LeCoPro - Learning Control for Production Machines

Advanced control methods are of vital importance for the machine building industry to enhance the efficiency and flexibility of production machines as traditional controllers have important limitations. Firstly, in many cases it is intricate or even impossible for the designers and operators to optimally tune the parameters of a traditional production machine controller due to the complex nature and the vaguely known dynamics of these machines. Furthermore, traditional control algorithms are not able to track changing system parameters and varying environmental conditions, which often appear in practical situations, and will consequently not adapt the control parameters accordingly. These drawbacks of traditional control algorithms, which result in suboptimal efficiency of the controlled machines, can be solved by the introduction of a learning behaviour in machine controllers. Learning controllers will allow machines to automatically learn the optimal control parameters and adapt to variations in both process parameters and environmental conditions. The LeCoPro project, which is funded by the agency for Innovation by Science and Technology in Flanders, intends to create a knowledge platform in Flanders on learning control strategies for production machines. The consortium includes one research centre (Flanders’ Mechatronics Technology Center) and six research groups from three universities (V.U.B.: COMO research group, Department ELEC; K.U.Leuven: Division PMA, Division MeBioS, Division SCD; U.Gent: SYSTeMS research group). To achieve the project’s objective, two related research tracks are acknowledged: (i) learning control methodologies for complex (sub)systems and (ii) learning control methodologies for decentralized systems. The poster highlights the challenges of the LeCoPro project and discusses the potential of learning control techniques on some development cases: a transmission, a badminton robot and a tractor with an implement.