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@ -835,7 +835,7 @@ During conceptual design, it was found that the guaranteed stability property of
To address this instability issue, two modifications to the classical IFF control scheme were proposed and analyzed.
The first involves a minor adjustment to the control law itself, while the second incorporates physical springs in parallel with the force sensors.
Stability conditions and optimal parameter tuning guidelines were derived for both modified schemes.
This is further discussed in Section\nbsp{}ref:sec:rotating and was the subject of publications \nbsp{}[[cite:&dehaeze20_activ_dampin_rotat_platf_integ_force_feedb;&dehaeze21_activ_dampin_rotat_platf_using]].
This is further discussed in Section\nbsp{}ref:sec:rotating and was the subject of a publication\nbsp{}[[cite:&dehaeze21_activ_dampin_rotat_platf_using]].
***** Design of complementary filters using $\mathcal{H}_\infty$ Synthesis
@ -5095,7 +5095,7 @@ Through coordinate transformation using the Jacobian matrix, the dynamics in the
Although this simplified model provides useful insights, real Stewart platforms exhibit more complex behaviors.
Several factors can significantly increase the model complexity, such as:
- Strut dynamics, including mass distribution and internal resonances\nbsp{}[[cite:&afzali-far16_inert_matrix_hexap_strut_joint_space;&chen04_decoup_contr_flexur_joint_hexap]]
- Strut dynamics, including mass distribution and internal resonances\nbsp{}[[cite:&chen04_decoup_contr_flexur_joint_hexap]]
- Joint compliance and friction effects\nbsp{}[[cite:&mcinroy00_desig_contr_flexur_joint_hexap;&mcinroy02_model_desig_flexur_joint_stewar]]
- Supporting structure dynamics and payload dynamics, which are both very critical for NASS
@ -8014,7 +8014,7 @@ One way to overcome these limitations is to combine several sensors using a tech
Fortunately, a wide variety of sensors exists, each with different characteristics.
By carefully selecting the sensors to be fused, a "super sensor" is obtained that combines the benefits of the individual sensors.
In some applications, sensor fusion is employed to increase measurement bandwidth\nbsp{}[[cite:&shaw90_bandw_enhan_posit_measur_using_measur_accel;&zimmermann92_high_bandw_orien_measur_contr;&min15_compl_filter_desig_angle_estim]].
In some applications, sensor fusion is employed to increase measurement bandwidth\nbsp{}[[cite:&shaw90_bandw_enhan_posit_measur_using_measur_accel;&zimmermann92_high_bandw_orien_measur_contr]].
For instance, in\nbsp{}[[cite:&shaw90_bandw_enhan_posit_measur_using_measur_accel]], the bandwidth of a position sensor is extended by fusing it with an accelerometer that provides high-frequency motion information.
In other applications, sensor fusion is used to obtain an estimate of the measured quantity with reduced noise\nbsp{}[[cite:&hua05_low_ligo;&hua04_polyp_fir_compl_filter_contr_system;&plummer06_optim_compl_filter_their_applic_motion_measur;&robert12_introd_random_signal_applied_kalman]].
More recently, the fusion of sensors measuring different physical quantities has been proposed to enhance control properties\nbsp{}[[cite:&collette15_sensor_fusion_method_high_perfor;&yong16_high_speed_vertic_posit_stage]].
@ -8022,12 +8022,12 @@ In\nbsp{}[[cite:&collette15_sensor_fusion_method_high_perfor]], an inertial sens
Beyond Stewart platforms, practical applications of sensor fusion are numerous.
It is widely implemented for attitude estimation in autonomous vehicles such as unmanned aerial vehicles\nbsp{}[[cite:&baerveldt97_low_cost_low_weigh_attit;&corke04_inert_visual_sensin_system_small_auton_helic;&jensen13_basic_uas]] and underwater vehicles\nbsp{}[[cite:&pascoal99_navig_system_desig_using_time;&batista10_optim_posit_veloc_navig_filter_auton_vehic]].
