Adaptive fixed-time minimal learning force/position control of uncertain manipulators subject to input saturation

This article solves the fixed-time force/position control problem for constrained manipulators in the presence of input saturation and uncertain dynamics. Under the fixed-time stability theory, a novel fixed-time auxiliary dynamic system (ADS) is first presented to compensate for the effects of input saturation nonlinearity. System uncertainties are estimated by using radial basis function neural networks (RBF NNs) and only need to tune one neural parameter online. In addition, with a fixed-time sliding mode surface and the proposed fixed-time ADS, a novel fixed-time adaptive neural force/position controller is designed which can not only ensure the fixed-time stability of the position tracking error but also enable the manipulator to track the desired force trajectory. By using the Lyapunov method, the boundedness of all signals in the closed-loop system is proved. Finally, the effectiveness of the proposed method is demonstrated by comparative simulation works.