Modeling of a dynamic dual-input dual-output fast steeringmirror system

Song, Hong; Zhang, Jun; Yang, Ping; Huang, Haocai; Zhan, Shuyue; Liu, Tengjun; Guo, Yilu; Wang, Hangzhou; Huang, Hui; Mu, Quanquan; Fang, Meifen; Yang, Mingyuan

doi:10.1631/fitee.1601221

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…However, only the single-axis piezostage is explored in the above-mentioned works. In 2017, a DIDO piezostage was studied by a subspace identification algorithm (Hong et al, 2017). In addition, a comprehensive method was also proposed in the literature (Wang et al, 2019a(Wang et al, , 2019b to identify a DIDO piezo actuated steering stage.…”

Section: Introductionmentioning

confidence: 99%

A systematic identification approach for biaxial piezoelectric stage with coupled Duhem-type hysteresis

Chen

Yang

2021

COMPEL

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Purpose The problem of parameter identification for biaxial piezoelectric stages is still a challenging task because of the existing hysteresis, dynamics and cross-axis coupling. This study aims to find an accurate and systematic approach to tackle this problem. Design/methodology/approach First, a dual-input and dual-output (DIDO) model with Duhem-type hysteresis is proposed to depict the dynamic behavior of the biaxial piezoelectric stage. Then, a systematic identification approach based on a modified differential evolution (DE) algorithm is proposed to identify the unknown parameters of the Duhem-type DIDO model for a biaxial piezostage. The randomness and parallelism of the modified DE algorithm guarantee its high efficiency. Findings The experimental results show that the characteristics of the biaxial piezoelectric stage can be identified with adequate accuracy based on the input–output data, and the peak-valley errors account for 2.8% of the full range in the X direction and 1.5% in the Y direction. The attained results validated the correctness and effectiveness of the presented identification method. Originality/value The classical DE algorithm has many adjustment parameters, which increases the inconvenience and difficulty of using in practice. The parameter identification of Duhem-type DIDO piezoelectric model is rarely studied in detail and its successful application based on DE algorithm on a biaxial piezostage is hitherto unexplored. To close this gap, this work proposed a modified DE-based systematic identification approach. It not only can identify this complicated model with more parameters, but also has little tuning parameters and thus is easy to use.

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Section: Introductionmentioning

confidence: 99%

A systematic identification approach for biaxial piezoelectric stage with coupled Duhem-type hysteresis

Chen

Yang

2021

COMPEL

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Efficient parameter estimation for second order plus dead time systems in process plant control

Pal,

Banerjee,

Dam

2024

Adv Control Appl

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Designing a controller for a process plant typically modeled as a first or second‐order system with dead time involves an efficient and accurate estimation of its parameters. Since many process plants are characterized as second‐order plus dead time (SOPDT) overdamped or critically damped systems, this study presents a straightforward parameter estimation method using transient response data from a step input at three specific time instants. Two‐time domain performance indices (PIs), (= for system dead time = 0 and for system dead time ≠ 0; , and are respectively the times at which the response reaches 5%, 10% and 90% of the steady‐state response) and (reciprocal of rise time ) are proposed in this work. Correlations are established between these PIs and the parameters of the SOPDT system. The first correlation establishes that is a function of the damping ratio only and is independent of the undamped natural frequency which means that the evaluation of from the time response data is sufficient to estimate . The second correlation establishes that for a given , is a function of . Thus the evaluation of from the time response data and an estimate of from the first correlation is sufficient to estimate . The proposed algorithm estimates the parameters of the canonical transfer function of the plant by these correlations from the three specific time instants, as mentioned above, of the step response of the plant. Comparisons with four methods existing in the literature reveal that the method is also effective with higher order systems that may be approximated by SOPDT dynamics, and robust even with measurement noise, making it useful for implementing control strategies, not only in process plants but also in other systems having similar time responses.

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