Neural Adaptive Distributed Formation Control of Nonlinear Multi-UAVs With Unmodeled Dynamics

Yu, Yajing; Guo, Jingjie; Ahn, Choon Ki; Xiang, Zhengrong

doi:10.1109/tnnls.2022.3157079

Cited by 66 publications

(20 citation statements)

References 38 publications

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“…Yu et al [42] investigates the problem of neural adaptive distributed formation control for quad rotor multiple UAVs subject to unmodeled dynamics and disturbance. Saengphet et al [43] uses the input and output data obtained from a flight mission of a tailless UAV for SISO mathematical model using frequency response.…”

Section: Related Workmentioning

confidence: 99%

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

et al. 2022

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Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work.

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Section: Related Workmentioning

confidence: 99%

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

et al. 2022

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“…A e B u e (14) By assuming 1d e as the expected value of 1 e , we can describe the error 1 δ as follows: = d  δ ee (16) Choose the Lyapunov function as…”

Section: A E H W Ementioning

confidence: 99%

“…And Jonathan designed formation control strategy for inter-intelligent body damping in order to eliminate the effect caused by interaircraft damping in the literature [13] and implemented hardware-in-the-loop formation control simulation. To further eliminate the interference of unmodeled dynamics for the system and improve the formation tracking performance, [14] designed neural adaptive sliding mode control controller to implement formation control.…”

Section: Introductionmentioning

confidence: 99%

“…1) Although the formation control problem of multiple UAVs was studied in [6], [12], [14] , the state information of UAVs can all be transmitted by a stable communication link, and the formation control designed in this paper is able to achieve formation control based on vision only.…”

Section: Introductionmentioning

confidence: 99%

“…2) Unlike [14], [23][24][25][26] which use neural networks to estimate model uncertainty or solve the control signal, the paper uses the backstepping control method to design the control law and uses ant colony algorithm to solve it in the prediction time domain, which effectively reduces the computational burden and solving time.…”

Section: Introductionmentioning

confidence: 99%

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UAV Formation Control Under Communication Constraints Based on Distributed Model Predictive Control

Chen¹,

Jin²,

Wang

et al. 2022

IEEE Access

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In this paper, an anti-disturbance control for UAVs formations in a three-dimensional environment using relative information is proposed. To accurately describe the UAV cluster motion error in a three-dimensional environment, an error estimation model based on the UAV model is established to realize the UAV state estimation based on relative information. The control law to achieve the stability of the UAV formation under bounded perturbation is also derived using the backstepping control strategy. Next, to realize that the UAV formation can achieve optimal control under performance constraints, an algorithm based on model predictive control is proposed, and an ant colony algorithm is used to accelerate the solution process. Then, the stability of the UAV formation under the action of this algorithm is investigated. Finally, the superiority and effectiveness of the algorithm compared with the traditional backstepping control method are verified through comparative simulations.

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Finite‐time adaptive quantized control of stochastic nonstrict‐feedback constrained nonlinear systems with multiple unmodeled dynamics

Chen

Luan

Liu

2023

Adaptive Control & Signal

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In this article, a finite-time adaptive quantized dynamic surface control problem is solved for stochastic partially nonaffine nonstrict-feedback constrained nonlinear systems with multiple unmodeled dynamics. Firstly, a variable energy function is built to handle the stochastic state unmodeled dynamics under nonstrict-feedback structure, and a normalized signal is employed to process the input unmodeled dynamics. Furthermore, a specific transformation technique is introduced to keep all states in a predefined asymmetric dynamic constrained region. Compared with the existing results, it not only avoids the circular argument, and also relaxes the condition of the constraint function. Then, the nonlinear function is approximated with the linearly parameterized neural networks. Subsequently, in order to make the tracking error reach a steady state in the finite time, with the help of the properties of the Gaussian function and dynamic surface control technique, a novel finite-time adaptive quantized controller is designed to ensure that all error signals are semi-globally practical finite-time stable and all states obey stochastic probabilistic constraints.Numerical simulation examples are provided to verify the theoretical results obtained.

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Neural Adaptive Distributed Formation Control of Nonlinear Multi-UAVs With Unmodeled Dynamics

Cited by 66 publications

References 38 publications

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

UAV Formation Control Under Communication Constraints Based on Distributed Model Predictive Control

Finite‐time adaptive quantized control of stochastic nonstrict‐feedback constrained nonlinear systems with multiple unmodeled dynamics

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