FPGA Implementation of a Kalman-Based Motion Estimator for Levitated Nanoparticles

Liao, Jiawei; Jost, Michael; Schaffner, Michael; Magno, Michele; Korb, Matthias; Benini, Luca; Tebbenjohanns, Felix; Reimann, René; Jain, Vijay Kumar; Groß, Michael; Militaru, Andrei; Frimmer, Martin; Novotny, Lukas

doi:10.1109/tim.2018.2879146

Cited by 18 publications

(8 citation statements)

References 23 publications

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“…The Kalman filter, a filtering technique used in engineering applications [32][33][34][35], can be implemented in real-time to accurately estimate the state of the particle's position and velocity. This state information is then used to apply the modulating feedback signal [13,36]. Such schemes are very effective for estimating the particle motion for small laser modulation, but above a certain (low) threshold loses track of the particle.…”

Section: Introductionmentioning

confidence: 99%

Optimal control for feedback cooling in cavityless levitated optomechanics

et al. 2019

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We consider feedback cooling in a cavityless levitated optomechanics setup, and we investigate the possibility to improve the feedback implementation. We apply optimal control theory to derive the optimal feedback signal both for quadratic (parametric) and linear (electric) feedback. We numerically compare optimal feedback against the typical feedback implementation used for experiments. In order to do so, we implement a state estimation scheme that takes into account the modulation of the laser intensity. We show that such an implementation allows us to increase the feedback strength, leading to faster cooling rates and lower center-of-mass temperatures.

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Section: Introductionmentioning

confidence: 99%

Optimal control for feedback cooling in cavityless levitated optomechanics

et al. 2019

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“…The Simulink model of Kalman stochastic reconstructor using P L controller for the temperature electric furnace shown in Figure 7. The efficiency of the stochastic reconstructor appears clearly on the comparison of the estimated variance, cleaned [28]- [30]. Of modeling and measurement noises, with the strongly noisy signal Vm.…”

Section: Discussionmentioning

confidence: 99%

Study of designing regulator for temperature electrical resistance furnace using Kalman stochastic reconstructor

Kada

Kebir²,

Berka³

et al. 2021

IJEECS

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<span>Electric <span>resistance furnaces are the most popular and widely used industrial electro thermal equipment which continues to be the subject of many improvements. The aim of this paper is to control the temperature of electrical furnace for noisy thermocouple sensors. It can be assessed by observing some variables, which are very difficult to observe. Due to limitations, mainly the location of thermal sensors and their noises. In this case, the temperature measurement is trained with centered Gaussian white noise. The problem of accurate temperatures estimation for such sensors is solved using Kalman filter, which is an optimized estimator that provides a computationally efficient way to estimate system state. Thus, variables that are not directly measurable can be reconstructed from the algorithm. Kalman stochastic reconstructor (KSR). We cannot use with fixed parameters to control the temperature. For this reason, this paper comes up with a KSR approach based pole placement (PL) hybrid controller to realize an algorithm for the temperature control electrical furnace. Results based on Matlab simulation show that the improved algorithm has well produced an optimal estimate of the temperature. Evolving over time from noisy measurements. Hybrid algorithm KSR approach based PL give good performance compared to PL controllers.</span></span>

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“…to cool the mechanical oscillator [49,52,53], or harness the optomechanical entanglement generated in the blue-detuned regime for various state preparation tasks [53]. Moreover, a crucial ingredient of LQG control, namely optimal quantum state estimation (corresponding to the classical Kalman filter), has been recently demonstrated for both mechanically compliant resonators [83,84] and levitated nanoparticles [85,86]. According to LQG control, the solution minimizing Eq.…”

Section: Bayesian Feedbackmentioning

confidence: 99%

Unconditional mechanical squeezing via back-action evading measurements and non-optimal feedback control

Di Giovanni,

Brunelli,

Genoni

2020

Preprint

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Backaction-evading (BAE) measurements of a mechanical resonator, by continuously monitoring a single quadrature of motion, can achieve precision below the zero-point uncertainty. When this happens, the measurement leaves the resonator in a quantum squeezed state. The squeezed state so generated is however conditional on the measurement outcomes, while for most applications it is desirable to have a deterministic, i.e., unconditional, squeezed state with the desired properties. In this work we apply feedback control to achieve deterministic manipulation of mechanical squeezing in an optomechanical system subject to a continuous BAE measurement. We study in details two strategies, direct (Markovian) and state-based (Bayesian) feedback. We show that both are capable to achieve optimal performances, i.e., a vanishing noise added by the feedback loop. Moreover, even when the feedback is restricted to be a time-varying mechanical force (experimentally friendly scenario) and an imperfect BAE regime is considered, the ensuing non-optimal feedback may still obtain significant amount of squeezing. In particular, we show that Bayesian feedback control is nearly optimal for a wide range of sideband resolution. Our analysis is of direct relevance for ultra-sensitive measurements and quantum state engineering in state-of-the-art optomechanical devices.

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FPGA Implementation of a Kalman-Based Motion Estimator for Levitated Nanoparticles

Cited by 18 publications

References 23 publications

Optimal control for feedback cooling in cavityless levitated optomechanics

Optimal control for feedback cooling in cavityless levitated optomechanics

Study of designing regulator for temperature electrical resistance furnace using Kalman stochastic reconstructor

Unconditional mechanical squeezing via back-action evading measurements and non-optimal feedback control

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