Yuying Liang

Bio: Yuying Liang is an academic researcher from Beihang University. The author has contributed to research in topics: Position (vector) & Moon landing. The author has an hindex of 2, co-authored 2 publications receiving 9 citations.

Terminal guidance strategy for a hybrid thrust-tether lunar landing scheme

Abstract: A hybrid thrust-tether lunar landing scheme and its terminal guidance strategy are proposed in this paper, which has potential application in avoiding the dusts aroused by the plume of thrusters. The combined lander is made up of a descent stage and a rover, which are connected by a tethered device. An innovative combination of fuzzy and variable-structure controllers is introduced to guide the terminal landing, which is more robust than some classical guidance laws derived from the linearized dynamics. At the beginning of this phase, the combined lander carries out the targeting guidance law from the height of 250 m to the desired landing site. When the combined lander arrives at the height of about 20 m, the tethered device is triggered to release the rover which is controlled by the tensioning force provided by the motor and windlass. In releasing the rover, the descent stage is required to hover above the lunar surface at a certain height until the rover meets safe landing conditions. After the rover cuts off the tether, the descent stage will be driven by the deputy thrusters as far away from the rover as possible. A typical scenario is implemented numerically to demonstrate the stabilization of the horizontal initial velocity even in nonzero azimuth angle case. To investigate the robustness of the closed-loop guidance law, a Monte-Carlo simulation is performed to create all the scenarios parameterized by the errors in initial position and velocity which is the result of last powered descent phase.

Global optimization of fuel consumption in J 2 rendezvous using interval analysis

Abstract: This paper addresses an open-time Lambert problem under first-order gravitational perturbations with unfixed parking time and transfer time. The perturbations are compensated by introducing its analytical solutions derived from Lagrange's planetary equations into Lambert problem. A drift vector of aim position correction is defined to reduce the aim position bias caused by the perturbations. The first purpose of optimization is to find sufficiently small intervals involving the global optimal parking time, transfer time, drift vector and velocity increment. The second is to determine the global solution or the solution close to it in these intervals. Interval analysis and a double-deck gradient-based method with GA estimating the initial range of drift vector are utilized to obtain the sufficiently small intervals including the global minimum velocity increment and the global minimum solution or one sufficiently close to it in these intervals.

Towards the use of fuzzy logic systems in rotary wing unmanned aerial vehicle: a review

Abstract: In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned Aerial Vehicle (UAV) in both civil and military sectors. Among various UAVs, the ability of rotary wing UAVs (RUAVs) in vertical take-off and landing, to hover and perform quick maneuvering attract researchers to develop models fully autonomous control framework. The majority of first principle techniques in modeling and controlling RUAV face challenges in incorporating and handling various uncertainties. Recently various fuzzy and neuro-fuzzy based intelligent systems are utilized to enhance the RUAV’s modeling and control performance. However, the majority of these fuzzy systems are based on batch learning methods, have static structure, and cannot adapt to rapidly changing environments. The implication of Evolving Intelligent System based model-free data-driven techniques can be a smart option since they can adapt their structure and parameters to cope with sudden changes in the behavior of RUAVs real-time flight. They work in a single pass learning fashion which is suitable for online real-time deployment. In this paper, state of the art of various fuzzy systems from the basic fuzzy system to evolving fuzzy system, their application in a RUAV namely quadcopter with existing limitations, and possible opportunities are analyzed. Besides, a variety of first principle techniques to control the quadcopter, their impediments, and conceivable solution with recently employed evolving fuzzy controllers are reviewed.

Adaptive Fuzzy Sliding Mode Control for the Probe Soft Landing on the Asteroids with Weak Gravitational Field

Abstract: For the trajectory control of the probe soft landing on the asteroids with weak gravitational field, this paper presents a combined integral sliding mode control with an adaptive fuzzy logic system, named adaptive fuzzy sliding mode control (AFSMC) scheme. Considering the uncertainty of the orbit dynamics model in the small body fixed coordinate system, and the polyhedron modeling uncertainty in the gravitational potential, a fuzzy logic system is adopted to approximate the upper bound of the uncertainties. In addition, a robust control item is introduced to compensate for the approximation error of fuzzy logic system. The designed adaptive law and robust item make the closed-loop control stable and the tracking errors are convergent to zero. The controller not only guarantees the rapidity and accuracy of the desired trajectory tracking, but also enhances the robustness of the control system, improving the dynamic tracking performance for the probe soft landing on asteroids. Finally, the contrastive simulation results are presented to show the feasibility and effectiveness of the proposed control scheme.

Problem A of 9th China trajectory optimization competition: Results found at NUAA

Abstract: This paper is intended to introduce a method and results of the NUAA team for problem A of the 9th China Trajectory Optimization Competition (CTOC-9). The cluster analysis, leader-follower system and repeat-groundtrack orbit are employed to obtain the solution with excellent performance index. The optimized process is performed in two parts. The first part is to search the monitoring sequence of GEO satellite beams. These beams have been grouped based on their monitoring features before searching, which is the critical factor that makes us in a dominant position. The simplified gains calculation model, which is built based on the leader-follower cluster system, is used to estimate monitoring gains. The second part is the configuration optimization of the monitoring cluster, which is transformed into the trajectory design of the followers because the leader's trajectory has been fixed. Based on the proposed formation configuration strategy, the trajectories of followers are solved by the genetic algorithm. The final result shows that it takes only 20.428 days to complete the mission, which ranks the first place in this competition.

Nonlinear Dynamics of a Space Tethered System in the Elliptic Earth-Moon Restricted Three-Body System

Abstract: This paper focuses on nonlinear oscillation of a space tether system connected to the Moon’s surface, elongating along the Earth-Moon line at L1 and L2 sides respectively. The full nonlinea...

Multi-objective Optimization for Time-Open Lambert Rendezvous Between Non-coplanar Orbits

Abstract: The problem of time-open Lambert multi-objective optimal rendezvous between non-coplanar orbits is studied in the paper. Based on the Lambert’s theorem, the descriptions of time-open Lambert rendezvous subjected to two-impulse and three-impulse are presented, and a universally applicable method for the solution of the transfer orbits is given. To deal with the multi-objective optimization subject to fuel consumption and flight time for rendezvous, an improved NSGA-II algorithm with a better performance is provided. Via the global optimization, Pareto set can be obtained by the improved NSGA-II for the multi-objective optimal rendezvous considering multi-constraints. Numerical simulations are conducted to validate the feasibility of the improved multi-objective optimization algorithm, and the results of the two-impulse and three-impulse multi-objective optimal rendezvous are shown.