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1. Model reference adaptive control for nonlinear time‐varying hybrid dynamical systems

   https://doi.org/10.1002/acs.3631  

   L'Afflitto, Andrea (August 2023, International Journal of Adaptive Control and Signal Processing)

   M. Grimble (Ed.)

   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. 

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2. ArUco based Reference Shaping for Real-time Precision Motion Control for Suspended Payloads

   Vexler, David; Stein, Adrian; Singh, Tarunraj (July 2024, IEEE Xplore)

   This work presents a real-time time-delay filtering approach for reference shaping of high precision motion control of vibratory systems. The motion of the system is initiated with a judicious (arbitrary) step command and the acquired motion data is used to estimate the modal parameters in realtime.The modal data is subsequently used to synthesize the subsequent step commands to mitigate the residual vibrations. The proposed control algorithm is tested on a gantry crane structure with a suspended payload. Our method estimates the system parameters based on computer vision while tracking an ArUco fiducial marker which is integral with the payload. Computational efficiency is ensured by using C++ to deploy the algorithm. The goal is to minimize the residual energy at the terminal displacement for rest-to-rest maneuvers of a suspended payload with unknown dynamics. An inertial measurement unit is used to track the pendular angular velocity at the end of the maneuver and is not used in the model identification process. 

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3. (2021). Making flying microgrids work in future aircrafts and aerospace vehicles. 52, 428-445.

   Ilić, M. D. (July 2021, Annual reviews in control)

   This paper concerns modeling, simulations and control design of turbo-electric distributed propulsion (TeDP) systems needed to power future hybrid aircraft systems. The approach taken is the one of control co-design by which the sizing and hardware selection of components and the TeDP architecture design are pursued so that potential e ects of control and automation are accounted for from the very beginning. Unique to this approach is a multi-layered modular modeling and control approach in which technology-specific modules comprising the complex dynamical system are characterized using unified interaction variables at their interfaces with the rest of the system. The dynamical performance of the interconnected system is assessed using these technology-agnostic interface variable specifications and, as such, can be applied to any candidate architecture of interest. Importantly, even the inputs to the TeDP system coming from pilot commands are modeled using such interface variables. This new multi-layered modeling captures the dynamics of energy and power as interactions. It also has a rather straightforward physical interpretation. The paper builds on our earlier results introduced for terrestrial power systems, including small micro-grids. We show how system feasibility and stability can be checked in real-time operations by modules exchanging the information about their interaction variables and adjusting in a near-autonomous manner so that, as system conditions vary, the interconnected system still functions. No such systematic control co-design exists to the best of our knowledge, but it is needed as both new technologies and more complex, often conflicting performance objectives emerge. We illustrate the approach on a representative TeDP architecture and compare it to today’s state-of-the-art. We close with a discussion on the generalization of the method for any given candidate architecture. Having such an approach dramatically reduces the R&D&D of novel candidate architectures. 

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4. UAV-enabled Human Internet of Things

   https://doi.org/10.1109/DCOSS49796.2020.00056  

   Rael, Kelly; Fragkos, Georgios; Plusquellic, Jim; Tsiropoulou, Eirini Eleni (May 2020, 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS))

   null (Ed.)

   In this paper, an Unmanned Aerial Vehicles (UAVs) - enabled human Internet of Things (IoT) architecture is introduced to enable the rescue operations in public safety systems (PSSs). Initially, the first responders select in an autonomous manner the disaster area that they will support by considering the dynamic socio-physical changes of the surrounding environment and following a set of gradient ascent reinforcement learning algorithms. Then, the victims create coalitions among each other and the first responders at each disaster area based on the expected- maximization approach. Finally, the first responders select the UAVs that communicate with the Emergency Control Center (ECC), to which they will report the collected data from the disaster areas by adopting a set of log-linear reinforcement learning algorithms. The overall distributed UAV-enabled human Internet of Things architecture is evaluated via detailed numerical results that highlight its key operational features and the performance benefits of the proposed framework. 

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5. Emergency Landing Trajectory Optimization for Fixed-Wing UAV under Engine Failure

   https://doi.org/10.2514/6.2019-0959  

   Fang, Xiang; Wan, Neng; Jafarnejadsani, Hamidreza; Sun, Donglei; Holzapfel, Florian; Hovakimyan, Naira (January 2019, AIAA SciTech Forum, San Diego, CA)

   With the growing popularity of autonomous unmanned aerial vehicles (UAVs), the improvement of safety for UAV operations has become increasingly important. In this paper, a landing trajectory optimization scheme is proposed to generate reference landing trajectories for a fixed-wing UAV with accidental engine failure. For a specific landing objective, two types of landing trajectory optimization algorithms are investigated: i) trajectory optimization algorithm with nonlinear UAV dynamics, and ii) trajectory optimization algorithm with linearized UAV dynamics. An initialization procedure that generates an initial guess is introduced to accelerate the convergence of the optimization algorithms. The effectiveness of the proposed scheme is verified in a high-fidelity UAV simulation environment, where the optimized landing trajectories are tracked by a UAV equipped with an L1 adaptive altitude controller in both the offline and online modes. 

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