Sensor fusion offers significant benefits for high-performance positioning control as demonstrated in\nbsp{}[[cite:&shaw90_bandw_enhan_posit_measur_using_measur_accel;&zimmermann92_high_bandw_orien_measur_contr;&min15_compl_filter_desig_angle_estim;&yong16_high_speed_vertic_posit_stage]].
Sensor fusion offers significant benefits for high-performance positioning control as demonstrated in\nbsp{}[[cite:&shaw90_bandw_enhan_posit_measur_using_measur_accel;&zimmermann92_high_bandw_orien_measur_contr;&yong16_high_speed_vertic_posit_stage]].
It has also been identified as a key technology for improving the performance of active vibration isolation systems\nbsp{}[[cite:&tjepkema12_sensor_fusion_activ_vibrat_isolat_precis_equip]].
Emblematic examples include the isolation stages of gravitational wave detectors\nbsp{}[[cite:&collette15_sensor_fusion_method_high_perfor;&heijningen18_low]] such as those employed at LIGO\nbsp{}[[cite:&hua05_low_ligo;&hua04_polyp_fir_compl_filter_contr_system]] and Virgo\nbsp{}[[cite:&lucia18_low_frequen_optim_perfor_advan]].
Two principal methods are employed to perform sensor fusion: using complementary filters\nbsp{}[[cite:&anderson53_instr_approac_system_steer_comput]] or using Kalman filtering\nbsp{}[[cite:&brown72_integ_navig_system_kalman_filter]].
For sensor fusion applications, these methods share many relationships\nbsp{}[[cite:&brown72_integ_navig_system_kalman_filter;&higgins75_compar_compl_kalman_filter;&robert12_introd_random_signal_applied_kalman;&fonseca15_compl]].
For sensor fusion applications, these methods share many relationships\nbsp{}[[cite:&brown72_integ_navig_system_kalman_filter;&higgins75_compar_compl_kalman_filter;&robert12_introd_random_signal_applied_kalman;&carreira15_compl_filter_desig_three_frequen_bands]].
However, Kalman filtering requires assumptions about the probabilistic characteristics of sensor noise\nbsp{}[[cite:&robert12_introd_random_signal_applied_kalman]], whereas complementary filters do not impose such requirements.
Furthermore, complementary filters offer advantages over Kalman filtering for sensor fusion through their general applicability, low computational cost\nbsp{}[[cite:&higgins75_compar_compl_kalman_filter]], and intuitive nature, as their effects can be readily interpreted in the frequency domain.
@ -8037,7 +8037,7 @@ While analog complementary filters remain in use today\nbsp{}[[cite:&yong16_high
Various design methods have been developed to optimize complementary filters.
The most straightforward approach is based on analytical formulas, which depending on the application may be first order\nbsp{}[[cite:&corke04_inert_visual_sensin_system_small_auton_helic;&yeh05_model_contr_hydraul_actuat_two;&yong16_high_speed_vertic_posit_stage]], second order\nbsp{}[[cite:&baerveldt97_low_cost_low_weigh_attit;&stoten01_fusion_kinet_data_using_compos_filter;&jensen13_basic_uas]], or higher orders\nbsp{}[[cite:&shaw90_bandw_enhan_posit_measur_using_measur_accel;&zimmermann92_high_bandw_orien_measur_contr;&stoten01_fusion_kinet_data_using_compos_filter;&collette15_sensor_fusion_method_high_perfor;&matichard15_seism_isolat_advan_ligo]].
Since the characteristics of the super sensor depend on proper complementary filter design\nbsp{}[[cite:&dehaeze19_compl_filter_shapin_using_synth]], several optimization techniques have emerged—ranging from optimizing parameters for analytical formulas\nbsp{}[[cite:&jensen13_basic_uas;&min15_compl_filter_desig_angle_estim;&fonseca15_compl]] to employing convex optimization tools\nbsp{}[[cite:&hua04_polyp_fir_compl_filter_contr_system;&hua05_low_ligo]] such as linear matrix inequalities\nbsp{}[[cite:&pascoal99_navig_system_desig_using_time]].
