Output feedback control for free-floating space robotic manipulators base on adaptive fuzzy neural network

Free-floating space robotic manipulators with uncertainty of model and external disturbances are considered by the paper. Joints motors of space manipulators usually work at low-velocity operating conditions, tachometers are difficult to satisfy signal request. The paper brings forward a model-free fuzzy basis function network output feedback control strategy of space robot base on velocity reconstructed. Firstly, space robot dynamic model is established, and then velocity signals are reconstructed by observer, the fuzzy basis function network is used to approximate nonlinear function of observer and controller and compensated the effect of system deviations to the velocity reconstruction. The network weights and the parameters can be tuned online by hybrid back-propagation algorithm and e-correction algorithm, with no off-line learning phase required. The proposed observer and controller do not require any space robot dynamic model (including the inertia matrix inverse). Based on Lyapunov theory, the paper proves that the whole closed-loop system is uniformly ultimately bounded (UUB). Comparison results further show the proposed control method of superiority comparison. Simulation results verify that the observer-based output feedback control strategy can achieve higher precision, and has important engineering value to uncertainty space robots without velocity information.