Learning Control for Production Machines (Lecopro)


Advanced control methods, which significantly enhance the efficiency of production machines, are of vital importance for the Flemish machine construction industry to preserve their leading position within the world economy. New control methods are necessary to satisfy the growing customer demands regarding both flexibility and productivity. Traditional controllers in production machines 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 learning behaviour in machine controllers. This will allow machines to automatically learn the optimal control parameters and adapt to variations in both process parameters and environmental conditions.

The realization of such ‘intelligent machines’ is the long-term goal of the LeCoPro project. The project intends to realize this goal by creating a knowledge platform in Flanders on learning control strategies for production machines. As Flanders has already a lot of expertise on learning techniques, but on different applications, the basic elements for the creation of such knowledge platform are already available. The more concrete objective of the project is to provide the Flemish machine builders with practical methodologies for the design of learning controllers for their production machines. To achieve this objective, two related research tracks are acknowledged: (i) learning control methodologies for complex (sub)systems and (ii) learning control methodologies for decentralized systems.