 |
Research |
Overview
The research interests of
Raymond
de Callafon include experimental modeling and model validation using
system identification techniques, the interaction between experimental
modelling and feedback control design and the design and implementation
of low complexity feedback control systems. Applications in which this
research can be applied vary from complex engineering applications such
as electromechanical systems (mechatronics) to bio-medical systems and
industrial processes in which data based modeling, signal processing, decision
and control play an important role.
Once a prototype
product is available or once an existing system is equipped with sensing
(and actuating) devices, the dynamic behavior can be modelled and validated
by collecting time domain data. These measurements can form the basis for
dynamic experimental modeling that can be used for prediction, validation,
monitoring and the development of a low complexity feedback control system.
The research and the theory involved with experimental modelling, prediction,
model validation, (structural health) monitoring and low complexity decisiona
nd control algorithms can be applied to many applications in which low
complexity modeling and feedback control are essential.
In short, the quote mentioned
above describes the
research interests
and the applications that Raymond
de Callafon is actively involved in. For more information, you can view
the research interests and applications items mentioned below, that have
links to specific papers and progress reports. Alternatively, you can browse
through the list of selected publications.
System Identification
-
Identification and dynamic modeling
relevant for control [2a, 3a,
1b,
4c,
5c,
17c]
-
Closed loop identification -
estimation of models on data obtained under feedback [1a,
17a,
1b,
8c,
28c]
-
Frequency domain identification
- curve fitting and software [3a,
6a,
7a]
-
Modeling of non-linear dynamics
and signal behavior [22c]
-
Teaching and software development
[3a, 5a,
12a,
5b,
25c,
26c]
Model (in)validation and
Fault Detection
-
Validation of models on the
basis of closed-loop performance measures and data [4a,
19c,
20c,
27c,
29c,
34c]
-
Structural damage estimation
and detection [16c, 24c]
Model reduction and low order
control design
-
Reduction of models and controllers
using closed-loop criteria [1b,
18c]
-
Direct low order optimal control
design - numerical optimization and iterative tuning [1b,
11c,
18c,
23c]
Identification and Control
of Electromechanical Systems
-
Modeling and control of a Compact
Disc servo mechanism [2b, 1c,
2c]
-
Modeling and control of a Wafer
Stage positioning mechanism [13a,
1b,
3b,
12c]
-
Piezo-electric dual-stage actuator
in a hard disk drive [9a, 10a,
11a,
15a,
16a,
21c,
31c]
Identification of sound/vibration
propagation and Active Noise and Vibration Control
-
Active noise/vibration control
[30c, 2d,
33c,
35c,
7d]
-
Active noise control in headset
[1d]
-
Active noise control appied
to a data projector [8d]
-
Estimation of structural dynamics
for health monitoring [16c, 24c]
High Track Density Magnetic
Recording
-
Modeling and control of piezo-electric
mico-actuator actuator [15a,
17a]
-
Systematic dual-stage feedback
control design [15a, 16a]
-
Estimation of disturbances in
a hard disk drive [32c] and
in a linear tape system [18a]
Uncertainty modeling and
model (in)validation
-
Product variability characterization
of a dual-stage actuator [4d]
-
Closed-loop model (in)validation
of aero-servo elastic systems [27c,
5d]
