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

Faraz Abed Azad, Saeed Ansari Rad, Mehrdad Arashpour

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)


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.

Original languageEnglish
Article number104211
Number of pages11
JournalAutomation in Construction
Publication statusPublished - May 2022


  • Construction management
  • Delta parallel robot
  • Dynamic model selection
  • Extended external model
  • Insufficient excitation
  • Pick and place
  • Reinforcement learning
  • Robust back-stepping control

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