| 引用本文: | 杨波,王哲.新型小脑模型关联控制器复合控制在电动加载系统中的结构及算法[J].控制理论与应用,2011,28(6):827~833.[点击复制] |
| YANG Bo,WANG Zhe.Structure and algorithm of hybrid control in cerebella model articulation controller for electric loading system[J].Control Theory and Technology,2011,28(6):827~833.[点击复制] |
|
| 新型小脑模型关联控制器复合控制在电动加载系统中的结构及算法 |
| Structure and algorithm of hybrid control in cerebella model articulation controller for electric loading system |
| 摘要点击 2129 全文点击 1946 投稿时间：2010-03-10 修订日期：2010-10-14 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/j.issn.1000-8152.2011.6.CCTA100225 |
| 2011,28(6):827-833 |
| 中文关键词 电动加载系统 多余力矩 CMAC神经网络 复合控制 非均匀量化 高斯权重系数 |
| 英文关键词 electric loading system surplus torque CMAC neural network hybrid control non-uniform quantization Gaussian weighting coefficient |
| 基金项目 北京航空航天大学蓝天计划基金资助项目. |
|
| 中文摘要 |
| 在电动加载系统中, 多余力矩强扰动和其他非线性因素直接影响力矩跟踪精度, 传统的控制方法很难得到满意的控制效果. 本文分析了电动加载系统中多余力矩产生机理, 提出了一种新型小脑模型关联控制器(CMAC)复合控制策略, 并对其结构及算法进行了研究. 在控制结构上以系统的指令输入和实际输出作为CMAC的激励信号, 采用误差作为训练信号, 并根据激励信号的特点, 提出了非均匀量化的思想. 不同于常规CMAC的误差平均分配, 新型CMAC根据高斯权重系数来分配误差. 动态仿真结果表明, 该方法有效抑制了加载系统的多余力矩及摩擦等非线性因素干扰, 提高了电动加载系统的控制精度, 增强了系统的稳定性. |
| 英文摘要 |
| The disturbance of surplus torque and the nonlinearity deteriorate the precision of torque tracking in the electric loading system. This undesired effect can not be removed satisfactorily by the conventional control. To deal with this problem, after analyzing the effect of surplus torque on the electric loading system, we propose a hybrid control based on the novel cerebella model articulation controller(CMAC) and investigate its structure and algorithm. This control adopts the desired output and the actual output of the electric loading system as the incentive signals of CMAC, and treats the error between the desired output and the actual output as the training signal for the CMAC structure. A non-uniform quantization scheme is then proposed according to the characteristics of the incentive signals. In the conventional CMAC, errors are equally distributed into memory cells; while the novel CMAC allocates errors according to the Gaussian weighting coefficients. Simulation results show that the proposed hybrid controller effectively suppresses the disturbance of surplus torque and the nonlinearity such as friction, improves the control precision of the electric loading system and enhances the control stability of the system. |
|
|
|
|
|