论文标题
基于Levenberg-Marquardt方法和无模型自适应（预测）控制的逆运动学的讨论
Discussions on Inverse Kinematics based on Levenberg-Marquardt Method and Model-Free Adaptive (Predictive) Control
论文作者
论文摘要
在此简介中，基于Levenberg-Marquardt（LM）方法的逆运动学的当前强大数值解决方案是通过控制理论而不是数值方法重新分析的。与当前的作品相比，通过分析校正模型自由自适应控制（MFAC）的控制性能，可以更清楚地分析计算和计算误差的收敛性能的鲁棒性。然后，这项研究主要是通过最大程度地减少预测跟踪误差来进行的，这是一种新的模型自适应预测控制（MFAPC）的方法，以解决逆运动学问题。最后，我们将MFAPC应用于模拟中机器人运动控制问题的控制器。它不仅显示出出色的控制性能，而且还有效地获取了逆运动学的解决方案。
In this brief, the current robust numerical solution to the inverse kinematics based on Levenberg-Marquardt (LM) method is reanalyzed through control theory instead of numerical method. Compared to current works, the robustness of computation and convergence performance of computational error are analyzed much more clearly by analyzing the control performance of the corrected model free adaptive control (MFAC). Then mainly motivated by minimizing the predictive tracking error, this study suggests a new method of model free adaptive predictive control (MFAPC) to solve the inverse kinematics problem. At last, we apply the MFAPC as a controller for the robotic kinematic control problem in simulation. It not only shows an excellent control performance but also efficiently acquires the solution to inverse kinematic.
