Online Optimization Control With Predictive Gradient Descent for MEMS Micro-Mirrors

Tan, Yonghong; Dong, Ruili; Li, Zhongzhen; Chai, Guo; Li, Meng

doi:10.1109/tie.2021.3097598

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…Subsequently, the amplified signal is fed to the PC using A/D converter (DS2004-BL1), which based on the real-time signal acquisition platform. Meanwhile, this amplified signal is also used to generate the optimizing control value via the cost function and set-trajectory [2,14]. The corresponding control framework is shown in Figure 9, which including an electromagnetic scanning micromirror, a driving circuit, a dSpace real-time acquisition platform and a PC.…”

Section: Resultsmentioning

confidence: 99%

“…Most closed-loop control tracking strategies with torsional micromirrors are modelbased control methods, e.g., sliding mode control (SMC) [5][6][7], robust control [8][9][10][11], internal model control [7,8], LQG control [12,13], predictive control [14], output regulation control (ORC) [1]. It should be noted that most of the above methods are based on linear or linearized models, which require nonlinear approximation near specific operating points of deflection micromirrors [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Online Optimization Method for Nonlinear Model-Predictive Control in Angular Tracking for MEMS Micromirror

Cao

Tan

2022

Micromachines

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In this brief, a precise angular tracking control strategy using nonlinear predictive optimization control (POC) approach is address. In order to deal with the model uncertainty and noise interference, a online Hammerstein-model-based POC is designed using online estimated parameters and model residual. Above all, a rate-dependent Duhem model is used to describe the nonlinear sub-model of the whole Hammerstein architecture for depicting multi-valued mapping nonlinear characteristic. Then, predictive output of angular deflection is obtained by Diophantine function based on linear submodel. Subsequently, the iterative control value depends on estimated parameters through data-driven is acquired. Later, based on the cost function, the iteratively optimization control quantity is fed back to the electromagnetic driven deflection micromirror (EDDM) system on the basis of Hammerstein architecture. It should be stressed that the control value is determined by real-time update model residual and defined cost function. Moreover, the stability of POC strategy is proposed. In addition, experimental result is proposed to validate the effectiveness of the control technique adopted in this paper.

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Section: Resultsmentioning

confidence: 99%