Helicopter Flight Control Law Design Using H&amp;gt;inf&amp;lt;&amp;#8734;&amp;gt;/inf&amp;lt;Techniques

Kureeemun, R.; Walker, Daniel J.; Manimala, Binoy J.; Voskuijl, Mark

doi:10.1109/cdc.2005.1582339

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…Multiloop approaches are discussed extensively in [6][7][8]. A static inner loop was computed using eigenstructure assignment, and an outer dynamic loop was computed using H 1 synthesis [6].…”

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confidence: 99%

“…A static inner loop was computed using eigenstructure assignment, and an outer dynamic loop was computed using H 1 synthesis [6]. An inner loop was designed using the linear quadratic regulator (LQR) technique, a standard H 1 outer loop [7], and four body angular measurements (roll and pitch angles and rates), and the rotor lag and flap state measurements and their derivatives are fed back. An innerloop attitude feedback control, midloop velocity feedback control, and the outer-loop position-control approach on an UAV is shown in [8], in which a multiloop single-input/single-output control structure is employed.…”

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confidence: 99%

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Structured H-Infinity Command and Control-Loop Design for Unmanned Helicopters

Gadewadikar

Lewis

Subbarao

et al. 2008

Journal of Guidance, Control, and Dynamics

123

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The aim of this paper is to present rigorous and efficient methods for designing flight controllers for unmanned helicopters that have guaranteed performance, intuitive appeal for the flight control engineer, and prescribed multivariable loop structures. Helicopter dynamics do not decouple as they do for the fixed-wing aircraft case, and so the design of helicopter flight controllers with a desirable and intuitive structure is not straightforward. We use an H 1 output-feedback design procedure that is simplified in the sense that rigorous controller designs are obtained by solving only two coupled-matrix design equations. An efficient algorithm is given for solving these that does not require initial stabilizing gains. An output-feedback approach is given that allows one to selectively close prescribed multivariable feedback loops using a reduced set of the states at each step. At each step, shaping filters may be added that improve performance and yield guaranteed robustness and speed of response. The net result yields an H 1 design with a control structure that has been historically accepted in the flight control community. As an example, a design for stationkeeping and hover of an unmanned helicopter is presented. The result is a stationkeeping hover controller with robust performance in the presence of disturbances (including wind gusts), excellent decoupling, and good speed of response. Nomenclature A = system or plant matrix a s = longitudinal blade angle B = control-input matrix b s = lateral blade angle C = output or measurement matrix D = disturbance matrix D in = inner-loop disturbance matrix D o = outer-loop disturbance matrix dt = disturbance G = nominal plant G s = loop-shaped plant K = static output-feedback gain matrix p = roll rate in the body-frame components Q = state weighting matrix q = pitch rate in the body-frame components R = control weighting matrix r = yaw rate in the body-frame components r fb = yaw-rate feedback U = velocity along the body-frame x axis ut = control input V = velocity along the body-frame y axis W = velocity along the body-frame z axis X = inertial position x axis x in t = inner-loop state vector x o t = outer-loop state vector Y = inertial position y axis y in t = inner-loop output vector y o t = outer-loop output vector Z = inertial position z axis zt = performance output = system L 2 gain in = L 2 gain inner loop o = L 2 gain outer loop = pitch angle = roll angle = yaw angle

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confidence: 99%

mentioning

confidence: 99%

Structured H-Infinity Command and Control-Loop Design for Unmanned Helicopters

Gadewadikar

Lewis

Subbarao

et al. 2008

Journal of Guidance, Control, and Dynamics

123

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“…The gimbaled like device on which the helicopter was connected to, allows only a three degrees of freedom motion of the latter. Other robust designs of helicopter control are reported in [6,50,82,97]. The work in [2] compares a simple eigenstructure assignment with full state feedback controller versus a typical LQR design.…”

