Nonlinear system-identification of the filling phase of a wet-clutch system

Widanage, Widanalage Dhammika; Stoev, Julian; Mulders, Anne Van; Schoukens, Joannes; Pinte, Gregory

doi:10.1016/j.conengprac.2011.09.002

Cited by 36 publications

(35 citation statements)

References 17 publications

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“…The clutch piston moves towards clutch packs compressing them until the clutch packs are just in contact and the process is defined as the filling phase of the wet clutch. 18 By applying Newton's second law to the forces on the piston and the continuity equation to the piston chamber, the state equations of the dynamic system in filling phase may be derived and presented in the following form 11 Hao et al 5…”

Section: Modeling Of a Wet Clutchmentioning

confidence: 99%

A new control strategy of the filling phase for wet dual clutch transmission

Huang

Zhang

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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The traditional filling control strategy mainly relies on a large number of calibrations and is an open loop control. In order to avoid cumbersome calibrations and improve the control precision, a systematic approach of the filling control for wet dual clutch transmission is established in this paper. First, a model of the electro-hydraulic actuator for the wet dual clutch transmissions is presented and validated by the experiments. Then, an analytic solution method is developed for the filling control. To obtain the analytic solution of the filling pressure trajectory in the filling phase, the nonlinear mathematic model for wet clutches is transformed into a model in linear space by the feedback linearization, and then the optimal filling pressure trajectory is obtained based on Pontryagin's Minimum Principle. Furthermore, to improve the control accuracy of a variable force solenoid, a closed loop control based on adaptive generalized predictive control algorithm is applied to the developed model of the electro-hydraulic actuator to track the optimal filling pressure trajectory. The simulation results indicate that the control strategy is effective for solving the filling control problem.

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Section: Modeling Of a Wet Clutchmentioning

confidence: 99%

A new control strategy of the filling phase for wet dual clutch transmission

Huang

Zhang

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…A third-order linear input-output model for the filling phase (from current to pressure) sampled at 1ms and a fourth-order polynomial nonlinear state-space (PNLSS) model containing terms in powers of states and input for the slip phase (from current to slip) sampled at 10ms have been identified [20]. An MPC controller with…”

Section: Two-level Nmpc (2l-nmpc)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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“…On the test bench, the available measurable variables are input rotational speed, output rotational speed, and transmission torque. Based on the input and output rotational speed, slip value can be calculated by (1). It can be assumed that when the piston has moved 90% of the complete distance to the friction plates, the output shaft starts to accelerate.…”

Section: Inputs and Outputsmentioning

confidence: 99%

“…Normally inside of a clutch system, there are hydraulic, electrical and mechanical components. It is very difficult to identify and model the wet clutch system since this is a typical nonlinear system [1]. Recently, some advanced learning control methods are used to control and optimize the engagement performance of the wet clutch system.…”

Section: Introductionmentioning

confidence: 99%

Position and Velocity Predictions of the Piston in a Wet Clutch System during Engagement by Using a Neural Network Modeling

Zhong

Wyns

Dutta

et al. 2012

IFIP Advances in Information and Communication Technology

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Abstract. In a wet clutch system, a piston is used to compress the friction disks to close the clutch. The position and the velocity of the piston are the key effectors for achieving a good engagement performance. In a real setup, it is impossible to measure these variables. In this paper, we use transmission torque and slip to approximate the piston velocity and position information. By using this information, a process neural network is trained. This neural predictor shows good forecasting results on the piston position and velocity. It is helpful in designing a pressure profile which can result in a smooth and fast engagement in the future. This neural predictor can also be used in other model-based control techniques.

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Nonlinear system-identification of the filling phase of a wet-clutch system

Abstract: The work presented illustrates how the choice of input perturbation signal and experimental design improves the derived model of a nonlinear system, in partic- G(k):Final frequency response estimate when averaged over realisations

Cited by 36 publications

References 17 publications

A new control strategy of the filling phase for wet dual clutch transmission

A new control strategy of the filling phase for wet dual clutch transmission

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

Position and Velocity Predictions of the Piston in a Wet Clutch System during Engagement by Using a Neural Network Modeling

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