Nonlinear Robust Tracking Control of a Quadrotor <scp>UAV</scp> on <scp>SE</scp>(3)

Lee, Taeyoung; Leok, Melvin; McClamroch, N. Harris

doi:10.1002/asjc.567

Cited by 259 publications

(222 citation statements)

References 30 publications

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“…By expressing the control equations in a coordinate-free, singularity-free manner, this controller can guarantee almost global exponential attractiveness (23), and in practice, it can control the quadrotor at almost inverted orientations (24). Yu et al (25) presented a similar formulation but decomposed the attitude controller to track the fast-response roll and pitch angles before the slow-response yaw angle, and Brescianini et al (26) presented a quaternion-based controller that also has singularity-free, globally asymptotic stability properties.…”

Section: Dynamics and Controlmentioning

confidence: 99%

Autonomous Flight

Tang

Kumar

2018

Annu. Rev. Control Robot. Auton. Syst.

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This review surveys the current state of the art in the development of unmanned aerial vehicles, focusing on algorithms for quadrotors. Tremendous progress has been made across both industry and academia, and full vehicle autonomy is now well within reach. We begin by presenting recent successes in control, estimation, and trajectory planning that have enabled agile, highspeed flight using low-cost onboard sensors. We then examine new research trends in learning and multirobot systems and conclude with a discussion of open challenges and directions for future research. 6.1

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Section: Dynamics and Controlmentioning

confidence: 99%

Autonomous Flight

Tang

Kumar

2018

Annu. Rev. Control Robot. Auton. Syst.

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“…Basically, the tracking controller design must consider disturbances and uncertainties. Several methods have been proposed, [20][21][22][23][24] including a geometric nonlinear controller and a nonlinear robust controller. However, the main purpose of this paper is to validate the proposed MDMOC trajectory generation method.…”

Section: Trajectory Tracking Controllermentioning

confidence: 99%

“…Both optimal trajectories were smooth and continuous under boundary conditions and path constraints. In particular, q(t) started from the initial position (1,1,1) and moved to the terminal position (22,25,20), satisfying waypoint-1, waypoint-2, and the no-fly zone ( Figure 6) constraints in minimum time T f = 32.0373 seconds as required by MDMOC. The computing time was 2.42 seconds, and the number of iterations was 632.…”

Section: Trajectory Generationmentioning

confidence: 99%

A multiphase DMOC‐based trajectory optimization method

Zhang

Wang

Inanc

2017

Optim Control Appl Methods

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SummaryDiscrete mechanics and optimal control (DMOC) is a methodology that takes advantage of variational structure to solve certain optimal control problems for mechanical systems. This paper proposes to combine a multiphase strategy with the original DMOC method, resulting in a new multiphase DMOC (MDMOC) method and making optimal trajectory generation more efficient. The advantages of the proposed method are demonstrated mathematically, and in addition, a quadrotor, unmanned aerial vehicle, simulation example is presented to show its superiority over the DMOC method. Furthermore, to show its potential application, an arbitrarily chosen controller was used to track the desired trajectory generated by MDMOC. This new MDMOC methodology can also be applied to other mechanical systems such as mobile robots and underwater gliders.KEYWORDS discrete mechanics and optimal control, optimal control applications, quadrotor, trajectory optimization | INTRODUCTIONRobotic vehicles are becoming increasingly popular because of their great flexibility and mobility. They are widely used in military, civilian, commercial, and many other fields. Traditionally, robotic vehicles can be categorized into 4 main types: underwater robots, land mobile robots, unmanned aircraft, and space robots. Recently, small unmanned aerial vehicles (UAVs), as a kind of self-propelled aerial robot, have attracted widespread research attention because of their potential applications, including reconnaissance, surveillance, search and rescue, and environmental monitoring.In practice, it is always preferable for UAVs to generate a real-time trajectory, which is the foundation of reconnaissance, exploration, or tracking missions. However, due to their limited payload, it is essential to plan the optimal trajectory to achieve minimum time and/or minimum energy objectives.1 Therefore, an efficient trajectory optimization method and corresponding tracking controller design are indispensable. Generally, trajectory optimization can be considered as an optimal control problem. 2 Many kinds of optimization algorithm have been proposed for different systems. Betts 3 presented a survey showing that numerical methods for solving optimal trajectory problems can normally be classified into 2 categories: indirect and direct. Direct methods have been widely used by transforming a continuous optimal control problem into a nonlinear programming (NLP) problem. 4 Bouktir et al 5 presented a numerical method to generate a minimum-time optimal trajectory for a quadrotor with nonlinear under-actuated properties; Ross et al 2 proposed a guess-free spectral algorithm with several limiting assumptions in which the problem must be reasonably bounded and scaled; Chamseddine et al 6 applied a planning/replanning strategy to a quadrotor and illustrated the simplicity and efficiency of the method. All these approaches can solve the specified problems with relative efficiency. However, they are applicable only to trajectory optimization problems for certain specific simple mechanical...

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“…In recent papers [3], [4], [5] the use of a hierarchical control structure has been treated, exploiting the fact that there exists a time scale separation between MAV translational and rotational dynamics, latter being the fast dynamics of the system. However, the vast majority of the works only make mention of this feature, and do not address the problem in a theoretical point of view, using tools like the singular perturbation theory.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have exploited the aforementioned hierarchical structure [1], [3], [4], [5], [6], [7], but they use the time scale separation just to justify the implementation of two This work was partially supported by the Institute for Science & Technology of Mexico City (ICyTDF).…”

Section: Introductionmentioning

confidence: 99%

Lyapunov-based controller using singular perturbation theory: An application on a mini-UAV

Flores

Lozano

2013

2013 American Control Conference

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Abstract-In this paper, a Lyapunov-based control using singular perturbation theory is proposed and applied on dynamics of a miniature unmanned aerial vehicle (MAV). Such controller is designed taking into account the presence of the small parameter ǫ on vehicle dynamics, causing a time-scale separation between the attitude and translational dynamics of the MAV. The stability analysis is demonstrated by presenting a scenario in which the time-scale property arises on the the MAV dynamics. In addition, the values of the parameter ǫ for which the control law is validated, are given. Simulations are derived an presented to demonstrate the effectiveness of the control law. The proposed controller has been applied to a Quad-plane MAV experimental platform, in order to validate the performance and to show the time-scale property.

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Nonlinear Robust Tracking Control of a Quadrotor UAV on SE(3)

Cited by 259 publications

References 30 publications

Autonomous Flight

Autonomous Flight

A multiphase DMOC‐based trajectory optimization method

Lyapunov-based controller using singular perturbation theory: An application on a mini-UAV

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