Neural Net Computer Controller for a Six Axis Manipulator

六足并联机器人(Stewart Platform，简称Stewart平台)具有六自由度操作的能力，其机械结构比具有类似操作能力的传统串联机构简单得多。尽管这种机构机械简单，但由于很难对高度非线性、高度耦合的机构进行实时控制，因此除了低精度、低速应用外，这些机构还没有被利用。由于此类平台的正运动学没有已知的闭合解，这加剧了控制问题。该项目将开发两个相互协作的神经网络，共同帮助在高速、高精度领域控制六足并联机械手。这两个神经网络各自由两个级联的神经网络组成。提出的系统很有趣，因为它解决了一个没有已知的可接受解决方案的问题，还因为有许多重要的应用程序可以用Stewart平台实现，如果控制问题可以克服的话。该系统还具有重要意义，因为它是神经网络的级联组织，可以大大减少系统所需的训练时间，达到良好的效果。

A six legged parallel link manipulator (called a Stewart Platform) has the ability to perform six degree of freedom manipulation, and is mechanically much simpler than conventional serial mechanisms with similar manipulative capability. Despite their mechanical simplicity, such mechanisms have not been exploited except for low accuracy, low speed applications because of the difficulty in performing real-time control of the highly non-linear, highly coupled mechanism. The control problem is exacerbated by the fact that there is no known closed from solution to the forward kinematics of such platforms. This project will develop two cooperating neural nets which together help control a six legged parallel link manipulator in the high speed, high accuracy domain. Each of the two neural nets is itself composed of tow cascaded neural nets. The proposed system is interesting because it solves a problem for which there is no known acceptable solution, and also because there are many important applications which could be cost effectively implemented with a stewart Platform if the control problem could be overcome. The proposed system is also significant because is cascaded organization of neural nets should greatly reduce the training time required for the system to achieve good results.