This article studies the leader-follower weighted consensus control for a class of one-sided Lipschitz (OSL) nonlinear fractional-order (FO) multiagent systems (FOMASs) with norm-bounded time-varying parametric uncertainty and time delay. First, it addresses a more complex nonlinear form, known as the OSL nonlinearity condition, which has recently been frequently used in the control field. Second, the weighted consensus protocol is achieved using the current and time delay state information of the FOMASs, which guarantees accurate control and fast convergence speed. Third, new low-conservative sufficient conditions for asymptotic convergence and stability are obtained, using the linear matrix inequality (LMI) approach and the FO Razumikhin theorem. Further, order- and delay-dependent consensus protocol for the leader-follower OSL FOMASs is achieved, with design parameters being easily computed by solving LMI constraints. Finally, three simulation examples are studied to demonstrate the effectiveness and feasibility of the new control scheme. The new LMI-based condition is less conservative, is applicable to both Lipschitz nonlinear FOMASs and linear FOMASs, and the control protocol is more universal compared to those proposed in the existing literature.

It is well known that the passive stereo system cannot adapt well to weak texture objects, e.g., white walls. However, these weak texture targets are very common in indoor environments. In this paper, we present a novel stereo system, which consists of two cameras (an RGB camera and an IR camera) and an IR speckle projector. The RGB camera is used both for depth estimation and texture acquisition. The IR camera and the speckle projector can form a monocular structured-light (MSL) subsystem, while the two cameras can form a binocular stereo subsystem. The depth map generated by the MSL subsystem can provide external guidance for the stereo matching networks, which can improve the matching accuracy significantly. In order to verify the effectiveness of the proposed system, we build a prototype and collect a test dataset in indoor scenes. The evaluation results show that the Bad 2.0 error of the proposed system is 28.2% of the passive stereo system when the network RAFT is used. The dataset and trained models are available at https://github.com/YuhuaXu/MonoStereoFusion.