A two-level Iterative Learning Control scheme for the engagement of wet clutches

Depraetere, Bruno; Pinte, Gregory; Symens, Wim; Swevers, Jan

doi:10.1016/j.mechatronics.2011.01.002

Cited by 27 publications

(13 citation statements)

References 18 publications

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“…A wet clutch system realizes this by means of contact friction between two series of friction plates, see Fig. 7 for a schematic cross section [6]. By construction these friction plates are fully submerged in oil so that slip induced heat can be transferred adequately.…”

Section: System Descriptionmentioning

confidence: 99%

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Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch

et al. 2020

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A numerically efficient inverse method for parametric model uncertainty identification using maximum likelihood estimation is presented. The goal is to identify a probability model for a fixed number of model parameters based on a set of experiments. To perform maximum likelihood estimation, the output probability density function is required. Forward propagation of input uncertainty is established combining Polynomial Chaos and moment matching. High-order moments of the output distribution are estimated using the generalized Polynomial Chaos framework. Next, a maximum entropy parametric distribution is matched with the estimated moments. This method is numerically very attractive due to reduced forward sampling and deterministic nature of the propagation strategy. The methodology is applied on a wet clutch system for which certain model variables are considered as stochastic.The number of required model simulations to achieve the same accuracy as the brute force methodologies is decreased by one order of magnitude. The probability model identified with the high order estimates resulted into a true log-likelihood increase of about 4% since the accuracy of the estimated output probability density function could be improved up to 47%.

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Section: System Descriptionmentioning

confidence: 99%

“…The load consists of a flywheel (2, 5 kg m 2 ) and a brake. This system was previously used to study smooth gearbox controllers [6].…”

Section: Practical Set-upmentioning

confidence: 99%

Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch

et al. 2020

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“…3. As explained in [11], its input shaft is connected to a hollow cylinder with internal grooves, called the drum. The first set of friction plates (clutch plates) with external toothing can slide in those grooves, whereas the second set of friction plates (clutch discs) with internal toothing can slide over a grooved bus connected to the output shaft.…”

Section: Wet-clutch Experiments and Modelmentioning

confidence: 99%

Parametric Uncertainty Quantification Using Polynomial Chaos Expansions Applied to a Wet Friction Clutch Model

Tod¹,

Groote²,

Lefebvre³

et al. 2019

IJMO

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Recently, designing mechatronic systems has become more and more dependent on models that are used to predict performance in a virtual environment, and the models involved are becoming increasingly more complex multiphysical systems. Instead of spending much time modeling increasingly detailed physical models, uncertainties can be explicitly considered to model the lack of knowledge. The mismatch between real-life experiments and model simulations due to parametric uncertainties can be quantified using likelihood estimation and Monte Carlo sampling techniques for propagation. In this paper, we attempt to significantly accelerate the process using polynomial chaos expansions for propagation and a genetic algorithm to maximize likelihood. The soundness of this approach is demonstrated on a wet friction clutch system. The results show that the method has a strong potential for scalability with respect to the number of uncertain parameters.

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“…Implementation on typical industrial controllers is thus possible, but when this is not the case it is more likely due to memory problems than due to a high computational load. A more detailed description of the implementation applied to the wet clutch can be found in [25].…”

Section: Two-level Ilc (2l-ilc)mentioning

confidence: 99%

Model-based and model-free learning strategies for wet clutch control

et al. 2014

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This paper presents an overview of model-based (Nonlinear Model Predictive Control, Iterative Learning Control and Iterative Optimization) and model-free (Genetic-based Machine Learning and Reinforcement Learning) learning strategies for the control of wet-clutches. The benefits and drawbacks of the different methodologies are discussed, and illustrated by an experimental validation on a test bench containing wet-clutches. In general, all strategies yield a good engagement quality once they converge. The model-based strategies seems most suited for an online application, because they are inherently more robust and require a shorter convergence time. The model-free strategies meanwhile seem most suited to offline calibration procedures for complex systems where heuristic tuning rules no longer suffice.

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A two-level Iterative Learning Control scheme for the engagement of wet clutches

Cited by 27 publications

References 18 publications

Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch

Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch

Parametric Uncertainty Quantification Using Polynomial Chaos Expansions Applied to a Wet Friction Clutch Model

Model-based and model-free learning strategies for wet clutch control

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