Results
On this page you can find an overview of the development cases studied in LeCoPro and a selection of the project results achieved on these cases.
Development cases:
 Transmission |
 Badminton demonstrator |
 Tractor with implement |
Transmission
In this development case, learning control is applied to the engagement of wet clutches. The objective is to learn the optimal current profile to the electro-hydraulic valve, which controls the pressure of the oil to the clutch. A wet clutch is a system with one input, which is characterised by a hard non-linearity when the piston of the clutch gets in contact with the friction plates. These clutches are typically used in power transmissions of off-road vehicles, which operate under strongly varying environmental conditions. These features make the control of wet clutches a suitable case to validate the techniques of WP 1. The validation experiments are carried out on a dedicated test bench (Fig. 1), where an electromotor (30kW) drives a flywheel (25 kgm2) via a torque convertor and two mechanical transmissions. The developed control scheme is tested on the first range clutch of the left transmission while the right transmission is used to vary the actual load observed by the first transmission. This set-up will also be used to test the global learning control techniques developed in WP 2. The techniques of WP 2 will be applied to control the current to the valves of the opening and closing clutch during a gear shifting.
Fig. 1: Transmission test set-up
Selected results:
- A two-level optimization-based learning controller for wet clutches
B. Depraetere, G. Pinte, J. Swevers
29th Benelux Meeting on Systems and Control
Heeze, The Netherlands, March 30 - April 1, 2010 - Iterative optimization of the filling phase of wet clutches
B. Depraetere, G. Pinte, J. Swevers
The 11th International Workshop on Advanced Motion Control (AMC 2010)
Nagaoka, Japan, March 21 - 24, 2010
Badminton demonstrator
To demonstrate its mechatronic competences, FMTC has constructed a badminton robot (Fig. 2). A visual system with two high-definition black and white cameras is used to localize the fast moving shuttle. The robot itself consists of one linear motor and two rotational motors. In the LeCoPro project, the application of learning techniques to the motion control of these different motors will be investigated. The main challenge is the development of fast learning algorithms, which can take into account the uncertainty of the estimated shuttle trajectory.
A movie of the badminton robot in action can be found here.
Fig. 2: Badminton demonstrator
Tractor with implement
This development case consists of a small tractor (Fig. 3), that can be controlled automatically (steering, speed), and a carried implement, that has rear wheels to steer relative to the tractor. In a first phase, learning control will be applied at the subsystem level to control the tractor. In a second phase the entire system with two subsystems (tractor + implement) will be considered. The main challenges in this application for the developed learning controllers will lie in the effect of varying soil conditions and the interaction between the tractor and the implement.
Fig. 3: Tractor with implement