control and sensing solutions for active magnetic bearings

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