Structure and Parameter Identification of Process Models with Hard Non-linearities for Industrial Drive Trains by Means of Degenerate Genetic Programming

Tantau, Mathias; Perner, Lars; Wielitzka, Mark; Ortmaier, Tobias

doi:10.5220/0007949003680376

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…x ∈ R NS is the states vector, u ∈ R Ni is the input vector and y ∈ R No is the output vector. This also defines the size of the matrices, which can be formed from mass-, stiffness-and damping-matrix, see [31]. The system matrix of all systems considered here contains two eigenvalues at 0 because the force input is integrated twice for the position.…”

Section: B Observabilitymentioning

confidence: 99%

Multisensor-Configuration for Improved Identifiability and Observability of Electromechanical Motion Systems

Tantau

Wielitzka

Ortmaier

et al. 2020

2020 IEEE International Conference on Mechatronics and Automation (ICMA)

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For many problems in the field of control design phenomenological models are required so that the need for parameter identification of given model structures arises. These models can be combined with observers to derive the system states in operation in addition to the parameters. However, identification and observation are limited in accuracy due to the restriction to existing series sensors. In the case of electric drives it is possible that due to elasticities in the structure part of the system is vibrating while the position sensor measures a nearly constant position. In this paper, the use of additional acceleration sensors is investigated in terms of identifiability and observability, which are installed at different points of the structure. The analysis is traced back to measures on the sensitivity matrix, where the integrating behaviour of the plant and the combination of different sensor types (position, velocity, acceleration) require special consideration. An industry-like stacker crane is used as a testbed for validation. It is shown that both identifiability and observability can be improved by the additional sensors in many cases. There is a good agreement between the expected and measured frequency response when the acceleration sensor is mounted on the first or second mass. Deviations only occur when mounted on the load suspension device, which is assumed to be the third mass.

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Section: B Observabilitymentioning

confidence: 99%

Multisensor-Configuration for Improved Identifiability and Observability of Electromechanical Motion Systems

Tantau

Wielitzka

Ortmaier

et al. 2020

2020 IEEE International Conference on Mechatronics and Automation (ICMA)

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“…In the process of model selection an exhaustive search over all combinations of submodels is carried out. More elegant procedures such as genetic programming [17] are avoided here in order to reduce the complexity and the effect of coincidence. For each model the parameters are optimized by matching the calculated and measured frequency response in an equation error formulation.…”

Section: B Model Selectionmentioning

confidence: 99%

Frequency Domain Model Selection for Servo Systems ensuring Practical Identifiability

Tantau

Wielitzka

Ortmaier

et al. 2020

2020 IEEE International Conference on Mechatronics and Automation (ICMA)

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Physically motivated models of servo control systems with coupled mechanics are required for control design, simulation etc. Often, however, the effort of modelling prohibits these model-based methods in industrial applications. Therefore, all approaches of automatic modelling / model selection are naturally appealing. In this paper a procedure for model selection in frequency domain is proposed that minimizes the Kullback-Leibler distance between model and measurement while considering only those models that are practically identifiable. It aims at mechanical models of servo systems including multiplemass resonators. Criteria for practical identifiability are derived locally from the sensitivity matrix which is calculated for different formulations of the equation error. In experiments with two industry-like testbeds the methodology proves to reveal the characteristic mechanical properties of the two setups.

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“…Chen et al (2002); Villwock (2007), works on model selection are mainly limited to data-driven, static models (Hoeting et al (1999)) of other disciplines such as biology (Volinsky et al (1996)) and finance (Draper (1995)). Here, transfer function models of servo systems are identified in frequency domain allowing an intuitive comparison with the measurements and avoiding the difficulty to automatize time domain simulations, see Tantau et al (2019Tantau et al ( , 2020. The challenge with these models is that linearity in the physical parameters is generally not maintained.…”

Section: Introductionmentioning

confidence: 99%

Model selection for servo control systems

Tantau¹,

Perner²,

Wielitzka³

2021

IJMA

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Physically motivated models of electromechanical motion systems are required in several applications related to control design, model-based fault detection and simply interpretation. Often, however, the high effort of modelling prohibits these model-based methods in industrial applications. Therefore, all approaches of automatic modelling / model selection are naturally appealing. An intuitive approach is to identify the parameters of several model candidates and to select the one with the best fit on unseen data. A shortcoming of this approach is that the chosen model may be one with high complexity in which some of the physically interpretable parameters are not practically identifiable and uncertain. Also, ambiguities in selecting the model structure would not be disguised resulting in false confidence in a chosen model. Designing a reasonable set of candidate models requires that distinguishability of models can be checked prior to the identification procedure.This paper proposes a strategy for frequency domain model selection. The resulting model is tailored to ensure practical identifiability of all parameters for the given excitation. The analysis is based on local sensitivity calculated for the frequency domain cost function. Also, the paper describes distinguishability analysis of candidate models utilizing transfer function coefficients and Markov parameters. Model selection and distinguishability analysis are applied to a class of models as they are commonly used to describe servo control systems. It is shown in experiments on an industrial stacker crane that model selection works with little user interaction, except from defining normalized hyperparameters and ensuring that the resulting model is sound. Distinguishability is analysed systematically for all models that result from rearranging actuator, sensor and spring-damper elements along a chain of discrete masses. It can be proved or disproved for almost all combinations of potential models.

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Structure and Parameter Identification of Process Models with Hard Non-linearities for Industrial Drive Trains by Means of Degenerate Genetic Programming

Cited by 5 publications

References 7 publications

Multisensor-Configuration for Improved Identifiability and Observability of Electromechanical Motion Systems

Multisensor-Configuration for Improved Identifiability and Observability of Electromechanical Motion Systems

Frequency Domain Model Selection for Servo Systems ensuring Practical Identifiability

Model selection for servo control systems

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