کنترل یادگیری تکراری برای پر کردن کلاچ های مرطوب

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 cannot 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.

A wet clutch is a mechanical device that transmits torque from its input axis to its output axis by means of friction. Wet clutches are often used in power transmissions of off-road vehicles and agricultural machines to selectively engage gear elements. These vehicles are operating under varying environmental conditions, e.g. different temperatures in winter and summer. Moreover, these vehicles are also used during several years such that the clutches are subject to a significant amount of wear and their dynamics will change over time. As a consequence of these varying conditions and system dynamics, the control signals for wet clutches leading to an optimal engagement change drastically during the transmission's life cycle and the control action should therefore be adapted accordingly. In [1], the robust control of wet clutches is identified as a challenging industrial control problem. Much research has been carried out in this field in the past two decades and numerous patents have been generated [2], [3], [4] and [5]. In this paper, iterative learning control (ILC [6]) is presented as an alternative, efficient strategy for the control of wet clutches, which can find the appropriate control action despite the varying system dynamics and environmental conditions. Fig. 1(a) shows the design of a wet clutch. The input axis of the clutch is connected to a drum, which is a hollow cylinder with grooves on the inside. A first set of friction plates (clutch plates) with external toothing can slide in those grooves, while a second set of friction plates (clutch discs) with internal toothing can slide over a grooved bus connected to the output axis. An electro-hydraulic pressure-regulated proportional valve regulates the pressure inside the clutch with the objective to control the displacement of a piston which can press the two sets of friction plates together such that torque is transmitted. Initially, when the clutch is not engaged, the piston should be positioned as far as possible from the friction plates to avoid energy losses due to viscous friction of the oil between the plates. If the clutch is not hydraulically actuated, a return spring keeps the piston in this position. When the vehicle control unit gives the command to engage the clutch, the clutch is filled with oil in preparation of the effective engagement. The objective is to decrease the distance between the piston and the plates as fast as possible to zero, without the piston and the plates making brutal contact. This first phase in the engagement of a wet clutch is referred to as the filling phase. Afterwards, when the oil pressure is further increased, the friction plates are pressed together and torque is transmitted through the clutch. However, there is still a rotational speed-difference between the input and output shaft, resulting in energy dissipation in the clutch. A slip controller in this phase can be used to improve the quality of shifting [8]. Finally, when the oil pressure inside the clutch exceeds a certain level, the clutch will be fully engaged. The complete torque is transmitted through the clutch and the clutch is not slipping.In this paper, the control of the filling phase of wet clutches is studied. Nowadays, wet clutches in industrial transmissions are filled using a feedforward controller of the current to the electro-hydraulic valve, which regulates the oil pressure and hence the piston position in the clutch. Fig. 1(b) shows a typical parameterized, feedforward current signal, which is sent in the filling phase to the valve [2]. Although nowadays more advanced feedforward signals with more tunable parameters are sometimes applied [4], the above-mentioned parametrization perfectly illustrates the underlying idea behind the actual industrial control design. First, a current pulse is sent to the valve in order to generate a high pressure level in the clutch. This way, the piston will overcome the preloaded return spring and start to accelerate towards the friction plates. After this pulse, a lower constant current is sent out in order to decelerate the piston and position the piston near the friction plates. Finally a growing ramp current signal is sent to the valve such that the pressure in the clutch gradually increases and the clutch smoothly engages. The duration of the current pulse and the constant current level afterwards are critical to achieve a good filling and a smooth start of the engagement process [1]. On the one hand, a very long current pulse leads to an overfilling of the clutch such that the piston suddenly makes brutal contact with the friction plates resulting in undesired high peaks in the transmitted torque. On the other hand, a very short current pulse or a very low constant current level after the pulse leads to an underfilling of the clutch, resulting in a very slow engagement. To avoid over- and underfilling, in many industrial vehicles long calibration procedures are applied to find the optimal parameters of the feedforward current signal (i.e. the optimal combination of the pulse duration and constant current level) for a smooth clutch engagement. Furthermore, since the controlled system is time-varying, as described above, regular recalibrations of these parameters are inevitable. To