Output feedback robust \(H_{\infty}\) control design of elliptical orbital spacecraft rendezvous system with input saturation
Fixed Point Methods and Optimization, Volume 3, Issue 1, April 2026, Pages 1–19
XIANG-YU GAO
School of Mathematics and Statistics, Guangxi Normal University, Guilin, 541004, China
School of Mathematics, Yunnan Normal University, Kunming , 650092, China
AN-LONG CHEN
School of Mathematics and Statistics, Guangxi Normal University, Guilin, 541004, China
ZAI-YUN PENG
School of Mathematics, Yunnan Normal University, Kunming , 650092, China
College of Mathematics and Statistics, Chongqing JiaoTong University, Chongqing , 400074, China
Abstract
This paper studies the observer-based output feedback robust \(H_{\infty}\) control design problem for elliptical orbital spacecraft rendezvous system subject to both input saturation and external disturbance. Due to the limitation of existing technology and the effect of external disturbance, it is difficult to accurately measure the relative velocity information between target spacecraft and chaser spacecraft. In order to solve this problem, a state observer of spacecraft rendezvous system is established by the nonlinear Riccati matrix inequality method. Based on the obtained state observer, a discrete dynamic gain scheduling approach is adopted to design an observer-based output feedback robust \(H_{\infty}\) control of spacecraft rendezvous system, which can be obtained by solving the parametric periodic Riccati-like equation. The obtained observer-based output feedback robust \(H_{\infty}\) control can not only improve the dynamic performance of elliptical orbital rendezvous system, but also minimize the effect of external disturbance on the system. Finally, a practical example is provided to show the effectiveness of the proposed control design approach.
Cite this Article as
Xiang-Yu Gao, An-Long Chen, and Zai-Yun Peng, Output feedback robust \(H_{\infty}\) control design of elliptical orbital spacecraft rendezvous system with input saturation, Fixed Point Methods and Optimization, 3(1), 1–19, 2026