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Summary This paper presents the first model reference adaptive control system for nonlinear, time‐varying, hybrid dynamical plants affected by matched and parametric uncertainties, whose resetting events are unknown functions of time and the plant's state. In addition to a control law and an adaptive law, which resemble those of the classical model reference adaptive control framework for continuous‐time dynamical systems, the proposed framework allows imposing instantaneous variations in the reference model's trajectory to rapidly steer the trajectory tracking error to zero, while retaining the closed‐loop system's ability to follow a user‐defined signal. These results are enabled by the first extension of the classical LaSalle–Yoshizawa theorem to time‐varying hybrid dynamical systems, which is presented in this paper as well. A numerical simulation shows the key features of the proposed adaptive control system and highlights its ability to reduce both the control effort and the trajectory tracking error over a classical model reference adaptive control system applied to the same problem. 

This paper describes the development, implementation, and testing of a GNSS jammer localizer using power measurement profiles collected during un-crewed aerial system (UAS) fly-bys. A linearized measurement equation based on the Friis power transmission formula is derived in which RF channel propagation parameters are grouped into a single parameter for estimation. Synchronized power and UAS position measurements are processed in a batch-type sequential non-linear least squares algorithm for simultaneous estimation of static jammer position and received power model parameters. We develop a low size, weight, power, and cost (SWAP-C) quad-rotor UAS test bed that can collect and time-stamp power measurements with UAS position. Since GNSS jamming is illegal, a LoRa 868 MHz transmitter is used as a surrogate GNSS jammer during field testing – providing Received Signal Strength Indicator (RSSI) measurements to the LoRa receiver onboard the UAS. Testing is conducted at the Virginia Tech Kentland Experimental Aerial Systems Lab, where emitter localization is evaluated for three different trajectories. Experimental performance analysis suggests that meter-level localization accuracy is achievable with prior knowledge on source location and by accounting for antenna gain pattern variations over time in the estimation process with a first order Gauss Markov Process. 

In this paper, a novel model reference adaptive control (MRAC) architecture for nonlinear, time-varying, hybrid dynamical systems is applied for the first time to design the control system of a multi-rotor unmanned aerial vehicle (UAV). The proposed control system is specifically designed to address problems of practical interests involving autonomous UAVs transporting unknown, unsteady payloads and subject to instantaneous variations both in their state and in their dynamics. These variations can be due, for instance, to the payload’s dynamics, impacts between the payload and its casing, and sudden payload dropping and pickup. The proposed hybrid MRAC architecture improves the UAV’s trajectory tracking performance over classical MRAC also in the presence of motor failures. The applicability of the proposed framework is validated numerically through the first use of the high-fidelity simulation environment PyChrono for autonomous UAV control system testing.