Since the characteristics of the super sensor depend on proper complementary filter design\nbsp{}[[cite:&dehaeze19_compl_filter_shapin_using_synth]], several optimization techniques have emerged—ranging from optimizing parameters for analytical formulas\nbsp{}[[cite:&jensen13_basic_uas;&carreira15_compl_filter_desig_three_frequen_bands]] to employing convex optimization tools\nbsp{}[[cite:&hua04_polyp_fir_compl_filter_contr_system;&hua05_low_ligo]] such as linear matrix inequalities\nbsp{}[[cite:&pascoal99_navig_system_desig_using_time]].
As demonstrated in\nbsp{}[[cite:&plummer06_optim_compl_filter_their_applic_motion_measur]], complementary filter design can be linked to the standard mixed-sensitivity control problem, allowing powerful classical control theory tools to be applied.
For example, in\nbsp{}[[cite:&jensen13_basic_uas]], two gains of a Proportional Integral (PI) controller are optimized to minimize super sensor noise.
@ -8350,14 +8350,14 @@ This straightforward example demonstrates that the proposed methodology for shap
**** Synthesis of a set of three complementary filters
<<ssec:detail_control_sensor_hinf_three_comp_filters>>
Certain applications necessitate the fusion of more than two sensors\nbsp{}[[cite:&stoten01_fusion_kinet_data_using_compos_filter;&fonseca15_compl]].
Certain applications necessitate the fusion of more than two sensors\nbsp{}[[cite:&stoten01_fusion_kinet_data_using_compos_filter;&carreira15_compl_filter_desig_three_frequen_bands]].
At LIGO, for example, a super sensor is formed by merging three distinct sensors: an LVDT, a seismometer, and a geophone\nbsp{}[[cite:&matichard15_seism_isolat_advan_ligo]].
For merging $n>2$ sensors with complementary filters, two architectural approaches are possible, as illustrated in Figure\nbsp{}ref:fig:detail_control_sensor_fusion_three.
Fusion can be implemented either "sequentially," using $n-1$ sets of two complementary filters (Figure\nbsp{}ref:fig:detail_control_sensor_fusion_three_sequential), or "in parallel," employing a single set of $n$ complementary filters (Figure\nbsp{}ref:fig:detail_control_sensor_fusion_three_parallel).
While conventional sensor fusion synthesis techniques can be applied to the sequential approach, parallel architecture implementation requires a novel synthesis method for multiple complementary filters.
Previous literature has offered only simple analytical formulas for this purpose\nbsp{}[[cite:&stoten01_fusion_kinet_data_using_compos_filter;&fonseca15_compl]].
Previous literature has offered only simple analytical formulas for this purpose\nbsp{}[[cite:&stoten01_fusion_kinet_data_using_compos_filter;&carreira15_compl_filter_desig_three_frequen_bands]].
This section presents a generalization of the proposed complementary filter synthesis method to address this gap.
#+name: fig:detail_control_sensor_fusion_three

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@ -1,4 +1,4 @@
% Created 2025-04-21 Mon 23:35
% Created 2025-04-22 Tue 16:24
% Intended LaTeX compiler: pdflatex
\documentclass[a4paper, 10pt, DIV=12, parskip=full, bibliography=totoc]{scrreprt}
@ -41,7 +41,7 @@
\addbibresource{ref.bib}
\addbibresource{phd-thesis.bib}
\author{Dehaeze Thomas}
\date{2025-04-21}
\date{2025-04-22}
\title{Nano Active Stabilization of samples for tomography experiments: A mechatronic design approach}
\subtitle{PhD Thesis}
\hypersetup{
@ -669,7 +669,7 @@ During conceptual design, it was found that the guaranteed stability property of
To address this instability issue, two modifications to the classical IFF control scheme were proposed and analyzed.
The first involves a minor adjustment to the control law itself, while the second incorporates physical springs in parallel with the force sensors.
Stability conditions and optimal parameter tuning guidelines were derived for both modified schemes.
