| 引用本文: | 章云, 刘治.基于降阶位置/力模型的机器人神经网络控制[J].控制理论与应用,2007,24(4):541~545.[点击复制] |
| ZHANG Yun, LIU Zhi.Neural network robotic control for a reduced order position/force model[J].Control Theory and Technology,2007,24(4):541~545.[点击复制] |
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| 基于降阶位置/力模型的机器人神经网络控制 |
| Neural network robotic control for a reduced order position/force model |
| 摘要点击 1374 全文点击 923 投稿时间：2005-11-23 修订日期：2006-07-31 |
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| DOI编号 10.7641/j.issn.1000-8152.2007.4.006 |
| 2007,24(4):541-545 |
| 中文关键词 神经网络控制 降阶位置/力模型 机器人控制 自适应控制 鲁棒控制 |
| 英文关键词 neural network control reduced order position/force model robotic control adaptive control robust control |
| 基金项目 国家自然科学基金资助项目(60604006); 广东省自然科学基金资助项目(6021452,51685168) |
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| 中文摘要 |
| 双足机器人的双脚支撑期是实现其步行运动的重要过程, 然而耦合的位置/力控制难以保证其稳定平滑运动. 本文提出了一种基于降阶位置/力模型的机器人控制策略, 整合了位置控制子空间模型和力控制子空间模型, 通过模型降阶减小了控制器设计的复杂度, 并采用神经网络自适应控制方法综合多控制目标, 实现了双足机器人的平滑稳定控制并有效地抑制了系统外扰和参数不确定性的影响. 最后, 仿真算法验证了该控制方法和模型的有效性. |
| 英文摘要 |
| The double-support phase is an important walking process to guarantee a smooth switching motion during the locomotion of bipeds. However, the traditional coupled position/force controller can hardly achieve a stable and smooth motion for this phase. A robotic control method is proposed based on a reduced order position/force hybrid robotic model in this paper. The walking locomotion of biped robots in the double-support phase is modeled as a reduced order position/force hybrid model, where the position and force control models are integrated to consider various control performances as a whole and to reduce the complexity of the controller design. The neural network adaptive control method is then presented to guarantee the smooth locomotion and to attenuate the effect of external disturbances and parametric uncertainties. Simulation results are also reported to show the performance of the proposed control model and control scheme. |