Neural Network L-two-gain Robust Control for Flexible Arm Space Robot Based on Virtual Control Force Conception

Concerned dynamic modeling and control problems are discussed for free-floating flexible arm space robot with uncertain parameters and an uncontrolled base.According to the geometric relationship and law of conversation of momentum,the Lagrange equation of the second kind is utilized to model the dynamic function of the flexible arm space robot incorporating the assumed modes method.By using this model,a neural network L-two-gain robust control scheme with L-two-gain disturbance attenuation is proposed to dominate the base attitude and the joint angle of manipulator to track desired trajectories synchronously in joint space on condition that system parameters are unknown.In order to damp out vibration,conception of virtual force is used to design hybrid desired trajectory which integrate both flexible mode and rigid motion,through transforming the original control scheme and a neural network L-two-gain robust control based on virtual force conception is proposed.The control scheme needs neither linearly parameterize the dynamic equations of the system,nor know any system parameters.Since using the concept of virtual control force,so rigid trajectory track is guaranteed just by inputting one control,and at the same time,active suppression on flexible vibration is made,it's more suitable in practical using for space robot system.Theoretical analysis and simulation results verify the feasibility of the proposed control schemes.