Time filter

Source Type

Depraetere B.,Catholic University of Leuven | Pinte G.,Flanders Mechatronics Technology Center | Swevers J.,Catholic University of Leuven
Proceedings of the American Control Conference | Year: 2011

This paper presents a new iterative learning strategy to control wet clutches. These are complex hydraulic systems that are commonly used in automatic transmissions of heavy duty vehicles, and their control aims at performing fast and smooth engagements. Learning is used to overcome the need for complex models and to maintain performance despite large variations in the system behavior. Classical iterative learning control techniques can however not be employed directly since reference trajectories corresponding to the performance requirements are unavailable. Instead, the presented iterative learning strategy translates the performance requirements directly into an objective function and constraints, hence constituting a numerical optimization problem. After each engagement, this problem is solved in order to find the control signal for the next engagement, using a piecewise linear model for the clutch. Learning is included by using the measured response data to update the models and constraints used by the optimization problem. The presented strategy is successfully validated on an experimental test bench containing wet clutches. The learning process is shown to converge towards the desired engagement quality, and a demonstration is given of the robustness with respect to changes in the operating conditions. © 2011 AACC American Automatic Control Council. Source

Vo-Minh T.,Catholic University of Leuven | Tjahjowidodo T.,Flanders Mechatronics Technology Center | Ramon H.,Biosensors | Van Brussel H.,Catholic University of Leuven
IEEE/ASME Transactions on Mechatronics | Year: 2011

Two main challenges in using a pneumatic artificial muscle (PAM) actuator are the nonlinearity of pneumatic system and the nonlinearity of the PAM dynamics. The latter is complicated to characterize. In this paper, a Maxwell-slip model used as a lumped-parametric quasi-static model is proposed to capture the force/length hysteresis of a PAM. The intuitive selection of elements in this model interprets the unclear, but blended contributing causes of the hysteresis very well, which are assumed to originate from the dry friction of the double helix weaving of the PAM braided shell, the friction of the weaving and the bladder, the elasticity of the bladder and/or the deformation of the conical parts of a PAM close to the end caps. The obtained model is simple, but physically meaningful and easy to handle in terms of control. © 2006 IEEE. Source

Lourens E.,Catholic University of Leuven | Papadimitriou C.,Catholic University of Leuven | Papadimitriou C.,University of Thessaly | Gillijns S.,Flanders Mechatronics Technology Center | And 3 more authors.
Mechanical Systems and Signal Processing | Year: 2012

An algorithm is presented for jointly estimating the input and state of a structure from a limited number of acceleration measurements. The algorithm extends an existing joint input-state estimation filter, derived using linear minimum-variance unbiased estimation, to applications in structural dynamics. The filter has the structure of a Kalman filter, except that the true value of the input is replaced by an optimal estimate. No prior information on the dynamic evolution of the input forces is assumed and no regularization is required, permitting online application. The effectiveness and accuracy of the proposed algorithm are demonstrated using data from a numerical cantilever beam example as well as a laboratory experiment on an instrumented steel beam and an in situ experiment on a footbridge. © 2012 Elsevier Ltd. Source

Ompusunggu A.P.,Flanders Mechatronics Technology Center | Sas P.,Catholic University of Leuven | Van Brussel H.,Catholic University of Leuven
Mechatronics | Year: 2013

In this paper, a friction model appropriate for wet friction clutches based on the extension of the Generalized Maxwell Slip (GMS) friction model is integrated to a four-DOF lumped-mass-spring-damper system which represents a typical SAE#2 test setup. Degradation models expressing the evolutions of the friction model parameters are also proposed, where the structure of the degradation models is inspired from experimental results obtained in the earlier work. This way, the engagement dynamics of the clutch system during the useful lifetime can be simulated. It appears that the previously developed pre-and post-lockup features extracted from the simulated signals obtained in this study are qualitatively in agreement with the experimental results. Those features show their predictive behaviors that confirm their feasibility to be used for clutch monitoring and prognostics. Furthermore, the models and simulation procedure discussed in this paper can be employed for developing and evaluating prognostics algorithms for wet friction clutch applications. © 2013 Elsevier Ltd. All rights reserved. Source

Alujevic N.,Catholic University of Leuven | Zhao G.,Catholic University of Leuven | Depraetere B.,Flanders Mechatronics Technology Center | Sas P.,Catholic University of Leuven | And 2 more authors.
Journal of Sound and Vibration | Year: 2014

The optimum resonance frequency and the damping coefficient for passive Tuned Mass Dampers (TMD) have been determined using various optimality criteria in the past. In this study an active damping method using Inertial Actuators (IAs) is considered. Closed form expressions for the H2 optimal control parameters that minimise the kinetic energy of the primary structure are derived. It is shown that the resonance frequency of the IA should be as low as practically possible. However, for a given resonance frequency, an optimal pair exists of the passive damping coefficient of the IA and the feedback gain. Closed form expressions for these are provided in the paper. It is noted that the amount by which the described active approach outperforms an optimally tuned TMD having the same mass depends on the squared ratio of resonance frequencies of the IA and the primary structure. © 2014 Elsevier Ltd. Source

Discover hidden collaborations