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In this paper, a fixed-time control method is proposed for an uncertain robotic system with actuator saturation and constraints that occur a period of time after the system operation. A model-based control and a neural network-based learning approach are proposed under the framework of fixed-time convergence, respectively. We use neural networks to handle the uncertainty, and design an adaptive law driven by approximation errors to compensate the input deadzone. In addition, a new structure of stabilizing function combining with an error shifting function is introduced to demonstrate the robotic system stability and the boundedness of all error signals. It is proved that all the tracking errors converge into the compact sets near zero in fixed-time according to the Lyapunov stability theory. Simulations on a two-joint robot manipulator and experiments on a six-joint robot manipulator verified the effectiveness of the proposed fixed-time control algorithm. • A new error shifting function is designed for the proposed constraints. • A fixed-time control scheme integrates the input deadzone and system uncertainty. • Constraints are applied at some time after the start, instead of the beginning. • A novel structure of the Lyapunov stabilizing function is proposed. [ABSTRACT FROM AUTHOR]

A neural networks (NNs)-based learning policy is proposed for strict-feedback nonlinear systems with asymmetric full-state constraints and unknown gain directions. A state-constrained function is introduced such that the proposed adaptive control policy works for systems with constraints or without constraints in a unified structure. Furthermore, the unified state-constrained function can also deal with symmetric and asymmetric constraints without changing adaptive structures, which also avoids discontinuous actions. With Nussbaum gain technique and NNs-based approximation technique, the proposed control method can also effectively deal with the unknown signs of control gains, and matched and mismatched uncertainties are also solved by NN approximation technique. According to the Lyapunov theory, the tracking errors can be proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally the effectiveness of the proposed scheme is validated by numerical simulations. • State-constrained functions for dealing with symmetric and asymmetric constraints and constrained and unconstrained requirements. • Neural networks learning for identifying the mismatched and matched uncertainties and avoiding the linear-in-the-parameter (LIP) condition. • Nussbaum gain technique for removing any priori knowledges of the control gains. [ABSTRACT FROM AUTHOR]

We investigate the trajectory tracking problem of a quadrotor-slung-load system with unknown load mass in the presence of pre-specified performance specifications imposed on both the position of the load and the cable direction. In contrast to existing literature where performance bounds are only function of time, here we propose a revised performance bound which is also function of the tracking errors associated with the slung load subsystem. The unknown load mass is obtained from an adaptive law that is designed within the framework of backstepping. All system errors are shown to be uniformly ultimately bounded, with simultaneous guarantees of prescribed accuracy and constraint satisfaction. Finally, simulation results are presented to validate and assess the effectiveness and robustness of the proposed methodology. [ABSTRACT FROM AUTHOR]