Linear-quadratic optimal model-following control of a helicopter in hover

Pieper, J.K.; Baillie, S.W.; Goheen, K. R.

doi:10.1109/acc.1994.735223

Cited by 7 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, by using the SFEM developed herein, accurate low altitude flight aircraft models can be realized for control system design purposes. These models can be used in the controller for model-based control via numerous techniques including optimal control, for example, the linear quadratic regulator [14]. He has held research and teaching positions at Alcan Canada in Kingston, the National Research Council of Canada in Ottawa, and Carleton University in Ottawa.…”

Section: Discussionmentioning

confidence: 99%

A strapdown inertial navigation system for the flat-Earth model

Lovren

Pieper

1997

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

The development of a strapdown inertial navigation system (SINS) for aerodynamically controlled vehicles, which are limited to altitudes below 30 km (that is, a small distance compared with the Earth's radius of about 7000 km), or using the so-called flat-Earth model (FEM), is the principal objective of this work. In dealing with the FEM equations, the north, east, down (NED) frame on the surface of the Earth is taken as an inertial reference frame. Although, this frame is both accelerating and rotating, the accelerations associated with the Earth's rotation are negligible compared with the acceleration that can be produced by a maneuvering aircraft. Also, in this model, the gravity is taken as constant. In developing the SINS for the FEM, the aerodynamic force and moment have dominant roles, depending primarily on such variables as the angle of attack and sideslip, their derivatives, components of the angular velocity of the aircraft, and the control inputs. On the other hand, the SINS deals with such variables as the small-angle rotation vectors. Thus, it was necessary to link both set of variables as state variables of the strapdown FEM, as is done in this work. The developed model is relevant for small (less than 20 ± ) angles of attack and sideslip.

show abstract

Section: Discussionmentioning

confidence: 99%

A strapdown inertial navigation system for the flat-Earth model

Lovren

Pieper

1997

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…In the work of Budiyono and Wibowo, an optimal tracking controller has been designed for a small‐scale unmanned helicopter by using feedback linearization. For the purpose of tracking the hover attitude trajectory, a linear quadratic optimal model‐following control has been developed for a helicopter in the work of Pieper et al Under the requirement of the precision of the modeling, the optimal control with respect to nonlinear systems, which requires solving the nonlinear HJB equation, are further studied. In the work of Enns and Si, a neural network (NN) dynamic programming control method under optimal condition for the UAH has been utilized.…”

Section: Introductionmentioning

confidence: 99%

Inverse optimal control for unmanned aerial helicopters with disturbances

2018

Optim Control Appl Methods

View full text Add to dashboard Cite

Summary This paper proposes an optimal control method of an unmanned aerial helicopter (UAH) with unknown disturbances. Solving the Hamilton‐Jacobi‐Bellman (HJB) equation is considered as the common approach to design an optimal controller under a meaningful cost function when facing the nonlinear optimal control problem. However, the HJB equation is hard to solve even for a simple problem. The inverse optimal control method that avoids the difficulties of solving the HJB equation has been adopted. In this inverse optimal control approach, a stabilizing optimal control law and a particular cost function that are obtained by a control Lyapunov function are required. An integrator backstepping method is used in designing the optimal control law of the UAH. Furthermore, a disturbance‐observer–based control (DOBC) approach has been adopted in the optimal control law for dealing with the unknown disturbances of the UAH system. Simulation results have been given to certify the stability of the nonlinear UAH system and the validity of this developed control method.

show abstract

Linear-quadratic optimal model-following control of a helicopter in hover

Cited by 7 publications

References 3 publications

A strapdown inertial navigation system for the flat-Earth model

A strapdown inertial navigation system for the flat-Earth model

Linear and Nonlinear Control of Small-Scale Unmanned Helicopters

Inverse optimal control for unmanned aerial helicopters with disturbances

Contact Info

Product

Resources

About