Introducing convex BiLSTM-based controller applied to 3-PSP spatial parallel robot manipulator

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The idea of modeling first and then finding a control approach might have made sense when systems were changing on a vast scale, but not in today’s world where we are in danger of being run over on a highway called the information highway. The feedback phenomenon, which involves a system’s future being influenced by its past, presents a rational conflict in this context. However, we can move beyond these challenges by leveraging the system’s inherent ability to randomly repeat and re-create parts, allowing new features to emerge and the best repetition to become the standard. To address the mentioned challenges, this paper proposes a new structure based on bilateral long short-term memory (BiLSTM) to provide supervising feedback on the nonlinear components comprising friction, Coriolis, and Centrifugal forces in addition to gravity for adapting uncertain variations in the robotic system dynamics. A key characteristic of the proposed BiLSTM-based controller is the incorporation of memory. A new formulation is developed for determining when to recover old memories and to what extent, ensuring the controller remains both adaptive and efficient. Furthermore, given that for system identification, speed is vital; the proposed approach has dealt with the speed category by introducing a convex combination of BiLSTM layers. The stability of the whole structure has also been analyzed, and the updated learning rules have been derived. The simulation results show improved tracking performance and learning time compared to alternative approaches.