arXiv subjects

Matthew Longmire

Publications and source records attributed to Matthew Longmire.

3 recordsLinked to original sources

arXiv2023

Distributed Asynchronous Discrete-Time Feedback Optimization

In this article, we present an algorithm that drives the outputs of a network of agents to jointly track the solutions of time-varying optimization problems in a way that is robust to asynchrony in the agents' operations. We consider three operations that can be asynchronous: (1) computations of control inputs, (2) measurements of network outputs, and (3) communications of agents' inputs and outputs. We first show that our algorithm converges to the solution of a time-invariant feedback optimization problem in linear time. Next, we show that our algorithm drives outputs to track the solution of time-varying feedback optimization problems within a bounded error dependent upon the movement of the minimizers and degree of asynchrony in a way that we make precise. These convergence results are extended to quantify agents' asymptotic behavior as the length of their time horizon approaches infinity. Then, to ensure satisfactory network performance, we specify the timing of agents' operations relative to changes in the objective function that ensure a desired error bound. Numerical experiments confirm these developments and show the success of our distributed feedback optimization algorithm under asynchrony.

math.OC↗

arXiv2023

Deep Nonlinear Adaptive Control for Unmanned Aerial Systems Operating under Dynamic Uncertainties

Recent literature in the field of machine learning (ML) control has shown promising theoretical results for a Deep Neural Network (DNN) based Nonlinear Adaptive Controller (DNAC) capable of achieving trajectory tracking for nonlinear systems. Expanding on this work, this paper applies DNAC to the Attitude Control System (ACS) of a quadrotor and shows improvement to attitude control performance under disturbed flying conditions where the model uncertainty is high. Moreover, these results are noteworthy for ML control because they were achieved with no prior training data and an arbitrary system dynamics initialization; simply put, the controller presented in this paper is practically modelless, yet yields the ability to force trajectory tracking for nonlinear systems while rejecting significant undesirable model disturbances learned through a DNN. The combination of ML techniques to learn a system's dynamics and the Lyapunov analysis required to provide stability guarantees leads to a controller with applications in safety-critical systems that may undergo uncertain model changes, as is the case for most aerial systems. Experimental findings are analyzed in the final section of this paper, and DNAC is shown to outperform the trajectory tracking capabilities of PID, MRAC, and the recently developed Deep Model Reference Adaptive Control (DMRAC) schemes.

eess.SY↗

arXiv2023

Learning Flight Control Systems from Human Demonstrations and Real-Time Uncertainty-Informed Interventions

This paper describes a methodology for learning flight control systems from human demonstrations and interventions while considering the estimated uncertainty in the learned models. The proposed approach uses human demonstrations to train an initial model via imitation learning and then iteratively, improve its performance by using real-time human interventions. The aim of the interventions is to correct undesired behaviors and adapt the model to changes in the task dynamics. The learned model uncertainty is estimated in real-time via Monte Carlo Dropout and the human supervisor is cued for intervention via an audiovisual signal when this uncertainty exceeds a predefined threshold. This proposed approach is validated in an autonomous quadrotor landing task on both fixed and moving platforms. It is shown that with this algorithm, a human can rapidly teach a flight task to an unmanned aerial vehicle via demonstrating expert trajectories and then adapt the learned model by intervening when the learned controller performs any undesired maneuver, the task changes, and/or the model uncertainty exceeds a threshold

cs.RO↗