avoid these cumbersome calibrations, some patents [5] and [2] describe adaptive algorithms to update the current pulse parameters at each filling of the clutch, based on the velocity and acceleration of the input and output shaft in the previous clutch engagement. This paper presents a two-level control system for the filling of wet clutches (Fig. 2), which does not update the discrete parameters of a predefined, feedforward current signal but determines a continuous current signal to the valve for a new engagement based on measured sensor information at the current and previous engagements. The developed system only focuses on the control of the filling phase, afterwards the slip phase is controlled by a feedforward action. On the low level of the developed control system for the filling phase, a control algorithm is implemented to track a reference for a measured physical variable. In a first part of the paper, the piston position in the clutch is measured and regulated. This way, it is possible to make the piston follow a reference trajectory, where the piston initially moves quickly towards the friction plates, then stops near the friction plates and finally comes smoothly into contact with the friction plates. Based on this position-based algorithm, the oil pressure profile leading to a smooth engagement can be measured. This pressure profile is applied in the second part of the paper to determine the shape of the reference for a pressure-based learning algorithm, which regulates the filling phase based on a measurement of the oil pressure in the line between the valve and the clutch. Since the clutch filling is realized at successive clutch engagements, an ILC algorithm can be used at this low level to learn the current signal to the valve for the next engagement by using experience from the previous engagement. This way it is possible to obtain a good tracking accuracy of the piston position or the oil pressure despite a large model uncertainty of the controlled system between the current to the valve and the regulated variable due to nonlinearities, temperature changes, etc.On the high level of the developed control system, a parameterized reference signal for the regulated variable in the low-level controller is determined based on an adaptive algorithm. This high-level algorithm accounts for slowly changing system variations such as wear, which cannot be learned by the low-level controller. At regular intervals, after a fixed number of closings of the clutch, an assessment of the engagement quality at the previous closings is made by measuring one or more quality indices (e.g. the engagement time). Based on these measured quality indices, the reference trajectory for the low-level algorithm is adapted according to some adaptive rules. These adaptive rules have been derived for the high-level control of the position as well as pressure. The derived rules look similar to the above-mentioned rules described in some patents [5] and [2] to update the current pulse parameters. However, the rules to update the reference trajectory for the position or pressure are simpler than the existing rules to update the current parameters because they should only compensate for the slow systems variations. Furthermore, the derived rules are also more straightforward since the physical relation between the quality indices and the piston position/pressure is simpler than between these indices and the current to the valve, where the valve dynamics play an important role. The different controllers are tested on a dedicated test bench, where an electromotor (30 kW) drives a flywheel (1 kg m2) via a torque convertor and two mechanical transmissions (Fig. 3). The controllers developed in this project are tested on one of the five clutches of the first transmission, which is equipped with different sensors, measuring the speed of the ingoing and outgoing shaft, the position of the piston and the pressure of the oil to the clutch. The second transmission is used to vary the actual load observed by the first transmission and to apply an adjustable braking torque.

This paper demonstrates the effectiveness of ILC algorithms to automatically find the appropriate control action for the filling phase of wet clutches. This way, an ILC-controller can be a good alternative for the time-consuming (re)calibration procedures which are performed nowadays in off-road vehicles to determine the optimal control action. A position-based as well as a more industrially relevant pressure-based control system have been presented. Both control systems consist of a combination of a feedback control algorithm and an ILC algorithm. The ILC significantly enhances the performance of the feedback system, by using the knowledge of the quality of previous engagements to calculate the control signal for the next engagement. Finally, also an adaptive ILC algorithm is presented, which automatically defines the most suitable reference signal for the ILC algorithm, leading to a significant improvement of the engagement quality. Future work consists of a study of the other phases in the engagement of a clutch after the filling phase. Up till now, these phases are controlled by the generation of a feedforward control action. The objective is to explore the possibilities of model-based (e.g. ILC) as well as non-model-based (e.g. Reinforcement Learning) learning techniques for control after the filling phase.