Section: Linear Controller Designsmentioning

confidence: 99%

Linear and Nonlinear Control of Small-Scale Unmanned Helicopters

Raptis

Valavanis

2011

Intelligent Systems, Control and Automation: Science and Engineering

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No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Cover design: VTEX, VilniusPrinted on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) PrefaceUnmanned Aerial Vehicles (UAVs) or Unmanned Aircraft Systems (UAS), a term preferred by the U.S. Department of Defense, have seen unprecedented levels of growth over the last decade. Even though UAVs have been mainly used for military applications, there is a considerable and increasing interest for civilian applications. It is not an exaggeration to consider that as the technology matures, as small-scale UAVs become cost-effective with proven reliability and safety, and as the roadmap to integrating UAS into the National Airspace System (NAS) progresses, civilian applications will dominate the field. It is postulated that UAVs will be used in the future extensively for environmental monitoring, forest protection, wildfire detection, traffic monitoring, building, power line and bridge inspection, emergency response, crime prevention, search and rescue, mapping, surveillance, reconnaissance, border patrol, to name several applications.From all classes of UAVs, unmanned rotorcraft, and in particular unmanned helicopters, have advantages over fixed-wing UAVs because they take-off and land vertically, they do not require a runway, and they have the ability to hover and fly in (very) low altitudes. It is reasonable to assume that light-weight (<150 Kgr) and small-scale (<50 Kgr) helicopters will be the first ones to be allowed to fly in civilian airspace. Such helicopters, though, still retain the flight characteristics and physical principles of their full-scale counterparts. In addition, they are naturally more agile and dexterous compared to full-scale helicopters. Their flight capabilities, reduced size and cost have recently monopolized the attention of the UAV research community as they are preferred for a wide spectrum of applications. However, helicopters are highly unstable, nonlinear and coupled underactuated systems, and controller design for such systems is a rather challenging problem.The problem of designing autonomous flight controllers for small-scale helicopters is equally challenging, and the flight controller design problem is tightly connected with the helicopter modeling. Helicopter dynamics may be represented by both linear and nonlinear models of ordinary differential equations. Typically, the validity of the linear models is restricted in a certain region around a specific operating point, while nonlinear models provide a global description of the helicopter dynamics.

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“…Throughout the years, many control strategies for air vehicle f light c ontrol s ystem (FCS) design have been extensively researched. In historical sequence, some of these methods are classical pole placement and simple feedback methods, 1–3 modified linear quadratic regulator and l inear q uadratic G aussian (LQG) controllers, 4–6 modified H∞ control synthesis, 7–9 constrained m odel p redictive c ontrol, 10–12 and variance-constrained controllers. 13–21 Variance-constrained controllers are one of these control techniques.…”

Section: Introductionmentioning

confidence: 99%

Combined outputs variance constrained and input variance constrained design for flight control

Oktay

2015

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this article, an algorithm combining two different variance constrained control strategies (i.e. output variance constrained: OVC controller and input variance constrained: IVC controller) is developed. Performance of the resulting novel control technique called as combined output and input variance constrained controller is evaluated on helicopters. Their linearized state-space models are used to see feasibility and efficiency of this new strategy (i.e., output and input variance constrained). Closed loop responses of some outputs and inputs of interest and also some outputs out of interest are investigated while using output and input variance constrained for air vehicle flight control system. Then, closed loop performance of output and input variance constrained is compared with respect to the two other classical control techniques (i.e. outputs variance constrained and input variance constrained). Finally, robustness of output and input variance constrained with respect to the modeling uncertainities is examined.

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Helicopter Flight Control Law Design Using H>inf<∞>/inf<Techniques

Cited by 15 publications

References 10 publications

Structured H-Infinity Command and Control-Loop Design for Unmanned Helicopters

Structured H-Infinity Command and Control-Loop Design for Unmanned Helicopters

Linear and Nonlinear Control of Small-Scale Unmanned Helicopters

Combined outputs variance constrained and input variance constrained design for flight control

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