Precision flight control for a multi-vehicle quadrotor helicopter testbed

Hoffmann, Gabriel; Huang, Haomiao; Waslander, Steven L.; Tomlin, Claire J.

doi:10.1016/j.conengprac.2011.04.005

Cited by 257 publications

(125 citation statements)

References 18 publications

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“…Detailed treatment of the dynamics of quadrotor motion can be found in, e.g., (Hoffmann et al, 2011). Here, we assume a simplified model that is suitable for an operating regime around steady hover, and we summarize in this section the aspects of quadrotor operation that are relevant to near-hover flight.…”

Section: Quadrotor Theory Of Operationmentioning

confidence: 99%

On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments

Bouffard¹

2012

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE DEC 20122. REPORT TYPE 3. DATES COVERED 00-00-2012 to 00-00-2012 Copyright © 2012, by the author(s). TITLE AND SUBTITLE On-board Model Predictive Control of a QuadrotorAll rights reserved.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. AcknowledgementThe This report describes work in applying model predictive control (MPC) techniques to the control of quadrotor helicopters, a type of micro aerial vehicle (MAV) platform that has gained great popularity in recent years both in research and commercial/military settings. MPC is a form of optimal control which is attractive in part because it allows engineering requirements to be addressed directly in the design of the controller in terms of costs to be minimized and constraints to be satisfied in an optimization problem. Furthermore, for many engineering problems of interest, the optimization to be performed is convex, meaning that a global optimum can be efficiently computed. MPC first found broad early application in the process industry, where the typically longer time scales were compatible with the time necessary to solve the optimization problem. More recently with both the exponential increase in available computing power and the development of more efficient solution techniques, MPC has become an option for control of systems with faster dynamics, such as quadrotors.We bring together results from our application of two distinct variants of MPC. The common thread is that we seek advanced control algorithms that can be applied to an autonomous MAV like the quadrotor, ideally without requiring any external resources, i.e. we aim to perform all computations required for real-time closed-loop control on-board the vehicle.The first variant is known as explicit MPC, where in a sense the heavy numerical work of solving optimization problems is done a priori and off-line, such that the on-line implementat...

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Section: Quadrotor Theory Of Operationmentioning

confidence: 99%

On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments

Bouffard¹

2012

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“…The implementation that is probably closest to our approach is from Hoffmann et al (the STARMAC project), in which an autonomous flock of three quadrotors was shown that could perform collision-free flight using algorithms based on Nash Bargaining [19].…”

Section: Introductionmentioning

confidence: 99%

Outdoor flocking and formation flight with autonomous aerial robots

Vásárhelyi

Virágh

Somorjai

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

220

133

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Abstract-we present the first decentralized multi-copter flock that performs stable autonomous outdoor flight with up to 10 flying agents. By decentralized and autonomous we mean that all members navigate themselves based on the dynamic information received from other robots in the vicinity. We do not use central data processing or control; instead, all the necessary computations are carried out by miniature on-board computers. The only global information the system exploits is from GPS receivers, while the units use wireless modules to share this positional information with other flock members locally. Collective behavior is based on a decentralized control framework with bio-inspiration from statistical physical modelling of animal swarms. In addition, the model is optimized for stable group flight even in a noisy, windy, delayed and error-prone environment. Using this framework we successfully implemented several fundamental collective flight tasks with up to 10 units: i) we achieved self-propelled flocking in a bounded area with self-organized object avoidance capabilities and ii) performed collective target tracking with stable formation flights (grid, rotating ring, straight line). With realistic numerical simulations we demonstrated that the local broadcast-type communication and the decentralized autonomous control method allows for the scalability of the model for much larger flocks.

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“…Numerous research studies have investigated the attitude and position controllers' design and implementation, such as Proportional integral derivative (PID) and Linear quadratic (LQ) techniques, [5][6][7] Lyapunov-based control, 8,9 sliding mode control, 10,11 backstepping control, 10 integral backstepping, 12 fuzzy logic control, 13 and visual-based techniques. 14 The importance of considering the model uncertainties and external disturbances at the controller design stage has been noted by many researchers.…”

Section: Introductionmentioning

confidence: 99%

“…4 A specific kind of UAVs, the quadrotors, have attracted a lot of attention in the contemporary robotics because of their two main advantages over other vertical takeoff and landing UAVs, namely their mechanical simplicity and minimum damage in case of collision. 5 As a result of this popularity, a broad range of control techniques have been designed and evaluated to improve the flight performance of quadrotors.…”

Section: Introductionmentioning

confidence: 99%

Attitude and position controller design and implementation for a quadrotor

Mardan

Esfandiari

Sepehri

2017

International Journal of Advanced Robotic Systems

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The performance of quadrotors can be significantly disturbed in the presence of wind. In this article, a simple-toimplement attitude controller is proposed to render a robust and accurate trajectory tracking in the presence of disturbance and model uncertainties. The attitude controller design is based on quantitative feedback theory. A fuzzy logic controller is further employed to provide a satisfactory position trajectory tracking for the quadrotor. The performances of the controllers, in terms of disturbance rejection and trajectory tracking, are experimentally studied. Finally, a flight scenario is performed that compares the performance of the designed quantitative feedback theory-fuzzy control scheme with the ArduCopter controller.

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Precision flight control for a multi-vehicle quadrotor helicopter testbed

Cited by 257 publications

References 18 publications

On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments

On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments

Outdoor flocking and formation flight with autonomous aerial robots

Attitude and position controller design and implementation for a quadrotor

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