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Summary: 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. [ABSTRACT FROM AUTHOR]

In this article, a predefined time convergence adaptive tracking control scheme is designed for a class of uncertain robotic manipulators with input saturation. First, a novel auxiliary dynamic system is proposed to handle the influence of input saturation. Radial basis function neural networks are used to approximate the uncertainty of the closed‐loop system and the neural adaptive law is designed by using the given time constant so that the neural networks have a fast convergence rate. The adaptive tracking controller is constructed by utilizing a nonsingular terminal sliding mode surface. Different from the finite‐time and the fixed‐time sliding mode control methods where the upper bound of the convergence time is related to system parameters, the convergence time upper bound of the proposed sliding mode controller is a given constant. Finally, numerical simulations are performed to illustrate that the proposed control scheme possesses the advantages of fast convergence rate and input saturation elimination. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking problem of uncertain robot manipulators with input saturation and output constraints. Uncertainty and disturbance estimator (UDE) is used to tackle the model uncertainties and external disturbances. Different from most existing methods, UDE only needs the bandwidth of the unknown plant model for design, which makes it easy to be implemented. Nonlinear state-dependent function is employed to cope with output constraints and a second order auxiliary system is constructed to solve the input saturation. Finally, an UDE-based tracking controller is proposed based on the backstepping method. With the proposed control scheme, the input saturation and the output constraints are not violated, and all signals in the closed-loop system are bounded. The comparative simulation results of a two-link robot manipulator are utilized to validate the effectiveness and superiority of the proposed control method. [ABSTRACT FROM AUTHOR]

In this article, the issue of adaptive neural fixed-time tracking control for uncertain robotic manipulators subject to input saturation, external disturbance and prescribed constraints is studied. To handle the influence of input saturation, a novel auxiliary nonlinear dynamic system is constructed in which the system state is fixed-time stable. Radial basis function neural networks (RBF NNs) are used to approximate the system uncertainty. Instead of adjusting all weight vectors of RBF NNs, only one parameter is needed to be updated online. Then, based on performance function and auxiliary dynamic system, a fixed-time sliding mode controller with prescribed transient and steady-state performance is developed. Through theoretical analysis, it is concluded that the position tracking error can stabilize around the equilibrium point in fixed time and satisfy the prescribed requirements. Meanwhile, all signals in the closed-loop system are proved to be fixed-time stable by using the Lyapunov method. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]