Adaptive time-varying formation tracking control of unmanned aerial vehicles with quantized input

Wang, Yüan; He, Lei; Huang, Changqiang

doi:10.1016/j.isatra.2018.09.013

Cited by 42 publications

(19 citation statements)

References 28 publications

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“…Neural networks and fuzzy logic systems have been employed to compensate for unknown nonparametric uncertainties in approximation‐based adaptive containment control schemes of nonlinear multi‐agent systems in strict‐feedback 30‐33 and pure‐feedback forms 34,35 . To increase the efficiency of signal transmission under capacity‐limited networks, quantization‐based distributed cooperative control problems have been investigated for uncertain nonlinear multi‐agent systems 36‐41 . These results have been extended to lower‐triangular nonlinear multi‐agent systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Kim

Yoo

2022

Intl J Robust & Nonlinear

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This article presents a distributed adaptive containment control strategy using quantized state feedback and communication for uncertain multiple‐input‐multiple‐output pure‐feedback nonlinear multi‐agent systems with state quantization. It is assumed that quantized state variables of multiple followers and quantized outputs of multiple dynamic leaders are only available for constructing local adaptive feedback controllers of followers under a directed network. Compared with existing containment control approaches, the primary contribution of this study is dealing with discontinuously quantized state feedback and communication problems for containment control design in the presence of unknown nonaffine nonlinearities. Based on quantized state and communication information, local adaptive neural network trackers of followers and their adaptive tuning laws are developed to guarantee that the trajectories of all followers converge to the dynamic convex hull spanned by the multiple leaders regardless of unknown pure‐feedback nonlinearities. By analyzing the stability of the local quantization errors between quantized signals and original signals, we prove the boundedness of all closed‐loop signals and the convergence of the containment errors to a sufficiently small domain around the origin. Simulation examples involving flexible‐joint manipulators are presented to validate the proposed quantized feedback scheme.

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Section: Introductionmentioning

confidence: 99%

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Kim

Yoo

2022

Intl J Robust & Nonlinear

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“…Therefore, the burden of communication will be significantly reduced by virtue of the quantization mechanisms. In the last decade, quantized control in connection with robust approaches has attracted increasing amounts of attention from researchers and has been studied in the fields of spacecraft formation [24,25], unmanned aerial vehicles [26,27], and underwater vehicles [28]. However, to the best of the author's knowledge, developing controllers for USVs with quantized transmitted information remains an open problem.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to input quantization, another important issue that deserves further investigation is the state constraint control of USVs, which has been ignored in numerous studies [7,8,11,16,17,[25][26][27][28]. In practical applications, it is realistic and of great significance to consider that the USV position error should be limited strictly by both sides of the feasible channel to ensure the navigation safety of the vessel [6].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control for Underactuated USV with Prescribed Performance and Input Quantization

Jiang

Mao

et al. 2021

Symmetry

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This paper is devoted to the problem of prescribed performance trajectory tracking control for symmetrical underactuated unmanned surface vessels (USVs) in the presence of model uncertainties and input quantization. By combining backstepping filter mechanisms and adaptive algorithms, two robust control architectures are investigated for surge motion and yaw motion. To guarantee the prespecified performance requirements for position tracking control, the constrained error dynamics are transformed to unconstrained ones by virtue of a tangent-type nonlinear mapping function. On the other hand, the inaccurate model can be identified through radial basis neural networks (RBFNNs), where the minimum learning parameter (MLP) algorithm is employed with a low computational complexity. Furthermore, quantization errors can be effectively reduced even when the parameters of the quantizer remain unavailable to designers. Finally, the effectiveness of the proposed controllers is verified via theoretical analyses and numerical simulations.

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“…Adaptive formation control of multi unmanned aerial vehicles (UAVs) with time-varying is proposed by Wang et al [10]. The robust back-stepping controller utilized to stabilizing the UAV nonlinear model.…”

Section: Introductionmentioning

confidence: 99%

Formation Control of Multi Unmanned Aerial Vehicle Systems Based on DDS Middleware

2020

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In this paper formation control of Unmanned Aerial Vehicle System (UAVs) is introduced. L 1 controller with potential field technique and Data Distribution Service (DDS) middleware used for the navigation of the agents, which is multi-UAVs. L1 adaptive controller used to stabilizing the general equations of motion of each UAVs and the potential field technique used to formalize the followers around the leader. The exchanging data between the leader and the followers done through publisher/subscriber DDS middleware. This L 1 controller has a high performance coming from a robust adaptation of it. Robustness of the L 1 controller verified using Matlab Simulation. The Lyapunov method provided the analysis and stability of the framework of UAVs.

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Adaptive time-varying formation tracking control of unmanned aerial vehicles with quantized input

Cited by 42 publications

References 28 publications

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Trajectory Tracking Control for Underactuated USV with Prescribed Performance and Input Quantization

Formation Control of Multi Unmanned Aerial Vehicle Systems Based on DDS Middleware

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