Due to the highly uncertain and dynamic nature of military conflict and planetary exploration missions, enabling aerial and terrestrial unmanned autonomous systems (UAS) to gracefully adapt to mission and environmental changes is a very challenging task. In particular, the United States Army, Air Force, Navy, and NASA have recently shown interest in the task of load transportation by means of UAS, which rely heavily on the knowledge of both the UAS model and the load dynamics to function. Most of the currently available autopilot systems for UAS were built without suspended load transportation capabilities and are thus not appropriate, for example, to assist soldiers or planetary explorers in the tasks of carrying and deploying supplies, transporting injured people, or warfare. This research provides knowledge to the problem of autonomous suspended load transportation, attending national agencies expectations that UAS will perform in a reliable manner even in the challenging situation when loads of uncertain characteristics are transported and deployed, which heavily modify the UAS dynamic during the execution of the task. This work presents a novel model-free adaptive wavenet PID (AWPID)-based controller for enabling aerial UAS to transport cable suspended loads of unknown characteristics. In order to accomplish this goal, a control design is presented which enables the UAS to perform a trajectory tracking task, based solely on the knowledge of the UAS position. The methodology proposes a novel structure, which identifies inverse error dynamics using a radial basis neural network with daughter Mexican hat wavelets activation function. A real-time load transportation mission consisting of a multi-rotorcraft UAS carrying a cable suspended load of unknown characteristics validates the effectiveness of the trajectory tracking control strategy, showing smooth control signals even when the mathematical model of the aerial UAS and load dynamics are not known.
Military applications require networked multi-UAV system to perform practically, optimally and reliably under changing mission requirements. Lacking the effective control and communication algorithms is impeding the development of multi-UAV systems significantly. In this paper, distributed optimal flocking control and network co-design problem has been investigated for networked multi-UAV system in presence of uncertain harsh environment and unknown dynamics. First, the mathematical interaction between network imperfections and practical wireless network channel performance has been investigate. Then, a novel co-model has been developed for networked multi-UAV combining effects from physical system and network channel model effectively. Then, adopting neuro dynamics programming (NDP) technique and actor-critic-identifier (ACI) design architecture, a novel online finite horizon optimal flocking control and network co-design has been proposed. The developed algorithm cannot merely obtain the distributed optimal co-design within finite time, but also relax the stringent requirement about physical UAV system and network dynamics. In addition, developed novel co-design can satisfy the practical constraints, e.g. transmit power constraint etc. The Lyapunov stability analysis is used to validate the effectiveness of developed scheme. With the proper NN weight update law, proposed co-design can ensure all closed-loop signals and NN weights are uniformly ultimately bounded (UUB). Furthermore, simulation results have been provided to demonstrate the effectiveness of the developed scheme.
A 24 GHz medium-range human detecting sensor, using the Doppler Radar Physiological Sensing (DRPS) technique, which can also detect unmanned aerial vehicles (UAVs or drones), is currently under development for potential rescue and anti-drone applications. DRPS systems are specifically designed to remotely monitor small movements of non-metallic human tissues such as cardiopulmonary activity and respiration. Once optimized, the unique capabilities of DRPS could be used to detect UAVs. Initial measurements have shown that DRPS technology is able to detect moving and stationary humans, as well as largely non-metallic multi-rotor drone helicopters. Further data processing will incorporate pattern recognition to detect multiple signatures (motor vibration and hovering patterns) of UAVs.
KEYWORDS: Information security, Network security, Systems modeling, Neural networks, Control systems, Defense and security, Silver, Nickel, Rhodium, Neurons
This work presents a game theory-based consensus problem for leaderless multi-agent systems in the presence of
adversarial inputs that are introducing disturbance to the dynamics. Given the presence of enemy components
and the possibility of malicious cyber attacks compromising the security of networked teams, a position agreement
must be reached by the networked mobile team based on environmental changes. The problem is addressed under
a distributed decision making framework that is robust to possible cyber attacks, which has an advantage over
centralized decision making in the sense that a decision maker is not required to access information from all the
other decision makers. The proposed framework derives three tuning laws for every agent; one associated with
the cost, one associated with the controller, and one with the adversarial input.
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