It is a challenge for manipulator to quickly achieve detumbling of a space instable satellite (SIS) with low joint torque. In this paper, an adaptive variable admittance control method is proposed to address the above issue. Firstly, the on-ground experimental process of capturing and detumbling the SIS is introduced. Then, steady state error of the built Cartesian pose admittance controller is analyzed. An adaptive variable admittance controller is designed and the necessary and sufficient conditions of its stability and convergence are proved. After that, multi-objective optimization function for detumbling is established and non-dominated sorting genetic algorithm II (NSGA-II) based on general regression neural network (GRNN) is developed to optimize the parameters of proposed controller. Finally, numerical simulations and the ground detumbling experiment are conducted to validate the proposed method. Simulation results show that the proposed control method can effectively reduce detumbling torque and shorten detumbling time and the controller optimized by GRNN and NSGA-II significantly improves detumbling capacity. The comparison with other adaptive compliance control methods shows that the proposed method has better performance, with the 9.42% reduction in detumbling torque and the 13.25% reduction in detumbling time. Ground experiment data shows that the manipulator operates stably adopting the adaptive variable admittance control method and can effectively reduce the angular velocity of the spin SIS.

Aiming at the lack of reliable gradual fault detection and abnormal condition alarm and evaluation ability in the plasticizing process of single-base gun propellant, a fault detection and diagnosis method based on normalized mutual information weighted multiway principal component analysis (NMI-WMPCA) under limited batch samples modelling was proposed. In this method, the differences of coupling correlation among multi-dimensional process variables and the coupling characteristics of linear and nonlinear relationships in the process are considered. NMI-WMPCA utilizes the generalization ability of a multi-model to establish an accurate fault detection model in limited batch samples, and adopts fault diagnosis methods based on a multi-model SPE statistic contribution plot to identify the fault source. The experimental results demonstrate that the proposed method is effective, which can realize the rapid detection and diagnosis of multiple faults in the plasticizing process.

The base attitude of the space robot determines the power generation efficiency of the solar panel and the quality of communication with the ground station. After capturing space target, the base attitude of the formed base-manipulator-target system is uncertain. In space, attitude reorientation of the base-manipulator-target system is the non-holonomic constraint problem. There are few publications that exploit the non-holonomic behavior for attitude reorientation. In the study, a non-holonomic trajectory planning method for attitude reorientation inspired by the free-falling-cat self-righting reflex is proposed. First, the free-falling-cat self-righting reflex is analyzed. Then, the kinematic model of the base-manipulator-target system is established using the link generalized Jacobian matrix. Moreover, referring to the free-falling-cat self-righting reflex, the base-manipulator-target system is divided into the base part and the target part. The relationship between the rotation angular velocity of both and the configuration of the base-manipulator-target is deduced, and the attitude redirection trajectory is planned. The non-holonomic angular momentum conservation constraint and the holonomic linear momentum conservation constraint are derived. Finally, the numerical simulation is conducted. The simulation results show that the proposed method can be used for non-holonomic trajectory planning of the base-manipulator-target system for attitude reorientation in space.