2017
DOI: 10.3390/en10122127
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Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Abstract: Based on Lyapunov theory, this research demonstrates the stability of the sliding surface in the consensus problem of multi-agent systems. Each agent in this system is represented by the dynamically uncertain robot, unstructured disturbances, and nonlinear friction, especially when the dynamic function of agent is unknown. All system states use neural network online weight tuning algorithms to compensate for the disturbance and uncertainty. Each agent in the system has a different position, and their trajector… Show more

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Cited by 4 publications
(7 citation statements)
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“…After making the general equations, we will add these external forces. In (19), (̇) is the composition of friction on the two wheels. It includes FL, FR -friction force impact on the left-wheel and right-wheel; -the external force impacts the two wheels.…”
Section: Building the Neural Network To Compensate For Disturbancementioning
confidence: 99%
See 1 more Smart Citation
“…After making the general equations, we will add these external forces. In (19), (̇) is the composition of friction on the two wheels. It includes FL, FR -friction force impact on the left-wheel and right-wheel; -the external force impacts the two wheels.…”
Section: Building the Neural Network To Compensate For Disturbancementioning
confidence: 99%
“…However, with variability and uncertainty disturbance, the ability to reject interference is still low. To overcome those disadvantage problems mentioned above, in this study, the authors will develop from experiences in building control systems and noise compensation in previous articles [17]- [19] for the construction of the controller of TWSB robot with a nonlinear dynamic structure and uncertainty disturbance. This study's main contributions include designing a sliding mode controller combined with adaptive neural networks with online self-aligning parameters to eliminate the noise and uncertain components in the TWSB robot with nonlinear kinetics and determination of the uncertainty parameters.…”
Section: Introductionmentioning
confidence: 99%
“…The neural network with three layers A number of neural network control architectures have been proposed [13]- [15]. In this research, the author will use the Model Reference Control Architecture [16], [17] for controlling the driver circuit of DC motor. The control architecture is shown in the Figure 7.…”
Section: The Model Reference Control System Using Neural Networkmentioning
confidence: 99%
“…Furthermore, resilient flocking in time-dependent networks formed by mobile robot teams has been investigated in [22] and resilient opinion consensus in mobile social networks has been explored in [23]. It is worth mentioning that resilience to adversaries is conceptually distinct from resilience to disturbance or noise [24], and the methods used are very different.Motivated by the aforementioned works, we in this paper consider the resilient multiscale coordination control for multi-agent systems in the presence of adversarial (faulty or malicious) nodes. The scope of the adversarial nodes is assumed to be either bounded by a constant in the whole network, which will be referred to as the globally bounded model, or by a constant in the neighborhood of each cooperative node, which will be referred to as the locally bounded model.…”
mentioning
confidence: 99%
“…Furthermore, resilient flocking in time-dependent networks formed by mobile robot teams has been investigated in [22] and resilient opinion consensus in mobile social networks has been explored in [23]. It is worth mentioning that resilience to adversaries is conceptually distinct from resilience to disturbance or noise [24], and the methods used are very different.…”
mentioning
confidence: 99%