仿生机械臂的小脑控制模型和仿真

A Cerebellar Control Model and Simulation of Biomimetic Manipulator

提出了一种控制模拟仿生机械臂延伸运动的小脑学习模型.该模型在已知小脑结构的基础上,利用脊髓反射线路的伺服机制,将神经网络嵌入模拟哺乳动物运动的控制系统中,用以控制一个6肌肉块2关节的平面手臂,真正从生物学意义上实现了由Katayama和Kawato提出的并行分层控制的有关论述,并且证明能够迅速地学会精确轨迹控制.仿真结果表明,该小脑模型能够很好地学习不能由PDF+F控制器所提供动态逆模型的相关内容,且具有较好的理想轨迹跟踪性能.

This paper presents a learning cerebeUar model to control reaching movements of a simulated biomimetic manipulator. Utilizing the servo mechanism of the spinal reflex circuitry, the model embeds a neural network based on known cerebdlar circuitry in a simulation of the mammalian motor control system to control a 6-muscle 2-link planar arm.The system had implemented a biologically version of the parallel hierarchical control model proposed by Katayama and Kawato and was proved to be able to learn accurate trajectory control. The simulation results demonstrate that this cerebellar model was able to learn parts of the inverse dynamics model not provided by the PDF＋ F controller,and having nicer tracing performance of desired trajectory.