Neural-network-based distributed adaptive asymptotically consensus tracking control for nonlinear multiagent systems with input quantization and actuator faults

Yu, Li; Wang, Chaoli; Cai, Xuan; Li, Lin; Wang, Gang

doi:10.1016/j.neucom.2019.04.018

Cited by 29 publications

(21 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The leader UUV assumes that it has been controlled to follow the trajectory with the way-points (0, 6), (0, 12), (30,12), (30,6). The position of the obstacle is (15,9) and D out = 7 m, d in = 4 m. Figure 5 shows the distances among three BlueROV-1 drones in case of using collision avoidance terms compared to those in none of the collision avoidance term. We first examine the formation control without a collision avoidance term.…”

Section: Methodsmentioning

confidence: 99%

“…where Γ ic is a positive constant, and P is the positive definite solution based on the algebraic Riccati equation A P + PA + Q − PBB P = 0 (15) where Q ∈ R 6×6 is a positive-definite matrix.…”

Section: Formation Control Termmentioning

confidence: 99%

“…By designing neural network torque controllers with on-line learning, the robust velocity tracking and the desired formation is achieved and guaranteed. In [15], the input quantization, actuator faults, and unknown nonlinear of a consensus tracking control of multi-agent systems are studied under directed communication topology. This work has introduced smooth functions to compensate for the effect of quantization and bounded stuck faults.…”

Section: Introductionmentioning

confidence: 99%

“…The number of input values for neural network is 6. The neural network activation function is chosen as a log-sigmoid of the form φ(x i ) = 1/(1 + e −κt ), where κ is a positive constant (κ = 2 in this work).From Equations(10),(15), and (32), a solution P of the Riccati equation with Q = diag{1, 1, 1, 1, 1, 1} is P =…”

mentioning

confidence: 99%

See 3 more Smart Citations

Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments

et al. 2020

View full text Add to dashboard Cite

This paper addresses a formation tracking problem of multiple low-cost underwater drones by implementing distributed adaptive neural network control (DANNC). It is based on a leader-follower architecture to operate in hazardous environments. First, unknown parameters of underwater vehicle dynamics, which are important requirements for real-world applications, are approximated by a neural network using a radial basis function. More specifically, those parameters are only calculated by local information, which can be obtained by an on-board camera without using an external positioning system. Secondly, a potential function is employed to ensure there is no collision between the underwater drones. We then propose a desired configuration of a group of unmanned underwater vehicles (UUVs) as a time-variant function so that they can quickly change their shape between them to facilitate the crossing in a narrow area. Finally, three UUVs, based on a robot operating system (ROS) platform, are used to emphasize the realistic low-cost aspect of underwater drones. The proposed approach is validated by evaluating in different experimental scenarios.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Formation Control Termmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In addition to nonlinear systems, a neural-network-based leaderless consensus control problem of fractional-order multi-agent systems (FOMASs) with unknown nonlinearities and unknown external disturbances was reported 8 . The effect of actuator fault on consensus asymptotic convergence of nonlinear agents with unknown dynamics was discussed by Li et al 9 . Other examples include event-triggered consensus control problem for nonstrict-feedback nonlinear systems with a dynamic leader 10 , fixed-time leader-follower consensus problem for multi-agent systems (MASs) with output constraints, unknown control direction, unknown system dynamics, an unknown external disturbance 11 , stochastic nonlinear multi-agent systems with input saturation 12 etc.…”

Section: Introductionmentioning

confidence: 99%

Neuro-adaptive augmented distributed nonlinear dynamic inversion for consensus of nonlinear agents with unknown external disturbance

Mondal

Tsourdos

2022

Sci Rep

View full text Add to dashboard Cite

This paper presents a novel neuro-adaptive augmented distributed nonlinear dynamic inversion (N-DNDI) controller for consensus of nonlinear multi-agent systems in the presence of unknown external disturbance. N-DNDI is a blending of neural network and distributed nonlinear dynamic inversion (DNDI), a new consensus control technique that inherits the features of Nonlinear Dynamic Inversion (NDI) and is capable of handling the unknown external disturbance. The implementation of NDI based consensus control along with neural networks is unique in the context of multi-agent consensus. The mathematical details provided in this paper show the solid theoretical base, and simulation results prove the effectiveness of the proposed scheme.

show abstract

Decentralized adaptive output feedback dynamic surface control for stochastic nonstrict‐feedback interconnected nonlinear systems with actuator failures and input quantization via command filter

Zhang

Tang

Xia

et al. 2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this article, a new decentralized adaptive neural output feedback control scheme based on first‐order command filter is proposed for stochastic nonstrict‐feedback interconnected systems with prescribed performance, input quantization, actuator failures and unmodeled dynamics. The unknown smooth functions are eliminated by using the radial basis function neural networks. The immeasurable states in the system are estimated using the decentralized K‐filters, unmodeled dynamics is processed using dynamic signal, and the hyperbolic tangent function is applied to the construction of the prescribed performance function. The hysteretic quantizer and actuator failure are denoted in the form of linearization, and a smoothing function is introduced to compensate for the effects of quantization and bounded stuck faults. Based on the dynamic surface control (DSC) method and using the properties of Gaussian function to deal with stochastic nonstrict‐feedback interconnected systems, the first‐order command filter is used to replace the first‐order filter in the traditional DSC to eliminate the influence of filtering error on the systems, and an error compensation signal is introduced at the recursive each step of the design to construct a new error dynamic surface, which simplifies the derivation process and the design of the controller. Finally, the Lyapunov method is used to prove that all the signals in the whole controlled system are semiglobally uniformly ultimately bounded in probability and the tracking error is within the time‐varying constraint. The effectiveness of the proposed decentralized adaptive control method is verified by a numerical simulation and an example simulation.

show abstract

Neural-network-based distributed adaptive asymptotically consensus tracking control for nonlinear multiagent systems with input quantization and actuator faults

Cited by 29 publications

References 30 publications

Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments

Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments

Neuro-adaptive augmented distributed nonlinear dynamic inversion for consensus of nonlinear agents with unknown external disturbance

Decentralized adaptive output feedback dynamic surface control for stochastic nonstrict‐feedback interconnected nonlinear systems with actuator failures and input quantization via command filter

Contact Info

Product

Resources

About