode plot and pole zero map. The results show that the GWO based method is able to produce a Hammerstein model that yields identified output response close to the real experimental slosh output.

show abstract

Section: Introductionmentioning

confidence: 99%

Grey wolf optimizer for identification of liquid slosh behavior using continuous-time hammerstein model

et al. 2020

View full text Add to dashboard Cite

show abstract

Identification of Liquid Slosh Behavior Using Continuous-Time Hammerstein Model Based Sine Cosine Algorithm

Jui

Suid

Musa

et al. 2020

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Modified Sine Cosine Algorithm for Identification of Liquid Slosh based on Continuous-time Hammerstein Model

Jui

Suid

Ghazali

et al. 2020

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper presents the identification of liquid slosh plant using the Hammerstein model based on modified Sine Cosine Algorithm (mSCA). A remote car that carries a container of liquid is considered as a liquid slosh experimental rig. In contrast to other research works, this paper considers a piece-wise affine function in a nonlinear function of the Hammerstein model, which is more generalized function. Moreover, a continuous-time transfer function is utilized in the Hammerstein model, which is more suitable to represent a real system. The mSCA method is used to tune both coefficients in the nonlinear function and the transfer function of the Hammerstein model such that the error between the identified output and the real experimental output is minimized. The effectiveness of the proposed framework is assessed in terms of the convergence curve response, output response, and the stability of the identified model through the pole-zero map. The results show that the mSCA based method is able to produce a Hammerstein model that yields identified output response closes to the real experimental slosh output with 82.12 % improvement of sum of quadratic error.

show abstract

Recursive Subspace Identification Algorithm using the Propagator Based Method

Abstract: Subspace model identification (SMI)

Cited by 3 publications

References 13 publications

Grey wolf optimizer for identification of liquid slosh behavior using continuous-time hammerstein model

Grey wolf optimizer for identification of liquid slosh behavior using continuous-time hammerstein model

Identification of Liquid Slosh Behavior Using Continuous-Time Hammerstein Model Based Sine Cosine Algorithm

Modified Sine Cosine Algorithm for Identification of Liquid Slosh based on Continuous-time Hammerstein Model

Contact Info

Product

Resources

About