Optimal motion prediction using a primitive-based model-free iterative control approach for crane systems

This paper suggests an optimal behavior prediction mechanism for a control system, using previously learned solutions to simple tasks called primitives. The optimality of the behavior is formulated as a reference trajectory tracking problem. The primitives are stored in a library of pairs of reference input/controlled output signals. The reference input primitives are optimized in a Model-Free Iterative Learning Control framework without using knowledge of the controlled process. The new complex trajectories to be tracked are decomposed into the output primitives regarded as basis functions. The optimal reference input fed to the control system in order to track the desired new trajectory is then recomposed from the reference input primitives. The efficiency of this approach is demonstrated on a case study concerning the position control of a two-axis positioning mechanism in a laboratory crane system application.