Composite anti-disturbance position and attitude control for spacecrafts with parametric uncertainty and flexible vibration

The rendezvous and proximity operations with respect to a tumbling non-cooperative target pose high requirement for the position and attitude control accuracy of servicing spacecraft.However,multiple disturbances including parametric uncertainties,flexible vibration,and unknown nonlinear dynamics degrade the control performance significantly.In order to enhance the system anti-disturbance ability,this paper proposes a composite anti-disturbance control law for the spacecraft position and attitude tracking.Firstly,the relative position and attitude dynamic models with multiple disturbances are established,where the refined descriptions of multiple disturbances are accomplished based on their characteristics.Then,by combining a dual Disturbance ObserverBased Control(DOBC)and a sliding mode control,a composite controller with hierarchical architecture is proposed,where the dual DOBC in the feedforward channel is used to reject the flexible vibration,environment disturbance,and complicated nonlinear dynamics,while the parametric uncertainties are attenuated by the sliding mode control in the feedback channel.Stability analysis is carried out for the closed-loop system by unifying the sliding mode dynamics and observer dynamics.Finally,the effectiveness of the proposed controller is verified via numerical simulation and hardware-in-the-loop test.

Applications of intelligent computing in vehicular networks

Purpose–This paper aims to introduce vehicular network platform,routing and broadcasting methods and vehicular positioning enhancement technology,which are three aspects of the applications of intelligent computing in vehicular networks.From this paper,the role of intelligent algorithm in thefield of transportation and the vehicular networks can be understood.Design/methodology/approach–In this paper,the authors introduce three different methods in three layers of vehicle networking,which are data cleaning based on machine learning,routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles.Findings–In Section 2,a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database.In Section 3,the authors canfind when traffic conditions varied from freeflow to congestion,the number of message copies increased dramatically and the reachability also improved.The error of vehicle positioning is reduced by 35.39%based on the CV-IMM-EKF in Section 4.Finally,it can be concluded that the intelligent computing in the vehicle network system is effective,and it will improve the development of the car networking system.Originality/value–This paper reviews the research of intelligent algorithms in three related areas of vehicle networking.In thefield of vehicle networking,these research results are conducive to promoting data processing and algorithm optimization,and it may lay the foundation for the new methods.