Efficient low-order system identification from low-quality step response data with rank-constrained optimization

This paper describes three step response-based system identification methods of increasing complexity, together with a range of exercises that will enhance student understanding of this area in an engaging and practical way. For illustration purposes and practicality, it is assumed that the model to be identified is of the first-order plus dead time type. The first method uses a popular graphical technique, which is easy to understand and apply, but inaccurate when the response data are not ideal. The second uses the Nelder–Mead simplex method, which is a more powerful technique and has the added benefit of introducing undergraduate students to the concepts of numerical optimisation. The third uses an integral equation algorithm. The latter two methods, which can be readily extended to other model structures and input types, are also demonstrated using experimental data obtained from a tank level control system.

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Low-order system identification and optimal control of intersample behavior in ILC

2009 American Control Conference ◽

10.1109/acc.2009.5159951 ◽

2009 ◽

Cited By ~ 1

Author(s):

Tom Oomen ◽

Jeroen van de Wijdeven ◽

Okko Bosgra

Keyword(s):

Optimal Control ◽

System Identification ◽

Order System ◽

Low Order

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Evolutionary system identification in the time domain

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1243/0959651971539849 ◽

1997 ◽

Vol 211 (5) ◽

pp. 319-323 ◽

Cited By ~ 5

Author(s):

K C Tan ◽

Y Li

Keyword(s):

System Identification ◽

Identification Problem ◽

Step Response ◽

Multivariable System ◽

Test Rig ◽

Response Data ◽

Discrete Time Systems ◽

Non Linear Systems ◽

The Time Domain ◽

Time Systems

This paper develops a genetic algorithm based technique that may be used to identify multivariable system identification directly from plant step response data. Using this technique, globally optimized models for linear and non-linear systems can be identified without the need for a differentiable cost function or linearly separable parameters. Results are validated against a benchmark identification problem and a laboratory test-rig for continuous and discrete-time systems.

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Time-domain identification of One Noninteger Order Plus Time Delay models from step response measurements

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962319410113 ◽

2019 ◽

Vol 10 (01) ◽

pp. 1941011 ◽

Cited By ~ 4

Author(s):

Baris Baykant Alagoz ◽

Aleksei Tepljakov ◽

Abdullah Ates ◽

Eduard Petlenkov ◽

Celaleddin Yeroglu

Keyword(s):

Time Delay ◽

Controller Design ◽

Pso Algorithm ◽

Step Response ◽

Order System ◽

Domain Identification ◽

Response Data ◽

Numerical Solution Methods ◽

Solution Methods ◽

Experimental Step

Practical performances of controller design methods strongly depend on relevancy of identified models. Fractional order system models promise advantage of more accurate modeling of real systems. This study presents a discussion on utilization of two fundamental numerical solution methods of fractional calculus in identification problems of One Noninteger Order Plus Time Delay with one pole (NOPTD-I) models. The identification process is carried out by estimating parameters of a NOPTD-I type transfer function template so that the step response of the NOPTD-I model can sufficiently fit experimental step response data. In the study, step responses of NOPTD-I models are numerically calculated according to two fundamental methods, which are Mittag-Leffler (ML) function and Grunwald–Letnikov (GL) definition. Particle swarm optimization (PSO) algorithm is used to perform data fitting. Illustrative examples are presented to evaluate model parameter estimation performances of these two methods for synthetically generated noisy test data. An experimental study is conducted for modeling pitch rotor of TRMS to compare experimental performances.

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