Unmanned aerial manipulator (UAM) is usually a combination of a quadrotor and a robotic arm that can exert active influences on the environments. The control problems of the UAM system include model uncertainty caused by its center of gravity shift and external disturbances from the environments. To handle these two disturbances, a tracking control strategy is proposed for position and attitude control of the UAM in this paper. In particular, the model of the UAM is established considering with center of gravity shift and disturbances from environments. In the position control, both internal disturbances and external disturbances are compensated by using a sliding mode controller. In the attitude control, an adaptive law is designed to estimate internal disturbances, and a disturbance observer is designed to estimate external disturbances. The stability analysis of the proposed controller is provided and the effectiveness of the proposed method is verified in simulation.

This paper is concerned with antisynchronization in predefined time for two different chaotic neural networks. Firstly, a predefined-time stability theorem based on Lyapunov function is proposed. With the help of the definition of predefined time, it is convenient to establish a direct relationship between the tuning gain of the system and the fixed stabilization time. Then, the antisynchronization is achieved between two different chaotic neural networks via active control Lyapunov function design. The designed controller presents the practical advantage that the least upper bound for the settling time can be explicitly defined during the control design. With the help of the designed controller, the antisynchronization errors converge within a predefined-time period. Numerical simulations are presented in order to show the reliability of the proposed method.

This paper concentrates on the global predefined-time synchronization of delayed memristive neural networks with external unknown disturbance via an observer-based active control. First, a global predefined-time stability theorem based on a non-negative piecewise Lyapunov function is proposed, which can obtain more accurate upper bound of the settling time estimation. Subsequently, considering the delayed memristive neural networks with disturbance, a disturbance-observer is designed to approximate the external unknown disturbance in the response system with a Hurwitz theorem and then to eliminate the influence of the unknown disturbance. With the help of global predefined-time stability theorem, the predefined-time synchronization is achieved between two delayed memristive neural networks via an active control Lyapunov function design. Finally, two numerical simulations are performed, and the results are given to show the correctness and feasibility of the predefined-time stability theorem.

In this paper, a novel trajectory tracking control method of nonholonomic mobile robots based on the non-negative piecewise predefined-time theorem is proposed. The idea of cascade control is used to divide the posture error system of the mobile robot into two subsystems. Firstly, for the first-order subsystem, an active predefined-time controller is designed to realize that the angle error system converges and stabilizes to zero within a given time, which is preset in advance. Secondly, a novel predefined-time sliding mode controller is designed for the second-order subsystem, which adds a constant to compensate for the influence of singularity. Moreover, compared with the existing fixed-time control algorithm, the control scheme proposed in this paper provides a more accurate upper bound of the settling time estimation. For convenience, the complex expression of the settling time estimation is transformed into an adjustable parameter. Furthermore, the stability of the two developed controllers is analyzed and some conditions for selecting parameters are given. Finally, the simulation results show the feasibility and correctness of the proposed control algorithm. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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