Event-Triggered Intermittent Trajectory Tracking Control for Picking Robotic manipulator

Currently, robotic manipulators are widely used in the field of agricultural harvesting, becoming a key component of agricultural automation. However, the continuous control strategy of picking robotic manipulators requires high real-time information, which increases control costs. To address this, this paper proposes an event-triggered intermittent control strategy for trajectory tracking of picking robotic manipulators. Firstly, a dynamic model of a two-joint picking robotic manipulators considering external disturbances is established using Lagrangian mechanics. An RBF neural network is used to estimate the unknown dynamic functions and external disturbances of the robotic arm. Based on the backstepping method, an adaptive control design for continuous feedback trajectory tracking is developed. Upper and lower bound techniques are used to establish a quantifiable relationship between the control and rest zones, leading to the design of an event-triggered controller for the control zone. Using Lyapunov stability theory, the stability of the picking robotic manipulators system is proven, ensuring the expected tracking performance. The boundedness of closed-loop signals in the picking robotic manipulators system is used to prove that the event-triggered mechanism avoids Zeno behavior. Finally, by considering both periodic and non-periodic intermittent cases, the feasibility and effectiveness of the proposed control strategy for picking robotic manipulators are analyzed.