Adaptive neural network control for free-floating space robot

Free-floating space robot system is a very complicated nonlinear analysis. Traditional control strategy cannot meet the technical requirements when considering uncertain terms and external disturbances. For this reason, the influence of the uncertainty to the system is considered in this article. This article regard the control error and the error rate as system state. Basing on neural network on-line modeling technique, design the change rate of network weights and approach the uncertain parts by weight of training. Then design adaptive neural network control item, which can be used to amend nominal-model-based control and compensate the control error caused by uncertain terms. Meanwhile, The stability of control system is proved. The numerical simulation of 7 DOF(degree of freedom)space robot show that with the adaptive neural network controller proposed in this article, the end-effector with disturbances and load can also close to the desired trajectory and converge when the error within a finite range. The controller performs better than traditional PID controller.