Design, Implementation, and Evaluation of a Neural-Network-Based Quadcopter UAV System

A novel adaptive neural network-based fault-tolerant control scheme is proposed for six degree-of-freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate actuators and sensors’ faults in real time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter’s dynamic model to detect faults in the actuators and navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter actuators, and the helicopter tracks the desired trajectory without any interruption.

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“…In the helicopter control system, the six DoF controller model can be formulated as [32] x = f (x,ẋ, κ) (24)…”

Section: First Feedback Controller: Nonlinear Dynamic Inversionmentioning

confidence: 99%

Neural Network-Based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults

Mokhtari

Abbaspour²,

Yen

et al. 2021

Remote Sensing

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show abstract

“…Hence, the state dynamics should be controlled by inner and outer control loops that have faster and slower responses, respectively. In the helicopter control system, the six DoF controller model can be formulated as [32] x = f (x,ẋ, κ) (24) κ =f −1 (x,ẋ,ẍ)…”

Section: First Feedback Controller: Nonlinear Dynamic Inversionmentioning

confidence: 99%

Neural Network-based Active Fault-Tolerant Control Design for Unmanned Helicopter with Sensor Faults

Mokhtari¹,

Abbaspour²,

Yen³

et al. 2021

Preprint

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A novel adaptive neural network-based fault-tolerant control scheme is proposed for six-degree freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate sensors' faults in real-time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter's dynamic model to detect faults in the navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real-time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter navigation sensors, and the helicopter tracks the desired trajectory without any interruption.

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“…With the development of artificial intelligence (AI) technology, intelligent control has gained a lot of attention in the field of robot control. In the past decade, various intelligent control methods have been developed for UAV to achieve better performance of flight, for example, fuzzy-logic control [18], neural network [19] and reinforcement learning [20].…”

Section: Introductionmentioning

confidence: 99%

Backstepping Control of an Unmanned Helicopter Subjected to External Disturbance and Model Uncertainty

et al. 2021

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A helicopter is a highly nonlinear system. Its mathematical model is difficult to establish accurately, especially the complicated flapping dynamics. In addition, the forces and moments exerted on the fuselage are very vulnerable to external disturbances like wind gust when flying in the outdoor environment. This paper proposes a composite control scheme which consists of a nonlinear backstepping controller and an extended state observer (ESO) to handle the above problems. The stability of the closed-loop system can be guaranteed based on Lyapunov theory. The external disturbances and model nonlinearities are treated as a lumped disturbance. Meanwhile, the ESO is employed to compensate the influence by estimating the lumped disturbance in real-time. Numerical simulation results are presented to demonstrate that the algorithm can achieve accurate and agile attitude tracking under the external wind gust disturbances even with model uncertainties. When coming to the flight test, a block dropping device was designed to generate a quantifiable and replicable disturbance, and the experimental results indicate that the algorithm introduced above can reject the external disturbance rapidly and track the given attitude command precisely.

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Design, Implementation, and Evaluation of a Neural-Network-Based Quadcopter UAV System

Cited by 93 publications

References 24 publications

Neural Network-Based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults

Neural Network-Based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults

Neural Network-based Active Fault-Tolerant Control Design for Unmanned Helicopter with Sensor Faults

Backstepping Control of an Unmanned Helicopter Subjected to External Disturbance and Model Uncertainty

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