Formal Model of Collaborative Fault Tolerant Planning in Multi-Robotic Systems

Troubitsynå, Elena

doi:10.1109/codit55151.2022.9804073

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…In Equation (33), ρ il is defined in Equation ( 22), and Equation ( 31) can be written as Equation (34) by utilizing the direct neural network robust adaptive fault-tolerance law Equation (19) and control gain Equation (27).…”

Section: Robust Adaptive Fault-tolerant Controller Design For Multipl...mentioning

confidence: 99%

“…In this paper, MATLAB software is used for simulation verification and analysis to simulate and test the performance of the controller designed in this paper, and the following initial conditions are given in reference [32][33][34]. The overall simulation time is 50 s. Considering an extensive system composed of two identical air-ground integrated dynamic subsystems, the relevant parameters are defined in Equation (52).…”

Section: Simulation Analysismentioning

confidence: 99%

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Symmetric Collaborative Fault-Tolerant Control of Multi-Intelligence under Long-Range Transmission in Air–Ground Integrated Wireless High-Mobility Self-Organizing Networks

Wang,

Shao,

et al. 2024

Symmetry

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With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system’s fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%.

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