Distributed adaptive output consensus control of second-order systems containing unknown non-linear control gains

Wang, Gang; Wang, Chaoli; Du, Qinghui; Cai, Xuan

doi:10.1080/00207721.2016.1139760

Cited by 17 publications

(4 citation statements)

References 35 publications

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“…The application of these technologies in sports can provide detailed basic data and classified advanced data for athletes and coaches and comprehensively show the advantages and disadvantages of athletes in the competition [ 5 , 6 ] so that athletes find their own shortcomings and problems and further targeted training. In addition, these data statistics can also predict the results of the competition according to the relevant data of athletes or analyze the changes of competition tactics [ 7 – 9 ]. These detailed data results are generated on the basis of strong computing power, so in the early application of sports data statistics, the data category is less, mainly relying on manual statistics for data processing.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

Zhang

2021

Computational Intelligence and Neuroscience

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In the system design of table tennis robot, the important influencing factors of automatic detection of technical and tactical indicators for table tennis are table tennis rotation state, trajectory, and rebound force. But the general prediction algorithm cannot process the time series data and give the corresponding rotation state. Therefore, this paper studies the automatic detection method of technical and tactical indicators for table tennis based on the trajectory prediction using the compensation fuzzy neural network. In this paper, the compensation fuzzy neural network algorithm which combines the compensation fuzzy algorithm and recurrent neural network is selected to construct the automatic detection of technical and tactical indicators for table tennis. The experimental results show that the convergence time of the compensation fuzzy neural network is shorter, the training time is shortened, and the prediction accuracy is improved. At the same time, in terms of performance testing, the model can accurately distinguish the influence of table tennis rotation state and rebound on table tennis motion estimation, so as to improve the accuracy of motion trajectory prediction. In addition, the accuracy of trajectory prediction will be improved with the increase of input data. When the number of data reaches 30, the trajectory prediction error is within the actual acceptable error range.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

Zhang

2021

Computational Intelligence and Neuroscience

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“…The proof can be found in Hou et al (2009), Ge et al (2013) and Wang, Wang, Du, and Cai (2016), and it is omitted here for clarity and conciseness.…”

Section: Proofmentioning

confidence: 94%

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Wang

Yan

et al. 2016

International Journal of Systems Science

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This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.

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“…

Q_{i} ()(, u_{i})

is in the set

ϝ_{i} = (}{, 0, \pm u_{i, h}, \pm u_{i, h} ()(, 1 + δ_{i}), h = 1, 2, \dots)

. Remark 1 Different from [43], gains of

x_{i, r + 1}

are unknown functions with

r = 1, \dots, n_{i}

, and

x_{i, n_{i} + 1} = Q_{i} ()(, u_{i})

.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Cooperative adaptive finite‐time control for stochastic multi‐agent systems with input quantisation

Zhang

Sun

et al. 2019

IET Control Theory & Applications

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Distributed adaptive output consensus control of second-order systems containing unknown non-linear control gains

Cited by 17 publications

References 35 publications

[Retracted] Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

[Retracted] Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Cooperative adaptive finite‐time control for stochastic multi‐agent systems with input quantisation

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