Dynamic Event-triggered Model-free Adaptive Control for Robotic Manipulator with Input Saturation

This paper studies a dynamic event-triggered model-free adaptive control approach for a robotic manipulator with input saturation. Initially, a dynamic linearization model for robotic arms with input saturation is established. An enhanced model-free adaptive control approach is developed, utilizing only the input and output data of controlled systems, without requiring accurate system dynamics, and introducing a proportional derivative feedback strategy. Moreover, a dynamic event-triggered mechanism is designed to minimize unnecessary communication requirements. Meanwhile, an input saturation issue is considered. Finally, the convergence of the developed method is roughly provided, and several simulation studies verify the method’s effectiveness.