Back-stepping control of delta parallel robots with smart dynamic model selection for construction applications

Applications of robotic manipulators in construction fields is notorious; however, changes in system dynamics in the presence of heavy external loads and disturbances in pick-and-place operations are inevitable. To elude this, a novel smart online dynamic model selection is introduced and accompanied by a back-stepping sliding mode controller which is implemented on a 3-Degrees-Of-Freedom (DOF) Delta Parallel Robot. In order to fit the dominant behavior of the disturbances, reduced-order extended models, based on external loads, are identified in an online manner; thereafter, an off-policy reinforcement learning approach is exploited for smart dynamic model selection. Consequently, a robust evolving controller emerges able to perform pick-and-place tasks under any configuration of external loads, resulting in better tracking properties in comparison to fitting a single external model. Data-driven methods have potential for further improving the external loads' dominant behavior identification using the derived models' kernels opening up new avenues as future works.