80 research outputs found
A Nonlinear Disturbance Observer-based Adaptive Controller Considering CG Variation of Urban Aerial Mobility
No full textThis paper aims to propose a nonlinear adaptive attitude control method for urban aerial mobility (UAM) to counteract its center of gravity (CG) variations, which is considered one of the challenging issues for UAM development. To this end, we firstly analyze the effect of the dynamic change caused by the CG variations rigorously. The analysis result uncovers that the model uncertainties (it can be regarded as the internal disturbances) due to the CG variations are not rapidly changing during the operation of UAM. Based on this observation, a nonlinear adaptive controller is suggested to attenuate the disturbances by leveraging on a two-stage design procedure with the concept of the disturbance observer-based control (DOBC) and the three-loop control topology in this paper. To be more specific, the baseline controller, based on the feedback linearization control (FBLC) in conjunction with the three-loop control structure, is designed by including the disturbances as exogenous inputs to the system. And then, the nonlinear disturbance observer is separately designed to estimate these disturbances. Finally, the performance of the proposed method is examined through numerical simulations. © 2021 IEEE
Finding Aid for the James W. Silver & Martin J. Dain Collection (MUM00411)
No full textCorrespondence between James W. Silver, author of Mississippi: The Closed Society (1964), and Martin J. Dain, a photographer who documented Silver for Life magazine
도심 항공 모빌리티의 외란 보상을 위한 적응제어 시스템 설계
No full text학위논문(석사) - 한국과학기술원 : 항공우주공학과, 2022.2,[iv, 51 p. :]This thesis proposes an adaptive control system for the multicopter type Urban Air Mobility (UAM) subjected to unknown disturbances such as uncertainties in system parameters and wind, a significant barrier to achieving its mission. First, the effects of uncertainties in a UAM dynamics such as mass, the moment of inertia, and the center of gravity that may degrade the performance are rigorously analyzed.
The analysis results show that they appear to be a form of a constant bias or low-frequency disturbance. Based on these observations, the data-driven machine learning-based adaptive law is proposed for disturbance estimation by leveraging the modeling power of Gaussian process regression (GPR). While obtaining a training dataset for learning the GPR model, we utilize the conventional adaptive laws, e.g., time-delay approximation method, and nonlinear disturbance observer, for practicality. The entire control system is integrated by augmenting the disturbance estimation methodology with the baseline control applying the feedback linearization and time-scale separation assumption. Additionally, the control allocation algorithm to coordinate the virtual control efforts and further increase power efficiency is also proposed as our work concerns the over-actuated mechanical system. Finally, the numerical simulations verify the performance of the proposed disturbance rejection control algorithm in the presence of unknown external disturbance. The results present the enhanced performance of the proposed controller when compared with the previous conventional disturbance rejection approaches.한국과학기술원 :항공우주공학과
MOCKERY AND PIETY IN MACHADO DE ASSIS AND EÇA DE QUEIRÓS
No full textThe realist fictions of the Brazilian author Machado de Assis often explored religious themes through the presentation of keenly significant, minute details of everyday scenes and conversations. By contrast, the early novels of his contemporary Eça de Queirós demonstrate a preference for plotting broad dramatic climaxes. Machado and Eça coincide in exposing the unacknowledged secularization of Catholic consciences in Portugal and Brazil
Nonlinear Three-Loop Autopilot Design for Spaceplanes
No full textThis paper deals with the nonlinear autopilot design for spaceplanes based on the three-loop autopilot architecture. To this end, the nonlinear dynamics equations for spaceplanes during the reentry phase are first determined. The dynamic characteristics of the dynamics model are then investigated. The analysis results show that the time-scale separation is valid in the autopilot design for spaceplanes. Accordingly, based on the approximation of the time-scale separation, the proposed autopilot is designed by leveraging the feedback linearization control technique in conjunction with specific forms of the desired error dynamics. The key feature of the proposed autopilot lies in the fact that the resultant autopilot is given by the nonlinear three-loop autopilot structure, which has been widely applied to various flight vehicles. Thus, favorable characteristics of the three-loop autopilot are inherited. Numerical simulations verify our findings in this study
