control and sensing solutions for active magnetic bearings
TRANSCRIPT
Control and Sensing SolutionsFor Active Magnetic Bearings
Luis M. Castellanos Molina
Supervisor: Prof. Andrea Tonoli
Publications 2017-2018
List of attended courses
• Course 01QRVIU: Model predictive control: theory and practice. 4 CFU, 21/09/2017.
• Course 03LCLRO: Epistemologia della macchina. 4 CFU, 13/04/2018.
• Course 01QORRO: Writing Scientific Papers in English. 3 CFU, 21/02/2018.
• Course: 01LCPIU: Experimental modeling: costruzione di modelli da dati sperimentali. 6 CFU, 25/06/2018.
Ongoing work. AMB System Identification and Control
Ongoing work. Sensing solutions
Research context and motivation
An Active Magnetic Bearing (AMB) is a mechatronic system that supports a rotating shaft
using magnetic levitation. There is no contact between bearing and rotor and this permits
high speed operation with no lubrication, no mechanical wear, almost free of maintenance
and online tuning of the rotodynamic performance. Turbomolecular pumps, flywheel energy
storage systems, turbo blowers, heart pumps and turbo compressors are the most significant
industrial applications.
Goals of the activity:
• Develop accurate and cost-effective sensing solutions.
• Improve control performance by implementing robust
model based control techniques.
Future work
PhD program in
Mechanical Engineering
XXXII Cycle
Scheme of a single axis active magnetic bearing
• With OF-MPC, Offset-free tracking is
achieved based on disturbance estimation.
• Any plant-model mismatch is lumped into
the estimates.
• Inacurate models lead to more deteriorated
responses and poor performance.
Offset-Free Model Predictive Control (MPC)
Centrifugal compressor on AMBs
Published
• S. Circosta, A. Bonfitto, Renato Galluzzi, L. M. Castellanos Molina, N. Amati, A. Tonoli. Modeling and
validation of the radial force capability of bearingless hysteresis drives. Actuators 2018, 7, 69;
doi:10.3390/act7040069.
• A. Bonfitto, L. M. Castellanos Molina, A. Tonoli, N. Amati, Offset-free model predictive control for active
magnetic bearing systems, Actuators 7 (3). doi: 10.3390/act7030046.
• L. M. Castellanos Molina, A. Bonfitto, A. Tonoli, N. Amati, Identification of force displacement and
force-current factors in an active magnetic bearing system, in: 2018 IEEE International Conference on
Electro/Information Technology, 2018.
• Qingwen Cui, Andrea Tonoli, Nicola Amati, Angelo Bonfitto, Luis M. Castellanos, Damping Strategies
on a Horizontal Rotor Supported by Electrodynamic Bearings, in: ISMB16, 16th International
Symposium on Magnetic Bearings, Aug. 13―17, 2018.
Under revision
• L. M. Castellanos Molina, R. Galuzzi, A. Bonfitto, A. Tonoli, N. Amati. Position Sensorless Control on
an active magnetic levitation demonstrator. Transactions on Education.
• A. Bonfitto, Ran gabai, A. Tonoli, N. Amati, Resonant Inductive Displacement Sensor for Active
Magnetic Bearings. Sensors and Actuators A: Physical.
Identification of force-displacement and force-current factors in AMB systems
• Accurate linear models are
obtained by using grey-box
model estimation.
• The force factors uncertainty
has been confined
System response when a step current excitation
Resonant Inductive Displacement Sensor
• Small and low-cost layout.
• No need for external
oscillator.
• Inherent sensor/actuator
collocation.Sensor frequency response at different air gaps
Displacement Estimation Based on Current and Flux Density Measurement
• Low-cost sensing solution.
• Good properties of resolution
and sensitivity.
Control scheme
Step Response comparison
Numerical behavior of the measured flux density
Time-domain input-output data
Grey-box Modeling
• Analyze the control performance when low-cost sensing solutions are used.
• Identify more accurate nonlinear models.
• Implement nonlinear constrained control approaches to improve control
performance.
Finite element 3D modeling