Positioning Control for an Autonomous Airship

Yang, Yueneng; Wu, Jie; Zheng, Wei

doi:10.2514/1.c033709

Cited by 44 publications

(44 citation statements)

References 19 publications

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“…Control strategy has always been an important subject in airship research. After years of study, airship motion control has gained significant achievements with regard to hovering control, trajectory tracking, and path-following control [2][3][4]. The objective of the hovering control is to maintain the airship in a certain area against the wind, and the primary purpose of the trajectory tracking is to track a time-parameterized desired path, while the path-following control is mainly concerned with tracking a desired path without a specified temporal constraint.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Path-Following Control of a Robotic Airship with Reinforcement Learning

Nie

Zheng

Zhu

2019

International Journal of Aerospace Engineering

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This paper proposed an adaptive three-dimensional (3D) path-following control design for a robotic airship based on reinforcement learning. The airship 3D path-following control is decomposed into the altitude control and the planar path-following control, and the Markov decision process (MDP) models of the control problems are established, in which the scale of the state space is reduced by parameter simplification and coordinate transformation. To ensure the control adaptability without dependence on an accurate airship dynamic model, a Q-Learning algorithm is directly adopted for learning the action policy of actuator commands, and the controller is trained online based on actual motion. A cerebellar model articulation controller (CMAC) neural network is employed for experience generalization to accelerate the training process. Simulation results demonstrate that the proposed controllers can achieve comparable performance to the well-tuned proportion integral differential (PID) controllers and have a more intelligent decision-making ability.

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Section: Introductionmentioning

confidence: 99%

Three-Dimensional Path-Following Control of a Robotic Airship with Reinforcement Learning

Nie

Zheng

Zhu

2019

International Journal of Aerospace Engineering

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“…However, it should be noted that the aforementioned literatures have limitations: (i) Control object of the most valuable research papers focused on the airship controller design is the fully actuated airship, see for instance [9, 10, 17]. However, for most traditional airships, they are underactuated, so these controllers will not work when the control objects are underactuated. (ii) Most of the existing works only focus on the path tracking of airships in the horizontal plane, and inflate and deflated valves are used to adjust the buoyancy of the airship in order to change the flight altitude, see for instance [1, 9, 12].…”

Section: Introductionmentioning

confidence: 99%

Finite‐time spatial path following control for a robotic underactuated airship

Zhou

Yan

et al. 2019

IET Intelligent Trans Sys

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This study presents a finite‐time spatial path following control method for a robotic underactuated airship subject to model uncertainties and external disturbances. A finite‐time path following approach is proposed by combining the backstepping approach with the terminal sliding mode control technique, and the upper bounds of model uncertainties and external disturbances are estimated by the designed adaptive laws. Compared with existing works on the path following control of airships, the algorithm presented in this study can guarantee the airship track a spatial predefined path in finite time in the presence of model uncertainties and external disturbances. Simulations are given to illustrate the effectiveness of the proposed path following control method.

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“…12 But, these control methods are effective only around the equilibrium state. 7 So, nonlinear control approaches are proposed for the airships based on nonlinear models, such as H∞ control, 13 dynamic inversion control, 14 back-stepping approach, 6,15 sliding mode control, 16 and trajectory linearization control. 4 Besides the approaches mentioned above, intelligent control methods including fuzzy control 17 and neural network control 18 have also been applied to the airships successfully.…”

Section: Introductionmentioning

confidence: 99%

“…In this way, Yang et al. 6 proposed a backstepping sliding mode controller for ZY-01 airship in the presence of wind; the other one is to cope up with the wind in kinematic model. Zheng et al.…”

Section: Introductionmentioning

confidence: 99%

“…The dynamics model of the airship can be regarded as a strict-feedback system, and it is very convenient and effective to cope with this kind of system by using backstepping method. 6 But analytic calculation of the command derivatives is required, when backstepping technique is applied. And the analytic command derivatives will become increasingly complex as the system order increases.…”

Section: Introductionmentioning

confidence: 99%

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Planar path following nonlinear controller design for an autonomous airship

Zhou

Wang

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper investigates the planar path following control problem for an autonomous airship taking actuator saturation, actuator dynamics, parameter variation, and wind field into account. Firstly, improved nonlinear models of the airship are introduced and the control objective is formulated. Then, a command filtered backstepping controller combined with nonlinear disturbance observer is derived based on Lyapunov stability theory. The command filter is adopted to approximate the magnitude and rate saturations of actuator. The first-order inertia term is used to approximate the actuator dynamics. And, the nonlinear disturbance observer is employed to handle the disturbance caused by parameter variations and wind field. To guarantee the controller still works when saturations occur, the compensation terms are also designed. It is proved that the proposed controller can drive the airship to track a predefined path under actuator saturation, parameter variations and wind field. Besides, the proposed approach is able to avoid the complex analytic computation of command derivatives which is required in traditional backstepping method. Finally, simulations are carried out to illustrate effectiveness of the proposed controller.

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Positioning Control for an Autonomous Airship

Cited by 44 publications

References 19 publications

Three-Dimensional Path-Following Control of a Robotic Airship with Reinforcement Learning

Three-Dimensional Path-Following Control of a Robotic Airship with Reinforcement Learning

Finite‐time spatial path following control for a robotic underactuated airship

Planar path following nonlinear controller design for an autonomous airship

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