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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]

This paper solves the prescribed-time control problem for a class of robotic manipulators with system uncertainty and dead zone input. To make the system stable within a given convergence time T, a novel prescribed-time adaptive neural tracking controller is proposed by using the temporal scale transformation method and Lyapunov stability theory. Unlike the finite-time and the fixed-time stability where the convergence time depends on the controller parameters, the convergence time constant T is introduced into the proposed controller so that the closed-loop system will be stable within T. To cope with the system uncertainty, radial basis function neural networks (RBFNNs) are used and only need to update one parameter online. In addition, by choosing the same structure and parameters of RBFNNs, the proposed method can shorten the convergence time of the neural networks. Finally, simulation results are presented to demonstrate the effectiveness of the prescribed-time controller. [ABSTRACT FROM AUTHOR]

This article investigates the cooperative tracking control of multiple homogeneous uncertain nonholonomic wheeled mobile robots with state constraints. Transforming each mobile robot system into a chained form, a cooperative learning control scheme based on the adaptive neural network is proposed. Firstly, a virtual control law is designed for the kinematic model of the constrained chain system combined with the barrier Lyapunov function (BLF). Then, radial basis function neural networks (RBF NNs) are exploited to deal with the unknown nonlinear dynamics in the mobile robot system, a robust term is introduced to compensate for the NN approximation errors and the external disturbance, and the Moore–Penrose inverse is adopted to avoid the violation of state constraints. Communication network is used to realize the online sharing of NN weights of each mobile robot individuals, such that locally accurate identification of the unknown nonlinear dynamics with common optimal weights can be obtained. As a result, the learned knowledge can be reused in the cooperative learning control tasks and the trained network model has better generalization capabilities than the normal decentralized learning control. Finally, numerical simulation verifies the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]