A proposito di Hyp. II LGRQA Soph. Ph. Dain: Aristofane di Bisanzio e la cosiddetta rubrica della MYΘOΓOΠA
No full textA small number of scholarly hypotheseis to the Greek tragedies are expressly attributed in several medieval manuscripts to Aristophanes of Byzantium, while other hypotheseis with the same subjects and formal characteristics do not name the author. The actual role that could be awarded to the great Alexandrian philologist is heavily debated until today. No single hypothesis of the Aristophanes’ type is preserved in its original condition: only in the fewest cases have the copyists adopted the constitutive rubrics almost entirely; the major part was more often left out. Nevertheless, a definite sequence has been observed in the order of the preserved rubrics, most of which are characterized by repeated linguistic formulae. The rubrics that can be identified contain these elements: a very brief synopsis, μνϑgoπgoıíα, locus actionis — compositio chori — persona prologi, τό κɛφάλαıgoν,didascalia, an aesthetic judgement, τά τgoíδράματgoς πρόσωπα.The following article does not question the genuine character of the hypotheseis that are known to have been written by Aristophanes of Byzantium. Taking the example of hyp. II LGRQA Soph. Ph. Dain it is shown that many problems of those texts are related to their complex transmission history
A disturbance rejection control for urban air mobility using artificial sensor-based Gaussian process regression
No full textThis paper presents a learning -based disturbance rejection control strategy for Urban Air Mobility (UAM) with vertical take -off and landing capability, which is subject to uncertainties in system parameters. The two primary sources of uncertainty during UAM operation, specifically moment of inertia uncertainty and center of gravity variation, are thoroughly analyzed as they negatively impact control performance. Building upon the analysis outcomes, a novel adaptive scheme is proposed that employs the modeling capabilities of Gaussian process regression for online learning to estimate model uncertainties. To ensure the collection of high -quality training data without relying on state derivatives, a nonlinear disturbance observer is employed as an artificial sensor. The suggested control algorithm is formulated by integrating Gaussian process regression with a baseline control derived using the feedback linearization control technique. Theoretical analysis grounded in the Lyapunov theorem reveals that the tracking error of the closed -loop system is semi -globally uniformly and ultimately bounded. Numerical simulations are conducted to validate the effectiveness of the proposed approach. The results obtained confirm that the proposed method can achieve superior tracking performance, even in the presence of model uncertainties and time -varying disturbances, surpassing existing approaches.
Gaussian Process-based Adaptive Path-Following Guidance for Unmanned Aerial Vehicles
No full textIn this paper, an adaptive path-following guidance algorithm is proposed by leveraging the Gaussian process regression (GPR) model. To this end, a baseline path-following guidance law is first derived by utilizing the guidance kinematics and the specific form of the error dynamics called the optimal error dynamics, under the assumption that unknown external disturbance is measurable. A GPR model is designed with the purpose of estimating the unknown disturbance term. The GPR model is then augmented to the baseline path-following guidance law by replacing the unknown disturbance term in the guidance command ith the GPR model output. To construct a dataset for training the GPR model, a nonlinear disturbance observer is used to determine the output variable of the GPR model. Finally, the proposed algorithm is tested through numerical simulations
A Computationally Effective Gaussian Process Regression-based Path-following Guidance Law for Unmanned Vehicle
No full textIn this study, we propose a Gaussian Process Regression-based disturbance rejection path-following guidance law to enable unmanned vehicles to precisely follow predefined paths in the presence of disturbances such as wind. The baseline guidance algorithm is designed by feedback linearization and optimal error dynamics. The Gaussian process regression through the Kalman filter is introduced to reduce the complexity of the classic Gaussian process regression for estimating the disturbances. Through simulations, the performance of the designed disturbance rejection path-following algorithm is validated
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