L(sub 1) Adaptive Flight Control System: Flight Evaluation and Technology Transition

Certification of adaptive control technologies for both manned and unmanned aircraft represent a major challenge for current Verification and Validation techniques. A (missing) key step towards flight certification of adaptive flight control systems is the definition and development of analysis tools and methods to support Verification and Validation for nonlinear systems, similar to the procedures currently used for linear systems. In this paper, we describe and demonstrate the advantages of L(sub l) adaptive control architectures for closing some of the gaps in certification of adaptive flight control systems, which may facilitate the transition of adaptive control into military and commercial aerospace applications. As illustrative examples, we present the results of a piloted simulation evaluation on the NASA AirSTAR flight test vehicle, and results of an extensive flight test program conducted by the Naval Postgraduate School to demonstrate the advantages of L(sub l) adaptive control as a verifiable robust adaptive flight control system

Experimental Validation of L1 Adaptive Control: Rohrs' Counterexample in Flight

The paper presents new results on the verification and in-flight validation of an L1 adaptive flight control system, and proposes a general methodology for verification and validation of adaptive flight control algorithms. The proposed framework is based on Rohrs counterexample, a benchmark problem presented in the early 80s to show the limitations of adaptive controllers developed at that time. In this paper, the framework is used to evaluate the performance and robustness characteristics of an L1 adaptive control augmentation loop implemented onboard a small unmanned aerial vehicle. Hardware-in-the-loop simulations and flight test results confirm the ability of the L1 adaptive controller to maintain stability and predictable performance of the closed loop adaptive system in the presence of general (artificially injected) unmodeled dynamics. The results demonstrate the advantages of L1 adaptive control as a verifiable robust adaptive control architecture with the potential of reducing flight control design costs and facilitating the transition of adaptive control into advanced flight control systems

Stabilization of Cascaded Systems via L1 Adaptive Controller with Application to a UAV Path Following Problem and Flight Test Results

Abstract — This paper presents a theoretical framework for augmenting an existing autopilot by an adaptive element so that it tracks a given smooth reference command with desired specifications. The main contribution of the approach is that it allows for augmenting the autopilot without any modifications to it. The augmentative adaptive element is based on the L1 adaptive output feedback control architecture developed in [1]. The complete path following architecture of this paper enables a UAV with an off-the-shelf autopilot to follow a predetermined path that it was not otherwise designed to follow. The paper concludes with flight test results performed in Camp Roberts, CA, in February of 2007. I

Flight Validation of a Metrics Driven L(sub 1) Adaptive Control

The paper addresses initial steps involved in the development and flight implementation of new metrics driven L1 adaptive flight control system. The work concentrates on (i) definition of appropriate control driven metrics that account for the control surface failures; (ii) tailoring recently developed L1 adaptive controller to the design of adaptive flight control systems that explicitly address these metrics in the presence of control surface failures and dynamic changes under adverse flight conditions; (iii) development of a flight control system for implementation of the resulting algorithms onboard of small UAV; and (iv) conducting a comprehensive flight test program that demonstrates performance of the developed adaptive control algorithms in the presence of failures. As the initial milestone the paper concentrates on the adaptive flight system setup and initial efforts addressing the ability of a commercial off-the-shelf AP with and without adaptive augmentation to recover from control surface failures

Low-Cost High Precision Aerial Delivery System (LC-ADS)

FY2016 Funded ProposalResearch Proposa

Book Review of Cooperative Path Planning of Unmanned Aerial Vehicles, by Tsourdos A., White B., and Shanmugavel M.

The article of record as published may be found at https://doi.org/10.2514/1.5485

Generalized Optimal Control for Autonomous Mine Countermeasures Missions

The article of record as published may be found at https://doi.org/10.1109/JOE.2020.2998930This article presents a computational framework for planning mine countermeasures (MCM) search missions by autonomous vehicles. It employs generalized optimal control (GenOC), a model-based trajectory optimization approach, to maximize the expected search performance of vehicle–sensor pairs in different minehunting scenarios. We describe each element of the proposed framework and adapt it to solve real-world MCM motion planning problems. A key contribution of this article develops sensor models that are more tunable than conventional ones based on lateral range curves. The proposed models incorporate engineering parameters and 3-D geometry to compute mine detection probability as a function of sonar design and search vehicle trajectories. Specific examples for various forward-looking and sidescan sonar systems deployed by unmanned vehicles are included. Objective computations utilize these sonar detection models during optimization to minimize the risk that candidate search trajectories fail to detect mines in an area of interest. Simulation results highlight the flexibility of our proposed GenOC framework and confirm that optimal trajectories outperform conventional search patterns under time or resource constraints. We conclude by identifying some of the practical considerations of this approach, and suggest ways that numerical analysis of GenOC solutions can be used for MCM mission planning and decision aid development

Generation of Real-Time Optimal Carrier Landing Trajectories

Kaminer, IssacDobrokhodov: Principal InvestigatorNavy - N9 - Warfare SystemsNPS-17-N368-