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首页> 外文期刊>International Journal of Intelligent Systems >A time controlling neural network for time-varying QP solving with application to kinematics of mobile manipulators
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A time controlling neural network for time-varying QP solving with application to kinematics of mobile manipulators

机译:用应用于移动操纵器的运动学时代时变QP解决的神经网络的时间

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摘要

To obtain the solution for time-varying quadratic programming (QP), a time controlling neural network (TCNN) is presented and discussed. The traditional re-current neural networks provide a prospect for real-time calculations and repeatable trajectory control of the mobile manipulators due to its high executing processing and nonlinear disposal ability. However, the convergent time is still a considerable point for the solution of a dynamic system dealing with synchronism and robustness. In this note, a TCNN model by incorporating an initial rectified term is applied to solve the online calculation problems and the convergent time can be controlled in advance. Theoretical analyses on stability, prespecified time and convergence are rigorously clarified. Finally, effectiveness and precision of the TCNN model for the solution of a QP example have been verified. In addition, a repetitive trajectory planning for a three-wheel manipulator is introduced to demonstrate the superiority of the TCNN.
机译:为了获得用于时变二次编程(QP)的解决方案,呈现并讨论了控制神经网络(TCNN)的时间。传统的重新流神经网络由于其高执行处理和非线性处理能力而提供了移动操纵器的实时计算和可重复轨迹控制的前景。然而,收敛时间仍然是处理处理同步和鲁棒性的动态系统的相当值。在本说明中,应用初始整流术语的TCNN模型用于解决在线计算问题,并且可以预先控制收敛时间。严格澄清了稳定性,预先确定的时间和收敛性的理论分析。最后,已经验证了用于QP示例的解决方案的TCNN模型的有效性和精度。此外,引入了三轮操纵器的重复轨迹规划以证明TCNN的优越性。

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