This is further discussed in Section~\ref{sec:rotating} and was the subject of publications ~\cite{dehaeze20_activ_dampin_rotat_platf_integ_force_feedb,dehaeze21_activ_dampin_rotat_platf_using}.
This is further discussed in Section~\ref{sec:rotating} and was the subject of a publication~\cite{dehaeze21_activ_dampin_rotat_platf_using}.
\paragraph{Design of complementary filters using \(\mathcal{H}_\infty\) Synthesis}
For implementing sensor fusion, where signals from multiple sensors are combined, complementary filters are often employed.
@ -4666,7 +4666,7 @@ Through coordinate transformation using the Jacobian matrix, the dynamics in the
Although this simplified model provides useful insights, real Stewart platforms exhibit more complex behaviors.
Several factors can significantly increase the model complexity, such as:
\begin{itemize}
\item Strut dynamics, including mass distribution and internal resonances~\cite{afzali-far16_inert_matrix_hexap_strut_joint_space,chen04_decoup_contr_flexur_joint_hexap}
\item Strut dynamics, including mass distribution and internal resonances~\cite{chen04_decoup_contr_flexur_joint_hexap}
\item Joint compliance and friction effects~\cite{mcinroy00_desig_contr_flexur_joint_hexap,mcinroy02_model_desig_flexur_joint_stewar}
\item Supporting structure dynamics and payload dynamics, which are both very critical for NASS
\end{itemize}
@ -7372,7 +7372,7 @@ One way to overcome these limitations is to combine several sensors using a tech
Fortunately, a wide variety of sensors exists, each with different characteristics.
By carefully selecting the sensors to be fused, a ``super sensor'' is obtained that combines the benefits of the individual sensors.
In some applications, sensor fusion is employed to increase measurement bandwidth~\cite{shaw90_bandw_enhan_posit_measur_using_measur_accel,zimmermann92_high_bandw_orien_measur_contr,min15_compl_filter_desig_angle_estim}.
In some applications, sensor fusion is employed to increase measurement bandwidth~\cite{shaw90_bandw_enhan_posit_measur_using_measur_accel,zimmermann92_high_bandw_orien_measur_contr}.
For instance, in~\cite{shaw90_bandw_enhan_posit_measur_using_measur_accel}, the bandwidth of a position sensor is extended by fusing it with an accelerometer that provides high-frequency motion information.
In other applications, sensor fusion is used to obtain an estimate of the measured quantity with reduced noise~\cite{hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system,plummer06_optim_compl_filter_their_applic_motion_measur,robert12_introd_random_signal_applied_kalman}.
More recently, the fusion of sensors measuring different physical quantities has been proposed to enhance control properties~\cite{collette15_sensor_fusion_method_high_perfor,yong16_high_speed_vertic_posit_stage}.
@ -7380,12 +7380,12 @@ In~\cite{collette15_sensor_fusion_method_high_perfor}, an inertial sensor used f
Beyond Stewart platforms, practical applications of sensor fusion are numerous.
It is widely implemented for attitude estimation in autonomous vehicles such as unmanned aerial vehicles~\cite{baerveldt97_low_cost_low_weigh_attit,corke04_inert_visual_sensin_system_small_auton_helic,jensen13_basic_uas} and underwater vehicles~\cite{pascoal99_navig_system_desig_using_time,batista10_optim_posit_veloc_navig_filter_auton_vehic}.
Sensor fusion offers significant benefits for high-performance positioning control as demonstrated in~\cite{shaw90_bandw_enhan_posit_measur_using_measur_accel,zimmermann92_high_bandw_orien_measur_contr,min15_compl_filter_desig_angle_estim,yong16_high_speed_vertic_posit_stage}.
Sensor fusion offers significant benefits for high-performance positioning control as demonstrated in~\cite{shaw90_bandw_enhan_posit_measur_using_measur_accel,zimmermann92_high_bandw_orien_measur_contr,yong16_high_speed_vertic_posit_stage}.
It has also been identified as a key technology for improving the performance of active vibration isolation systems~\cite{tjepkema12_sensor_fusion_activ_vibrat_isolat_precis_equip}.
