Neural Adaptive Intermittent Output Feedback Control for Autonomous Underwater Vehicles With Full-State Quantitative Designs.

In this article, a neural adaptive intermittent output feedback control is investigated for autonomous underwater vehicles (AUVs) with full-state quantitative designs (FSQDs). To achieve the prespecified tracking performance determined by quantitative indices (e.g.

Adaptive Consensus Control for Nonlinear Multiagent Systems With Unknown Control Directions Using Event-Triggered Communication.

In this article, under directed graphs, an adaptive consensus tracking control scheme is proposed for a class of nonlinear multiagent systems with completely unknown control coefficients. Unlike the existing results, here, each agent is allowed to have multiple unknown nonidentical control directions, and continuous communication between neighboring agents is not needed. For each agent, we design a group of novel Nussbaum functions and construct a monotonously increasing sequence in which the effects of our Nussbaum functions reinforce rather than counteract each other.

Event-Based Consensus for Linear Multiagent Systems Without Continuous Communication.

In this paper, we propose a new distributed event-trigger consensus protocol for linear multiagent systems with external disturbances. Two consensus problems are considered: one is a leader-follower case and the other is a nonleader case. Different from the existing results, our proposed scheme enables each agent to decide when to transmit its state signals to its neighbors such that continuous communication between neighboring agents is avoided.