自适应模糊与CMAC并行的机器人力/位置控制

为提高机器人系统对机器人末端操纵器与外界工作环境接触时，其接触刚度不确定性的自适应能力，在机器人力／位置混合控制的基础上，设计出了一种基于自适应模糊与CMAC并行控制的机器人力控制器，采用小脑模型神经控制器实现前馈控制，实现被控对象的逆动态模型，自适应模糊控制器实现反馈控制，保证系统的稳定性，且抑制扰动。以平面两关节机器人进行仿真，仿真结果表明，系统的自适应能力和力跟踪能力有显著的提高，机械手在其末端操纵器与刚性变化范围较大的外界工作环境接触时，具有较强的适应能力，较好地完成了机器人的力／位置控制。

Force/Position Hybrid Control Based on Adaptive Fuzzy and CMAC

Based on the force/position hybrid control,a method by means of self-adaptive fuzzy control and CMAC is presented.For general industrial robot,it can improve the self-adaptivity of the control system when the end-effector of robot contact with the work environment which has uncertain contact stiffness.Simulation studies with a two-degrees-of-freedom robot show that the proposed control method obviously improves the self-adaptivity of the system to the variation of contact stiffness for work environment,compared with the conventional PID control.The results show that excellent force tracking is achieved.