Emblematic examples include the isolation stages of gravitational wave detectors~\cite{collette15_sensor_fusion_method_high_perfor,heijningen18_low} such as those employed at LIGO~\cite{hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system} and Virgo~\cite{lucia18_low_frequen_optim_perfor_advan}.
Two principal methods are employed to perform sensor fusion: using complementary filters~\cite{anderson53_instr_approac_system_steer_comput} or using Kalman filtering~\cite{brown72_integ_navig_system_kalman_filter}.
For sensor fusion applications, these methods share many relationships~\cite{brown72_integ_navig_system_kalman_filter,higgins75_compar_compl_kalman_filter,robert12_introd_random_signal_applied_kalman,fonseca15_compl}.
For sensor fusion applications, these methods share many relationships~\cite{brown72_integ_navig_system_kalman_filter,higgins75_compar_compl_kalman_filter,robert12_introd_random_signal_applied_kalman,carreira15_compl_filter_desig_three_frequen_bands}.
However, Kalman filtering requires assumptions about the probabilistic characteristics of sensor noise~\cite{robert12_introd_random_signal_applied_kalman}, whereas complementary filters do not impose such requirements.
Furthermore, complementary filters offer advantages over Kalman filtering for sensor fusion through their general applicability, low computational cost~\cite{higgins75_compar_compl_kalman_filter}, and intuitive nature, as their effects can be readily interpreted in the frequency domain.
@ -7395,7 +7395,7 @@ While analog complementary filters remain in use today~\cite{yong16_high_speed_v
Various design methods have been developed to optimize complementary filters.
The most straightforward approach is based on analytical formulas, which depending on the application may be first order~\cite{corke04_inert_visual_sensin_system_small_auton_helic,yeh05_model_contr_hydraul_actuat_two,yong16_high_speed_vertic_posit_stage}, second order~\cite{baerveldt97_low_cost_low_weigh_attit,stoten01_fusion_kinet_data_using_compos_filter,jensen13_basic_uas}, or higher orders~\cite{shaw90_bandw_enhan_posit_measur_using_measur_accel,zimmermann92_high_bandw_orien_measur_contr,stoten01_fusion_kinet_data_using_compos_filter,collette15_sensor_fusion_method_high_perfor,matichard15_seism_isolat_advan_ligo}.
Since the characteristics of the super sensor depend on proper complementary filter design~\cite{dehaeze19_compl_filter_shapin_using_synth}, several optimization techniques have emerged—ranging from optimizing parameters for analytical formulas~\cite{jensen13_basic_uas,min15_compl_filter_desig_angle_estim,fonseca15_compl} to employing convex optimization tools~\cite{hua04_polyp_fir_compl_filter_contr_system,hua05_low_ligo} such as linear matrix inequalities~\cite{pascoal99_navig_system_desig_using_time}.
Since the characteristics of the super sensor depend on proper complementary filter design~\cite{dehaeze19_compl_filter_shapin_using_synth}, several optimization techniques have emerged—ranging from optimizing parameters for analytical formulas~\cite{jensen13_basic_uas,carreira15_compl_filter_desig_three_frequen_bands} to employing convex optimization tools~\cite{hua04_polyp_fir_compl_filter_contr_system,hua05_low_ligo} such as linear matrix inequalities~\cite{pascoal99_navig_system_desig_using_time}.
As demonstrated in~\cite{plummer06_optim_compl_filter_their_applic_motion_measur}, complementary filter design can be linked to the standard mixed-sensitivity control problem, allowing powerful classical control theory tools to be applied.
For example, in~\cite{jensen13_basic_uas}, two gains of a Proportional Integral (PI) controller are optimized to minimize super sensor noise.
@ -7693,14 +7693,14 @@ This straightforward example demonstrates that the proposed methodology for shap
\subsubsection{Synthesis of a set of three complementary filters}
\label{ssec:detail_control_sensor_hinf_three_comp_filters}
Certain applications necessitate the fusion of more than two sensors~\cite{stoten01_fusion_kinet_data_using_compos_filter,fonseca15_compl}.
