In this paper, the finite-time tracking control problem of a class of multi-agent systems with nonaffine functions and uncertain nonlinearity is investigated, which is different from the existing on high-order multi-agent systems with pure feedback forms. The multi-agent systems considered in this paper, moreover, the nonaffine functions and uncertain nonlinearities are completely unknown, and the input of each follower agent is quantized through a hysteresis quantizer. Based on the help of the fuzzy logic systems approximator, an adaptive fuzzy finite-time tracking control protocol with adaptive update laws is presented by the backstepping technique. On the basis of the finite-time stability strategy and Bhat and Bernstein theorem, the finite-time stability of designed control protocol is fully analyzed. Under the proposed control protocol, it is indicated that the tracking error of each follower agent can press on a small neighborhood in a finite time. Finally, the effectiveness of designed control protocol in this paper is analyzed by numerical examples.

The mutual information (MI) is used as the target function to jointly optimize the space trajectory and radiated power of the airborne MIMO radar. By adjusting the space position of the radar and radiating energy in real time, the detection efficiency of the MIMO radar is improved. Firstly, the cooperative detection model of aviation swarm MIMO radar is constructed to quantitatively describe the relationship between radar position and radiated power parameters and echo. Therefore, the MI between the transmitted signal and the received signal at the same time is derived, and the MI of the radar echo at the current time and the next time is derived. Maximizing the amount of MI sent and received signals can improve the amount of information detected, and minimizing the amount of MI at adjacent moments can improve the quality of information. This paper designs a time-sharing optimization algorithm, and improves the Artificial Bee Colony algorithm (ABC) to optimize the above two MI to achieve real-time adjustment of radar position and power. Through simulation verification and algorithm comparison, the advantages of this algorithm are reflected.

Different from an ordinary project, a large group mission like the unmanned aerial vehicle (UAV) swarm cooperative strike mission is performed by multiple executors and needs to be strictly carried out according to the plan. Because of the complex cooperative relationships between the sub-missions that make up a large mission, a small disturbance may cause a delay in the entire plan. Therefore, the mission process must be precisely controlled in real time to resist disturbances and ensure that the mission proceeds as planned. To address the real-time process control problem of large group missions, we propose a novel dual-code group network plan diagram model that enables plan description and process tracking for complex group missions. Additionally, a mission process closed-loop feedback control system is designed that models the mission process control problem as a mapping from the mission state observation to plan adjustment. Furthermore, an analytic-based mission process control strategy is proposed and rigorously proven to converge and be effective, as well as demonstrate the maximum anti-disturbance capability. Finally, the control strategy is tested on a UAV swarm cooperative strike mission containing 56 sub-missions. The simulation results demonstrate that the proposed control strategy is capable of achieving high, fast, and accurate control for the mission process and enhancing the anti-disturbance capability of the plan by adjusting the mission plan in real time. This will provide a valuable reference for the management of large group missions.