Linear Motor Command Tracking: A Novel Immersion and Invariance Adaptive Control Method with Arctangent-Function-Based Parameter Projection

Zhou, Xingyu; Shen, Heran; Wang, Zejiang; Ahn, Hyunjin; Wang, Junmin

doi:10.1016/j.ifacol.2022.11.195

Cited by 2 publications

(1 citation statement)

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“…Parameter variations are a major source of non-periodic uncertainties, and some online parameter adaptation methods can be used to compensate for uncertainties caused by parameter variations. An adaptive immersion and invariance control method (AIIC) has been proposed for parameter adaptation in [29]- [31]. AIIC introduces a tuning function on parameter estimation to shape the convergence dynamics.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems

Liu,

Yu,

Lin

et al. 2024

IEEE Open J. Ind. Electron. Soc.

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Repetitive motion is one of the most common motion tasks in linear motor (LM) driven system. The LM performs repetitive motion based on periodic target trajectory under control, thus leading to periodic characteristics in certain system uncertainties. For this type of tasks, this article proposes an iterative learning observer-based high-precision motion control scheme, which comprehensively considers high-accuracy model compensation and periodic uncertainties estimation. A recursive least squares (RLS) algorithm-based indirect adaptation strategy is used to achieve high-accuracy parameter estimation and model compensation. A saturated constrained-type iterative learning observer is designed to effectively estimate and compensate for periodic uncertainties. The closed-loop stability of the system is guaranteed in the presence of both periodic and non-periodic uncertainties due to the composite adaptive robust control design. Comparative experiments are conducted on an LM driven motion platform to verify the effectiveness and advantages of the proposed control scheme. Furthermore, the experimental results confirm the enhancement of both transient and steady-state performance of the system.INDEX TERMS Adaptive robust control, iterative learning observer, linear motor, motion control, repetitive motion task.

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Section: Introductionmentioning

confidence: 99%