Abstract
This paper investigates the problem of event-triggered adaptive tracking control for space manipulator systems under pre-determined position constraints. This control scheme aims to overcome external perturbations, reduce the burden of data-transmission, and achieve constrained tracking. Focusing on the constraints of system performance, quadratic Lyapunov functions (QLF) are stitched with a set of asymmetric time-receding horizons (TRH) with fixed settling time, serving as a sufficient condition for the practically prescribed finite-time stability (PPFS) of target plants. By introducing event-triggered conditions, the control signals are transformed into non-periodically updated variables, promoting signaling efficiency while preserving the desired system performance. Complex nonlinearities are integrated and compensated adaptively, providing an ingenious design process and simplifying the construction of the controller. Finally, simulations demonstrate the effectiveness of the proposed scheme.
This work was supported in part by the Innovation Capability Support Program of Shaanxi (No. 2019TD-008); and in part by the China Scholarship Council (No. 202106290148).
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Hao, Z., Yue, X., Liu, L., Ge, S.S. (2022). Event-Triggered Adaptive Control for Practically Finite-Time Position-Constrained Tracking of Space Robot Manipulators. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13455. Springer, Cham. https://doi.org/10.1007/978-3-031-13844-7_45
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