To obtain the solution for time‐varying quadratic programming (QP), a time controlling neural network (TCNN) is presented and discussed. The traditional recurrent 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.

中文翻译：

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用于时变 QP 求解的时间控制神经网络在移动机械手运动学中的应用

为了获得时变二次规划 (QP) 的解决方案，提出并讨论了时间控制神经网络 (TCNN)。传统的递归神经网络由于其高执行处理能力和非线性处理能力，为移动机械手的实时计算和可重复轨迹控制提供了前景。然而，收敛时间对于处理同步性和鲁棒性的动态系统的求解来说仍然是一个相当重要的点。在这篇笔记中，通过合并初始校正项的 TCNN 模型被应用于解决在线计算问题，并且收敛时间可以提前控制。对稳定性、预设时间和收敛性的理论分析得到了严格的阐明。最后，已经验证了 TCNN 模型在解决 QP 示例时的有效性和精度。此外，还引入了三轮机械手的重复轨迹规划，以证明 TCNN 的优越性。