Adaptive Flight Control for an Autonomous Unmanned Helicopter

Johnson, Eric N.; Kannan, Suresh K.

doi:10.2514/6.2002-4439

Cited by 139 publications

(86 citation statements)

References 10 publications

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“…With an occasional exception 38 , outdoor autonomous MAVs make extensive use of GPS (c.f. 39,40,41,42 ). In indoor environments, GPS does not function properly, and autonomous flight becomes more difficult to reach.…”

Section: Autonomous Vision-based Flightmentioning

confidence: 99%

Design, Aerodynamics, and Vision-Based Control of the DelFly

Croon

Clercq

Ruijsink

et al. 2009

International Journal of Micro Air Vehicles

313

180

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Light-weight, autonomous ornithopters form a promise to observe places that are too small or too dangerous for humans to enter. In this article, we discuss the DelFly project, in which we follow a top-down approach to ever smaller and more autonomous ornithopters. Top-down signifies that the project always focuses on complete flying systems equipped with camera. We give arguments for the approach by explaining which findings on the DelFly I and DelFly II recently led to the development of the DelFly Micro: a 3.07-gram ornithopter carrying a camera and transmitter onboard. These findings concern the design, aerodynamics, and vision-based control of the DelFly. In addition, we identify main obstacles on the road to fly-sized ornithopters. INTRODUCTIONOne of the goals of research on Micro Air Vehicles (MAVs) is to arrive at fly-sized MAVs that can fly autonomously in complex environments. Such MAVs form a promise for observation tasks in places that are too small or too dangerous for humans to enter. Their small size would allow the MAVs to enter and navigate in narrow spaces, while autonomous flight would allow the MAV to operate at a large distance from its user.The requirements for the MAV described above are legion. For one, it needs to be as light as possible for endurance, while having enough onboard sensors and processing that allow it to navigate autonomously. Moreover, it needs to be able to hover, allowing it to get a "good look" at the object of observation. At the same time, it needs to fly at higher speeds to travel larger distances.It may come as no surprise that in their quest for a fly-sized MAV, researchers draw inspiration from natural systems. For example, flying insects comply with the requirements mentioned above and can thus provide inspiration for solving the engineering problems encountered in the creation of a fly-sized MAV. One of the key properties of systems inspired by flying insects is that they use flapping wing propulsion (they are ornithopters) 1,2,3,4,5,6,7,8,9 . Especially at smaller sizes, this propulsion method produces more lift than fixed wing configurations 10,11,12,13 .Essentially, there are two main approaches to creating small autonomous ornithopters: bottom-up and top-down. In the bottom-up approach, one starts by creating all the tiny parts that are deemed important to a fly-sized ornithopter 2,14,15 . The most remarkable example of this approach is the work of the Harvard Microrobotics Laboratory. They succeeded in creating a 60 mg robotic insect, which can produce sufficient lift to take off vertically. To achieve this, they made use of Smart Composite Microstructures (SCM) 2 . The robotic insect was still fixed to taut guide wires that restricted the robot to vertical motion and provided both energy and control. In future work, the group plans to allow all degrees of freedom and to incorporate onboard energy supply, sensors, and processing.In the top-down approach, one starts with a fully functioning (relatively large-scale) ornithopter. By studying this ornit...

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Section: Autonomous Vision-based Flightmentioning

confidence: 99%

Design, Aerodynamics, and Vision-Based Control of the DelFly

Croon

Clercq

Ruijsink

et al. 2009

International Journal of Micro Air Vehicles

313

180

View full text Add to dashboard Cite

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“…The airplane is the MURI research airplane with a wingspan of 15.7 (ft) and overall length of 11 (ft). Flight control is achieved using an adaptive neural network-based controller that generates actuator signals based on the navigation system outputs and commanded position, velocity, and attitude [17]. The follower airplane has a camera, whose position, orientation and field of view generates simulated images.…”

Section: Leader's State Estimationmentioning

confidence: 99%

Vision-Based Tracking of Uncooperative Targets

Kannan

Johnson

Watanabe³

et al. 2011

International Journal of Aerospace Engineering

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This paper presents a summary of a subset of the extensive vision-based tracking methods developed at Georgia Tech. The problem of a follower aircraft tracking an uncooperative leader, using vision information only, is addressed. In all the results presented, a single monocular camera is used as the sole source of information used to maintain formation with the leader. A Kalman filter formulation is provided for the case where image processing may be used to estimate leader motion in the image plane. An additional piece of information, the subtended angle, also available from computer vision algorithm is used to improve range estimation accuracy. In situations where subtended angle information is not available, an optimal trajectory is generated that improves range estimation accuracy. Finally, assumptions on the target acceleration are relaxed by augmenting a Kalman Filter with an adaptive element.

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“…where ν ad is the output of the adaptive element in (10). Both the adaptive law in (13) and the concurrent learning adaptive law in (14) were implemented for testing.…”

Section: B Baseline Linear Controller and Adaptive Control Augmentationmentioning

confidence: 99%

Experimental Results of Concurrent Learning Adaptive Controllers

Chowdhary

Wu²,

Cutler³

et al. 2012

AIAA Guidance, Navigation, and Control Conference

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Commonly used Proportional-Integral-Derivative based UAV flight controllers are often seen to provide adequate trajectory-tracking performance only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper suggests the use of adaptive controllers in speeding up the process of extracting good control performance from new UAVs. In particular, it is shown that a concurrent learning adaptive controller improves the trajectory tracking performance of a quadrotor with baseline linear controller directly imported from another quadrotors whose inertial characteristics and throttle mapping are very different. Concurrent learning adaptive control uses specifically selected and online recorded data concurrently with instantaneous data and is capable of guaranteeing tracking error and weight error convergence without requiring persistency of excitation. Flight-test results are presented on indoor quadrotor platforms operated in MIT's RAVEN environment. These results indicate the feasibility of rapidly developing high-performance UAV controllers by using adaptive control to augment a controller transferred from another UAV with similar control assignment structure.

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Adaptive Flight Control for an Autonomous Unmanned Helicopter

Cited by 139 publications

References 10 publications

Design, Aerodynamics, and Vision-Based Control of the DelFly

Design, Aerodynamics, and Vision-Based Control of the DelFly

Vision-Based Tracking of Uncooperative Targets

Experimental Results of Concurrent Learning Adaptive Controllers

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