Certain applications necessitate the fusion of more than two sensors~\cite{stoten01_fusion_kinet_data_using_compos_filter,carreira15_compl_filter_desig_three_frequen_bands}.
At LIGO, for example, a super sensor is formed by merging three distinct sensors: an LVDT, a seismometer, and a geophone~\cite{matichard15_seism_isolat_advan_ligo}.
For merging \(n>2\) sensors with complementary filters, two architectural approaches are possible, as illustrated in Figure~\ref{fig:detail_control_sensor_fusion_three}.
Fusion can be implemented either ``sequentially,'' using \(n-1\) sets of two complementary filters (Figure~\ref{fig:detail_control_sensor_fusion_three_sequential}), or ``in parallel,'' employing a single set of \(n\) complementary filters (Figure~\ref{fig:detail_control_sensor_fusion_three_parallel}).
While conventional sensor fusion synthesis techniques can be applied to the sequential approach, parallel architecture implementation requires a novel synthesis method for multiple complementary filters.
Previous literature has offered only simple analytical formulas for this purpose~\cite{stoten01_fusion_kinet_data_using_compos_filter,fonseca15_compl}.
Previous literature has offered only simple analytical formulas for this purpose~\cite{stoten01_fusion_kinet_data_using_compos_filter,carreira15_compl_filter_desig_three_frequen_bands}.
This section presents a generalization of the proposed complementary filter synthesis method to address this gap.
\begin{figure}[htbp]

143
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@ -1,34 +1,139 @@
@inproceedings{dehaeze21_mechat_approac_devel_nano_activ_stabil_system,
author = {Dehaeze, T. and Bonnefoy, J. and Collette, C.},
title = {Mechatronics Approach for the Development of a Nano-Active-Stabilization-System},
booktitle = {MEDSI'20},
year = {2021},
@inproceedings{dehaeze18_sampl_stabil_for_tomog_exper,
author = {Dehaeze, T. and Magnin Mattenet, M. and Collette, C.},
title = {Sample Stabilization For Tomography Experiments In Presence
Of Large Plant Uncertainty},
booktitle = {MEDSI'18},
year = 2018,
number = 10,
pages = {153--157},
doi = {10.18429/JACoW-MEDSI2018-WEOAMA02},
url = {https://doi.org/10.18429/JACoW-MEDSI2018-WEOAMA02},
address = {Geneva, Switzerland},
isbn = {978-3-95450-207-3},
language = {english},
month = 12,
publisher = {JACoW Publishing},
series = {Mechanical Engineering Design of Synchrotron Radiation Equipment and Instrumentation},
venue = {Chicago, USA},
series = {Mechanical Engineering Design of Synchrotron Radiation
Equipment and Instrumentation},
venue = {Paris, France},
keywords = {publication},
}
@inproceedings{brumund21_multib_simul_reduc_order_flexib_bodies_fea,
author = {Philipp Brumund and Thomas Dehaeze},
title = {Multibody Simulations with Reduced Order Flexible Bodies obtained by FEA},
booktitle = {MEDSI'20},
year = {2020},
@inproceedings{dehaeze19_compl_filter_shapin_using_synth,
author = {Dehaeze, T. and Verma, M. and Collette, C.},
title = {Complementary Filters Shaping Using $\mathcal{H}_\infty$
Synthesis},
booktitle = {7th International Conference on Control, Mechatronics and
Automation (ICCMA)},
year = 2019,
pages = {459--464},
doi = {10.1109/ICCMA46720.2019.8988642},
url = {https://doi.org/10.1109/ICCMA46720.2019.8988642},
language = {english},
publisher = {JACoW Publishing},
series = {Mechanical Engineering Design of Synchrotron Radiation Equipment and Instrumentation},
venue = {Chicago, USA},
keywords = {publication},
}
