Event-triggered adaptive guiding control for robotic systems with a velocity observer based on guiding vector field

In this paper, an event-triggered adaptive guiding control (ETAGC) scheme is proposed, where the velocity state of the controlled robotic systems is unmeasurable. To improve the robustness of tracking control of the robotic systems in dynamic environment, the guiding vector field is designed, where the desired trajectory is obtained by designing an auxiliary dynamic system that converges to the path with non-time-parametric representation. Moreover, velocity observer and neural network are employed for the velocity estimating and uncertainty compensating to enhance control performance. The event-triggered scheme is used to reduce the communication burden with Zeno behavior prevented. The stability of the entire control scheme is verified by mathematical analysis and simulation, where the simulation results show that the robotic system effectively tracks the desired path while reducing the communication burden with the proposed ETAGC scheme, where the performance of the velocity observer and event-triggered mechanism performs well.