Experimental comparison of online parameter identification schemes for a nanopositioning stage with variable mass

Eielsen, Arnfinn A.; Polóni, Tomáš; Johansen, Tor Arne; Gravdahl, Jan Tommy

doi:10.1109/aim.2011.6027083

Cited by 6 publications

(9 citation statements)

References 14 publications

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“…(13), (14) and the propagation of observer dynamics in Eq. (29), (28) is required through the numerical simulation.…”

Section: Ode Solver Used By Ekf and Mhomentioning

confidence: 99%

“…The actuation signal and measured response was generated and recorded using a dSPACE DS1103 harware-in-the-loop board, at a sampling frequency of 10 kHz. More details about the actual hardware setup can be found in [14]. From the frequency response data, reported in [14], it was found that for the case with the payload attached, the natural frequency f 1 = √ x 3 /2π = 423[Hz] and damping ratio ζ 1 = x 4 /2 √ x 3 = 0.0146.…”

Section: B Instrumentation and Experimental Datamentioning

confidence: 99%

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Moving Horizon Observer for vibration dynamics with plant uncertainties in nanopositioning system estimation

Polóni

Eielsen

Rohal’-Ilkiv

et al. 2012

2012 American Control Conference (ACC)

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Abstract-This paper considers the estimation of states and parameters of a Single-Degree-of-Freedom (SDOF) vibration model in nanopositioning system based on a nonlinear Moving Horizon Observer (MHO). The MHO is experimentally tested and verified on measured data. The information about the displacement and speed together with the system parameters and unmodeled force disturbance is estimated through the Sequential Quadratic Programming (SQP) optimization procedure. The MHO provided superior performance in comparison with the benchmark method Extended Kalman Filter (EKF) in terms of faster convergence.

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“…(13), (14) and the propagation of observer dynamics in Eq. (29), (28) is required through the numerical simulation.…”

Section: Ode Solver Used By Ekf and Mhomentioning

confidence: 99%

Section: B Instrumentation and Experimental Datamentioning

confidence: 99%

Moving Horizon Observer for vibration dynamics with plant uncertainties in nanopositioning system estimation

Polóni

Eielsen

Rohal’-Ilkiv

et al. 2012

2012 American Control Conference (ACC)

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“…The discrepancy for b 0 is mainly due to the larger driving voltage amplitude used in the experiment, compared to the amplitude used to find the frequency response. The discrepancy for a 1 is likely due to the presence of colored noise due to feedback and the hysteresis disturbance, since the parameter sensitivity for the model (1) with respect to a 1 is very small, as was pointed out in [18]. The small fluctuations in the a 1 estimate is due to noise, and can be reduced by decreasing e.g.…”

Section: Resultsmentioning

confidence: 99%

“…As was demonstrated in [18], the parameter sensitivities of a mass-spring-damper system suggest an emphasis on a frequency domain around the expected dominant resonant frequency of the system; a bandpass filter. As the scheme requires differentiation of the measured deflection, the bandpass filter must have a relative degree equal to the highest order of differentiation needed, so that the filters will be proper.…”

Section: B Adaptive Lawmentioning

confidence: 99%

“…As designing an optimal input signal is usually not feasible for arbitrary tracking control tasks, a practically feasible solution is to find a pre-filter which emphasizes certain frequency domains in the signals used in the parameter identification scheme [6], [14], [18]. In addition to provide more optimal signals with regards to the information matrix, the pre-filter is also beneficial since it can attenuate disturbances and non-modeled effects; thus a more dominantly rich signal.…”

Section: B Adaptive Lawmentioning

confidence: 99%

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Adaptive control of a nanopositioning device

Eielsen

Gravdahl

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Abstract-High-bandwidth tracking control is desirable in many nanopositioning applications, including scanning probe microscopy. Typical nanopositioner designs have several sources of uncertainty which can degrade control performance, and even induce instability. Salient uncertainties are in the control gain and the resonant frequencies of the mechanical structure. The control gain varies due to hysteresis and creep which result in a control gain that is dependent on the offset, range, and form of the driving signal, as well as actuator temperature and age. The resonant frequencies change due to payload mass. In order to maintain performance in the presence of moderately changing dynamic response, a model reference adaptive control (MRAC) scheme is proposed and implemented. The details of implementing a working MRAC will be discussed. Most notably, a novel augmentation of the parameter identification scheme in the form of a special pre-filter, will be shown to be necessary to obtain parameter convergence, and thus also stability in the case of the MRAC scheme. Experimental results are presented to assess the performance. I. INTRODUCTIONNanopositioning is typically associated with scanning probe microscopy (SPM), and its many applications. Some application task typically require general reference trajectory tracking, i.e., for manipulation, fabrication, and lithography. In order to improve throughput in such settings, high bandwidth control is required [1]- [3]. As the dynamic response of typical positioning systems employed has a fair amount of uncertainty, both inherently and due to the specific application, the control laws used also need to have sufficient robustness. Although the dynamic response is uncertain, it is dominantly linear and can be well described by linear ordinary differential equations for specific operating points. These systems should therefore be amenable to adaptive control schemes, which in principle can provide higher and more consistent performance than standard robust static control schemes.The standard indirect model reference adaptive control (MRAC) framework [4] is used in this work in order to develop a complete adaptive control scheme for a nanopositioning device of common design. As will be demonstrated, there are some important considerations to be made with regards to how to choose the plant model, how to tune the control law, and how to obtain parameter convergence for the adaptive law. The resulting control scheme is believed to be a well performing MRAC. The experimental results should therefore be indicative of the performance that can be expected applying MRAC to this particular type of system.The main novelty presented is a special pre-filter needed in order to obtain parameter convergence.

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Proactive vehicle oscillation suppression

Jung

2018

Fortschritte Naturstofftechnik

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Experimental comparison of online parameter identification schemes for a nanopositioning stage with variable mass

Cited by 6 publications

References 14 publications

Moving Horizon Observer for vibration dynamics with plant uncertainties in nanopositioning system estimation

Moving Horizon Observer for vibration dynamics with plant uncertainties in nanopositioning system estimation

Adaptive control of a nanopositioning device

Proactive vehicle oscillation suppression

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