@inproceedings{dehaeze20_activ_dampin_rotat_platf_integ_force_feedb,
author = {Dehaeze, T. and Collette, C.},
title = {Active Damping of Rotating Platforms using Integral Force
Feedback},
booktitle = {Proceedings of the International Conference on Modal
Analysis Noise and Vibration Engineering (ISMA)},
year = 2020,
url =
{https://past.isma-isaac.be/downloads/isma2020/proceedings/Contribution_304_proceeding_3.pdf},
keywords = {publication},
}
@article{verma20_multi_degree_freed_isolat_system,
author = {Verma, M. and Lafarga, V. and Dehaeze, T. and Collette, C.},
title = {Multi-Degree of Freedom Isolation System With High
Frequency Roll-Off for Drone Camera Stabilization},
journal = {IEEE Access},
year = 2020,
doi = {10.1109/ACCESS.2020.3027066},
url = {https://doi.org/10.1109/ACCESS.2020.3027066},
keywords = {publication},
}
@article{verma20_virtual_sensor_fusion_high_precis_contr,
author = {Verma, M. and Dehaeze, T. and Zhao, G. and
Watchi, J. and Collette, C.},
title = {Virtual Sensor Fusion for High Precision Control},
journal = {Mechanical Systems and Signal Processing},
volume = 150,
pages = 107241,
year = 2020,
doi = {10.1016/j.ymssp.2020.107241},
url = {https://doi.org/10.1016/j.ymssp.2020.107241},
keywords = {publication},
}
@article{dehaeze21_activ_dampin_rotat_platf_using,
author = {Thomas Dehaeze and Christophe Collette},
title = {Active Damping of Rotating Platforms Using Integral Force Feedback},
author = {Dehaeze, T. and Collette, C.},
title = {Active Damping of Rotating Platforms Using Integral Force
Feedback},
journal = {Engineering Research Express},
year = {2021},
year = 2021,
doi = {10.1088/2631-8695/abe803},
url = {https://doi.org/10.1088/2631-8695/abe803},
month = {2},
month = 2,
keywords = {publication},
}
@inproceedings{brumund21_multib_simul_reduc_order_flexib_bodies_fea,
author = {Brumund, P. and Dehaeze, T.},
title = {{Multibody Simulations with Reduced Order Flexible Bodies
Obtained by FEA}},
booktitle = {Proc. MEDSI'20},
year = 2021,
number = 11,
pages = 286,
doi = {10.18429/JACoW-MEDSI2020-WEPB08},
url = {https://jacow.org/medsi2020/papers/WEPB08.pdf},
language = {english},
paper = {WEPB08},
publisher = {JACoW Publishing, Geneva, Switzerland},
venue = {Chicago, USA, Jul. 2021},
keywords = {publication},
}
@inproceedings{dehaeze21_mechat_approac_devel_nano_activ_stabil_system,
author = {T. Dehaeze and J. Bonnefoy and G. R. L. Collette},
title = {{Mechatronics Approach for the Development of a
Nano-Active-Stabilization-System}},
booktitle = {Proc. MEDSI'20},
year = 2021,
number = 11,
pages = 93,
doi = {10.18429/JACoW-MEDSI2020-TUIO02},
url = {https://jacow.org/medsi2020/papers/TUIO02.pdf},
language = {english},
paper = {TUIO02},
publisher = {JACoW Publishing, Geneva, Switzerland},
venue = {Chicago, USA, Jul. 2021},
keywords = {publication},
}
@article{tsang22_optim_sensor_fusion_method_activ,
author = {Tsang, T. T. L. and Li, T. G. F. and Dehaeze, T. and Collette, C.},
title = {Optimal Sensor Fusion Method for Active Vibration Isolation
Systems in Ground-Based Gravitational-Wave Detectors},
journal = {Classical and Quantum Gravity},
volume = 39,
number = 18,
pages = 185007,
year = 2022,
doi = {10.1088/1361-6382/ac8780},
url = {http://dx.doi.org/10.1088/1361-6382/ac8780},
keywords = {publication},
}
@inproceedings{dehaeze22_fastj_uhv,
author = {Dehaeze, T. and Ducott{\'e}, L.},
title = {The Fastjack - A robust, UHV compatible and high
performance linear actuator},
year = 2022,
organization = {EUSPEN},
keywords = {publication},
}