Neural network-based dynamic target enclosing control for uncertain nonlinear multi-agent systems over signed networks.

Neural networks have significant advantages in the estimation of uncertainty dynamics, which can afford highly accurate prediction outcomes and enhance control robustness. With this in mind, this study presents a neural network-based method to investigate the uncertain target enclosing control problem for multi-agent systems over signed networks. Firstly, a nominal target enclosing controller is constructed by adding the target information component into the classical bipartite consensus error, in which the multi-agent system can be grouped to enclose the target from opposite sides.

Robust bipartite tracking consensus of multi-agent systems via neural network combined with extended high-gain observer.

In this paper, the robust bipartite tracking consensus problem for second-order multi-agent systems has been addressed in the presence of lumped disturbances and unknown velocity information. The leader's dynamics are modeled by uncertain fractional-order equality based on the neural network (NN), and the followers can only obtain the position information of the leader under the signed networks. A novel robust bipartite tracking consensus control scheme is developed by combining the NN approximators, the continuous sliding mode control (SMC) strategy, and the improved extended high-gain observer (EHGO).

An Image Augmentation Method Based on Limited Samples for Object Tracking Based on Mobile Platform.

This paper proposes an image augmentation model of limited samples on the mobile platform for object tracking. The augmentation method mainly aims at the detection failure caused by the small number of effective samples, jitter of tracking platform, and relative rotation between camera and object in the tracking process. Aiming at the object tracking problem, we first propose to use geometric projection transformation, multi-directional overlay blurring, and random background filling to improve the generalization ability of samples.