Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems developed for failure detection, identification, and reconfiguration, as well as upset recovery, need to be evaluated over broad regions of the flight envelope or under extreme flight conditions, and should include various sources of uncertainty. To apply formal robustness analysis, formulation of linear fractional transformation (LFT) models of complex parameter-dependent systems is required, which represent system uncertainty due to parameter uncertainty and actuator faults. This paper describes a detailed LFT model formulation procedure from the nonlinear model of a transport aircraft by using a preliminary LFT modeling software tool developed at the NASA Langley Research Center, which utilizes a matrix-based computational approach. The closed-loop system is evaluated over the entire flight envelope based on the generated LFT model which can cover nonlinear dynamics. The robustness analysis results of the closed-loop fault tolerant control system of a transport aircraft are presented. A reliable flight envelope (safe flight regime) is also calculated from the robust performance analysis results, over which the closed-loop system can achieve the desired performance of command tracking and failure detection. * Senior Staff Scientist, Robust and Adaptive Control, AIAA Member, email:shinjy@nianet.org.† Senior Researcher, Dynamic Systems and Control Branch, AIAA Member. ‡ Researcher, Dynamic Systems and Control Branch, AIAA Member.
This paper proposes an analysis framework for robustness analysis of a nonlinear dynamics system that can be represented by a polynomial linear parameter varying (PLPV) system with constant bounded uncertainty. The proposed analysis framework contains three key tools: 1) a function substitution method which can convert a nonlinear system in polynomial form into a PLPV system, 2) a matrix-based linear fractional transformation (LFT) modeling approach, which can convert a PLPV system into an LFT system with the delta block that includes key uncertainty and scheduling parameters, 3) µ-analysis, which is a well known robust analysis tool for linear systems. The proposed analysis framework is applied to evaluating the performance of the LPV-fault detection and isolation (FDI) filters of the closedloop system of a transport aircraft in the presence of unmodeled actuator dynamics and sensor gain uncertainty. The robustness analysis results are compared with nonlinear time simulations.
A methodology for improving attitude stability and control for low-speed and hovering air vehicle is under development. In addition to aerodynamically induced control forces such as vector thrusting, the new approach exploits the use of bias momenta and torque actuators, similar to a class of spacecraft system, for its guidance and control needs. This approach will be validated on a free-flying research platform under development at NASA Langley Research Center. More broadly, this platform also serves as an in-house testbed for research in new technologies aimed at improving guidance and control of a Vertical TakeOff and Landing (VTOL) vehicle. 1 Research Motivation This paper gives an overview of the ongoing research in precision guidance and robust control based on the NASA Flying Test Platform (NFTP) research vehicle currently under development at NASA Langley Research Center. The research is motivated by core GN&C objectives that include optimal guidance and navigation, and robust attitude and position stabilization under uncertain exogenous disturbances and model variations. A key goal of this research is to investigate novel technologies to improve attitude stability particularly during hovering or at low airspeed flight wherein conventional control effectors become ineffective. This particular need arises from current limitations in attitude stabilization performance for
This paper considers the problem of direct model reference adaptive control when the plant-model matching conditions are violated due to abnormal changes in the plant or incorrect knowledge of the plant's mathematical structure. The approach consists of direct adaptation of state feedback gains for state tracking, and simultaneous estimation of the plant-model mismatch. Because of the mismatch, the plant can no longer track the state of the original reference model, but may be able to track a new reference model that still provides satisfactory performance. The reference model is updated if the estimated plant-model mismatch exceeds a bound that is determined via robust stability and/or performance criteria. The resulting controller is a hybrid direct-indirect adaptive controller that offers asymptotic state tracking in the presence of plant-model mismatch as well as parameter deviations.
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