Indirect adaptive trajectory control of MEMS LCR

Izadian, Afshin

doi:10.1109/iecon.2011.6119976

Cited by 2 publications

(2 citation statements)

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“…‫ݖ‬ሺ݇ሻ ൌ ߠ * ߮ሺ݇ሻ, (7) where ‫ݖ‬ሺ݇ሻ is the output of the system, ߠ * is a vector of unknown parameters and is being identified by recursive least square with dynamic data weighting, ߮ሺ݇ሻ is the vector of earlier inputs and outputs. Consider the discrete-time transfer function given in (6), and of the form…”

Section: Part I: Discrete-time Parametric Modelmentioning

confidence: 99%

“…However based on the manufacturing techniques, MEMS can have some variation in their parameters and are susceptible to faults. In addition, like any physical system, these devices age with time rendering a substantial variation in their parameters [6]. Therefore accurate system parameter identification and proper tracking controls are crucial in devices employing arrays of MEMS sensors or actuators [7], especially in mission critical applications like medical implants, satellites, etc.…”

Section: Introductionmentioning

confidence: 99%

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Model predictive control of MEMS LCR

Singh

Izadian

Anwar

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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This paper illustrates the application of model predictive control for trajectory control of micro electro mechanical lateral comb resonators (MEMS LCRs). A recursive least square estimator with dynamic data weighting method is used to estimate the parameters of MEMS in real-time. Accordingly, the control law and the gains are derived to achieve high performance trajectory control. Simulation results demonstrate a close trajectory profile tracking and accurate parameter estimation performance.I.

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Section: Part I: Discrete-time Parametric Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%