A novel exponential varying-parameter neural network (EVPNN) is presented and investigated to solve the inverse redundancy scheme of the mobile manipulators via quadratic programming (QP). To suspend the phenomenon of drifting free joints and guarantee high convergent precision of the end effector, the EVPNN model is applied to trajectory planning of mobile manipulators. Firstly, the repetitive motion scheme for mobile manipulators is formulated into a QP index. Secondly, the QP index is transformed into a time-varying matrix equation. Finally, the proposed EVPNN method is used to solve the QP index via the matrix equation. Theoretical analysis and simulations illustrate that the EVPNN solver has an exponential convergent speed and strong robustness in mobile manipulator applications. Comparative simulation results demonstrate that the EVPNN possesses a superior convergent rate and accuracy than the traditional ZNN solver in repetitive trajectory planning with a mobile manipulator.

中文翻译：

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用于机械手重复跟踪的指数变参数神经网络

提出并研究了一种新颖的指数变参数神经网络（EVPNN），以通过二次编程（QP）解决移动机械手的逆冗余方案。为了暂停自由关节漂移的现象并确保末端执行器的高收敛精度，将EVPNN模型应用于移动机械手的轨迹规划。首先，将用于移动机械手的重复运动方案制定为QP指标。其次，将QP指数转换为时变矩阵方程。最后，提出的EVPNN方法用于通过矩阵方程求解QP指数。理论分析和仿真表明，EVPNN求解器在移动机械手应用中具有指数级的收敛速度和强大的鲁棒性。