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Leuven, Belgium

Widanage W.D.,Vrije Universiteit Brussel | Stoev J.,FMTC | Schoukens J.,Vrije Universiteit Brussel
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2012

This paper discusses the design, implementation and the advantages of three types of signals for nonlinear system analysis and identification. They belong to the class of multisine signals and are the random phase, positively skewed and crest factor optimised multisine signals. A straightforward routine to combine such a signal with the system's typical input signal is discussed. The advantages of using such signals is illustrated through the results obtained from identifying the dynamics of a mechanical wet-clutch system. © 2012 IFAC. Source

Wang Z.,Holst Center | Bouwens F.,Holst Center | Vullers R.,Holst Center | Petre F.,FMTC | Devos S.,FMTC
Proceedings of IEEE Sensors | Year: 2011

This paper addresses the development of an energy-autonomous wireless vibration sensor for condition-based monitoring of machinery. Such technology plays an increasingly important role in modern manufacturing industry. In this work, energy harvesting is realized by resorting to a custom designed thermoelectric generator. The developed wireless vibration sensor has a remotely tunable sampling rate, which caters to the different needs of various operating conditions. The two key features, energy autonomy and wireless measurement, are demonstrated successfully by the experimental results obtained on the thermoelectric generator and the wireless sensor. © 2011 IEEE. Source

Depraetere B.,Celestijnenlaan | Pinte G.,FMTC | Symens W.,FMTC | Swevers J.,Celestijnenlaan
Mechatronics | Year: 2011

This paper discusses the application of Iterative Learning Control (ILC) algorithms for the engagement of wet clutches. A two-level control scheme is presented, consisting of a high level ILC-type algorithm which iteratively updates parameterized reference trajectories which are tracked by the low level tracking control. At this low level, two standard ILC controllers are used to first track a pressure reference in the filling phase and afterwards a slip reference in the slip phase of the clutch engagement. The performance and robustness of the presented approach are validated on an experimental test setup. It is shown that both levels are crucial to achieve good engagement quality during normal machine operation. Through the use of this ILC control scheme, it is possible to avoid time-consuming and cumbersome experimental (re)calibrations, which are nowadays used to achieve and maintain good performance despite the complex and time-varying dynamics of wet clutches. © 2011 Elsevier Ltd. All rights reserved. Source

Widanage W.D.,Vrije Universiteit Brussel | Stoev J.,FMTC | Van Mulders A.,Vrije Universiteit Brussel | Schoukens J.,Vrije Universiteit Brussel | Pinte G.,FMTC
Control Engineering Practice | Year: 2011

The work presented illustrates how the choice of input perturbation signal and experimental design improves the derived model of a nonlinear system, in particular the dynamics of a wet-clutch system. The relationship between the applied input current signal and resulting output pressure in the filling phase of the clutch is established based on bandlimited periodic signals applied at different current operating points and signals approximating the desired filling current signal. A polynomial nonlinear state space model is estimated and validated over a range of measurements and yields better fits over a linear model, while the performance of either model depends on the perturbation signal used for model estimation. © 2011 Elsevier Ltd. Source

Pinte G.,FMTC | Depraetere B.,Celestijnenlaan | Symens W.,FMTC | Swevers J.,Celestijnenlaan | Sas P.,Celestijnenlaan
Proceedings of ISMA 2010 - International Conference on Noise and Vibration Engineering, including USD 2010 | Year: 2010

This paper discusses the development of an advanced Iterative Learning Control (ILC) scheme for the filling of wet clutches. In the presented scheme, the appropriate actuator signal for a new clutch engagement is learned automatically based on the quality of previous engagements, such that time-consuming and cumbersome calibrations can be avoided. First, an ILC controller, which uses the position of the piston as control input, is developed and tested on a non-rotating clutch under well controlled conditions. Afterwards, a similar strategy is tested on a rotating set-up, where a pressure sensor is used as the input of the ILC controller. On a higher level, both the position and the pressure controller are extended with a second learning algorithm, that adapts the reference position/pressure to account for environmental changes which can not be learned by the low-level ILC controller. It is shown that a strong reduction of the transmitted torque level as well as a significant shortening of the engagement time can be achieved with the developed strategy, compared to traditional time-invariant control strategies. Source

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