In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter pertur...In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter perturbation caused by the uncertainty derived from grasping mass variation cannot be ignored.The existence of vibration and parameter perturbation makes the rotation control of flexible manipulators difficult,which seriously affects the operation accuracy of manipulators.What’s more,the complex dynamic coupling brings great challenges to the dynamics modeling and vibration analysis.To solve this problem,this paper takes the space flexible manipulator with an underactuated hand(SFMUH)as the research object.The dynamics model considering flexibility,multiple nonlinear elements and disturbance torque is established by the assumed modal method(AMM)and Hamilton’s principle.A dynamic modeling simplification method is proposed by analyzing the nonlinear terms.What’s more,a sliding mode control(SMC)method combined with the radial basis function(RBF)neural network compensation is proposed.Besides,the control law is designed using a saturation function in the control method to weaken the chatter phenomenon.With the help of neural networks to identify the uncertainty composition in the SFMUH,the tracking accuracy is improved.The results of ground control experiments verify the advantages of the control method for vibration suppression of the SFMUH.展开更多
Serious startup drift of the Ring Laser Gyroscope(RLG)is observed during cold startup process,which will dramatically degrade the performances of the corresponding Inertial Navigation System(INS).In this paper,correla...Serious startup drift of the Ring Laser Gyroscope(RLG)is observed during cold startup process,which will dramatically degrade the performances of the corresponding Inertial Navigation System(INS).In this paper,correlation analysis method,which analyzes the relationship between the startup drift of the RLG and the temperature change,is used to determine the significant temperature-related terms during gyroscope startup.Based on the significant temperature-related terms and the startup time length,a startup drift compensation model for RLG based on monotonicity-constrained Radial Basis Function(RBF)neural network is proposed and validated.Compared with the raw RLG data without compensation,the standard deviation of the RLG output with the proposed constrained RBF network model is decreased by more than 46%,and the peak-to-peak value is decreased by more than 35%.Compared with the traditional multiple regression model,the standard deviation and peak-to-peak value of the RLG output are decreased by more than 10%and 6%,respectively.Compared with the common RBF network model,the standard deviation and peak-to-peak value of the RLG output are decreased by more than 8%and 3%,respectively.Navigation experiments also validate the effectiveness of the compensation model.展开更多
基金supported by the National Natural Science Foundation of China(No.52275090)the Fundamental Research Funds for the Central Universities(No.N2103025)+1 种基金the National Key Research and Development Program of China(No.2020YFB2007802)the Applied Basic Research Program of Liaoning Province(No.2023JH2/101300159)。
文摘In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter perturbation caused by the uncertainty derived from grasping mass variation cannot be ignored.The existence of vibration and parameter perturbation makes the rotation control of flexible manipulators difficult,which seriously affects the operation accuracy of manipulators.What’s more,the complex dynamic coupling brings great challenges to the dynamics modeling and vibration analysis.To solve this problem,this paper takes the space flexible manipulator with an underactuated hand(SFMUH)as the research object.The dynamics model considering flexibility,multiple nonlinear elements and disturbance torque is established by the assumed modal method(AMM)and Hamilton’s principle.A dynamic modeling simplification method is proposed by analyzing the nonlinear terms.What’s more,a sliding mode control(SMC)method combined with the radial basis function(RBF)neural network compensation is proposed.Besides,the control law is designed using a saturation function in the control method to weaken the chatter phenomenon.With the help of neural networks to identify the uncertainty composition in the SFMUH,the tracking accuracy is improved.The results of ground control experiments verify the advantages of the control method for vibration suppression of the SFMUH.
基金supported in part by the National Natural Science Foundation of China(No.61203199)。
文摘Serious startup drift of the Ring Laser Gyroscope(RLG)is observed during cold startup process,which will dramatically degrade the performances of the corresponding Inertial Navigation System(INS).In this paper,correlation analysis method,which analyzes the relationship between the startup drift of the RLG and the temperature change,is used to determine the significant temperature-related terms during gyroscope startup.Based on the significant temperature-related terms and the startup time length,a startup drift compensation model for RLG based on monotonicity-constrained Radial Basis Function(RBF)neural network is proposed and validated.Compared with the raw RLG data without compensation,the standard deviation of the RLG output with the proposed constrained RBF network model is decreased by more than 46%,and the peak-to-peak value is decreased by more than 35%.Compared with the traditional multiple regression model,the standard deviation and peak-to-peak value of the RLG output are decreased by more than 10%and 6%,respectively.Compared with the common RBF network model,the standard deviation and peak-to-peak value of the RLG output are decreased by more than 8%and 3%,respectively.Navigation experiments also validate the effectiveness of the compensation model.
