Adaptive backstepping integral control of a small‐scale helicopter for airdrop missions

Lee, Chi-Tai; Tsai, Ching‐Chih

doi:10.1002/asjc.211

Cited by 37 publications

(27 citation statements)

References 19 publications

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“…||a|| ≤ a m (18) The constraint (16) represents the description of soft obstacles, for example, it can depict the requirement of a UAV's survivability. The constraints are rooted in the property limitations of the UAV itself, and are satisfied at every time step k all through the planning.…”

Section: Constraintsmentioning

confidence: 99%

UAV Path Planning in Mixed‐Obstacle Environment via Artificial Potential Field Method Improved by Additional Control Force

Luo¹,

Yu²,

Mei³

et al. 2014

Asian Journal of Control

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This paper deals with a UAV path planning problem in the environment where both solid obstacles and soft obstacles exist. The artificial potential field approach is updated by introducing an additional control force and integrating it with the concept of receding horizon control for UAV trajectory optimization. The original problem is converted into a multi-objective optimization problem by regarding the involved additional control term as the optimization variable. Seeing as the establishment of an additional control force of soft obstacles is dependent on the probability of certain specifications such as survivability, the additional control term accomplishes the description of the specified property index better than those that have been considered in the past, such as distance and control energy. The optimal solution to the path planning problem, in terms of the additional control force method, and the computational efficiency according to the receding horizon control, both contribute to the proposed algorithm. Meanwhile, the proposed method is able to protect the UAV from local minima of additional control force. The numerical examples verify the advantages of this approach.

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Section: Constraintsmentioning

confidence: 99%

UAV Path Planning in Mixed‐Obstacle Environment via Artificial Potential Field Method Improved by Additional Control Force

Luo¹,

Yu²,

Mei³

et al. 2014

Asian Journal of Control

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show abstract

“…With the flapping and pitching progress taken into account, the backstepping was utilized in [2] to achieve the landing of a helicopter. In [3], the backstepping was applied to improve the trajectory tracking capability. In [4], a filtered backstepping controller was designed to realize the bounded tracking with the state constraint.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], a filtered backstepping controller was designed to realize the bounded tracking with the state constraint. However, the controllers in [3,4] both required a dynamical expansion of the control, which may violate the thrust constraint.…”

Section: Introductionmentioning

confidence: 99%

Adaptive backstepping trajectory tracking controller for a miniature helicopter

Zou

2016

Adaptive Control & Signal

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An adaptive backstepping controller is proposed to achieve the asymptotically stable trajectory tracking for a miniature helicopter. The helicopter model is firstly decomposed into a cascaded structure between the position and attitude loops with unmodeled dynamics. The backstepping technique is applied to exploit this cascaded structure and to streamline the controller design procedure. Hyperbolic tangent functions, instead of traditional sign functions, are adopted to complete adaptive algorithms for adjusting the upper bounds of the unmodeled dynamics. Further, the controller introduces an auxiliary dynamic system to ensure the thrust constraint and to obviate singularity during the command attitude extraction. The stability analysis demonstrates that, the asymptotical stability of the auxiliary dynamic system warrants the asymptotically stable tracking of the miniature helicopter system. Simulations verify the theoretical results and analyze the tracking performance.

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“…Ahmed et al [16] developed it in a complete nonlinear helicopter model. Lee et al [17][18] further developed it for a helicopter with airdrop missions by adding an adaptive law or a radial basis function neural network (RBFNN) law. Bouabdallah et al [19] and Fang et al [20] used the integral backstepping method to improve the trajectory tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Robust Tracking Control of Helicopters Using Backstepping with Disturbance Observers

He¹,

Pei²,

Sun³

2013

Asian Journal of Control

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This paper addresses the robust and accurate trajectory tracking problem for unmanned helicopters in the presence of model uncertainties and external disturbances. First, the helicopter's model is simplified to a six-degrees-of-freedom rigid body augmented with a simplified rotor dynamic model, with the model uncertainties and the external disturbances being treated as lumped unknown disturbances. Second, a nonlinear disturbance observer is designed to estimate this lumped disturbance. Then, a backstepping controller with disturbance compensation is designed to ensure robust and highly trajectory tracking. After that, the theoretical analysis of the efficiency of the designed disturbance observer-based backstepping controller (Backstepping+DO) is shown by the Lyapunov theory. Finally, simulation results and conclusions are presented and discussed.

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Adaptive backstepping integral control of a small‐scale helicopter for airdrop missions

Cited by 37 publications

References 19 publications

UAV Path Planning in Mixed‐Obstacle Environment via Artificial Potential Field Method Improved by Additional Control Force

UAV Path Planning in Mixed‐Obstacle Environment via Artificial Potential Field Method Improved by Additional Control Force

Adaptive backstepping trajectory tracking controller for a miniature helicopter

Robust Tracking Control of Helicopters Using Backstepping with Disturbance Observers

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