Adaptive trajectory tracking control of a free-flying space robot subject to input nonlinearities

The input nonlinearities widely exist in a large range of mechanical systems. Nonetheless, they were relatively less considered in the trajectory tracking control of a space robot in the previous studies. In this paper, an adaptive neural network (NN) control method is proposed for the fast and exact trajectory tracking control of an attitude-controlled free-flying space robot subject to input nonlinearities. The parametric uncertainties and external disturbances are also taken into the consideration. First, a model-based controller is designed to track the desired trajectory of the space robot within a framework of backstepping technique. Then, an adaptive NN controller is designed by using two NNs to compensate for the lumped uncertainties caused by parametric uncertainties and external disturbances and the input nonlinearities, respectively. Rigorous theoretical analysis for the semiglobal uniform ultimate boundedness of the whole closed-loop system is provided. The proposed adaptive NN controller is structurally simple and model-independent, which makes the controller affordable for practical applications. In addition, the proposed adaptive NN controller can guarantee the position and velocity tracking errors converge to the small neighborhoods about zero even in the presence of parametric uncertainties, external disturbances, and input nonlinearities. To the best of the authors' knowledge, there are really limited existing controllers can achieve such excellent performance in the same conditions. Numerical simulations illustrate the effectiveness and superiority of the proposed control method.