This paper presents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision positi...This paper presents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized;that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of the proposed control system so that the stability of the system can be guaranteed. The simulation results of three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology.展开更多
A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant inf...A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger,the generalization ability is better,but the difference of degradation states is not obvious.If r is smaller,the different states are distinct,but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.展开更多
CMAC(Cerebellar Model Articulation Controller)和PD(Proportional Derivative)复合控制算法有时因输出不平滑会引起加载电机抖动而影响控制效果.通过对该输出不平滑问题进行分析,提出了一种新的提高输出平滑性的改进CMAC复合控制算法...CMAC(Cerebellar Model Articulation Controller)和PD(Proportional Derivative)复合控制算法有时因输出不平滑会引起加载电机抖动而影响控制效果.通过对该输出不平滑问题进行分析,提出了一种新的提高输出平滑性的改进CMAC复合控制算法,该方法通过新的权值更新公式,在权值更新时直接达到减小误差和提高输出平滑性的目的.仿真和实验结果表明:改进后的算法能够有效提高输出平滑性,降低了21%的稳态误差,且保证在加载时有良好的稳定性和抗干扰能力.展开更多
为了解决设备相关颜色空间CMYK与设备无关颜色空间之间的相互转换问题,利用小脑模型神经网络(cerebellar model articulation controller,CMAC)高度非线性拟合能力,研究CMYK颜色空间与CIE L*a*b*之间的转换关系,研究结果显示该方法具有...为了解决设备相关颜色空间CMYK与设备无关颜色空间之间的相互转换问题,利用小脑模型神经网络(cerebellar model articulation controller,CMAC)高度非线性拟合能力,研究CMYK颜色空间与CIE L*a*b*之间的转换关系,研究结果显示该方法具有结构简单,易于软件和硬件的实现,将IT8.7/3标准色靶文件中104个专业色块值作为检验样本,检验样本的平均色差为1.6,完全适用于两种不同颜色空间之间的转换过程.展开更多
为了提高双层被动隔振系统隔离低频结构噪声的效果,采用混合隔振思想,将小脑模型神经网络(cerebellar model articulation controller,CMAC)理论与PID控制算法相结合,设计了双层混合隔振系统CMAC与PID复合控制器,仿真分析了双层混合隔...为了提高双层被动隔振系统隔离低频结构噪声的效果,采用混合隔振思想,将小脑模型神经网络(cerebellar model articulation controller,CMAC)理论与PID控制算法相结合,设计了双层混合隔振系统CMAC与PID复合控制器,仿真分析了双层混合隔振系统在不同低频正弦激励信号下的加速度和加速度功率谱。仿真结果表明,采用CMAC与PID复合控制的双层混合隔振系统的隔振效果要优于被动双层隔振系统的隔振效果。展开更多
文摘This paper presents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized;that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of the proposed control system so that the stability of the system can be guaranteed. The simulation results of three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology.
基金The National Natural Science Foundation of China(No.60443007,50390063).
文摘A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger,the generalization ability is better,but the difference of degradation states is not obvious.If r is smaller,the different states are distinct,but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.
文摘CMAC(Cerebellar Model Articulation Controller)和PD(Proportional Derivative)复合控制算法有时因输出不平滑会引起加载电机抖动而影响控制效果.通过对该输出不平滑问题进行分析,提出了一种新的提高输出平滑性的改进CMAC复合控制算法,该方法通过新的权值更新公式,在权值更新时直接达到减小误差和提高输出平滑性的目的.仿真和实验结果表明:改进后的算法能够有效提高输出平滑性,降低了21%的稳态误差,且保证在加载时有良好的稳定性和抗干扰能力.
文摘为了解决设备相关颜色空间CMYK与设备无关颜色空间之间的相互转换问题,利用小脑模型神经网络(cerebellar model articulation controller,CMAC)高度非线性拟合能力,研究CMYK颜色空间与CIE L*a*b*之间的转换关系,研究结果显示该方法具有结构简单,易于软件和硬件的实现,将IT8.7/3标准色靶文件中104个专业色块值作为检验样本,检验样本的平均色差为1.6,完全适用于两种不同颜色空间之间的转换过程.
文摘为了提高双层被动隔振系统隔离低频结构噪声的效果,采用混合隔振思想,将小脑模型神经网络(cerebellar model articulation controller,CMAC)理论与PID控制算法相结合,设计了双层混合隔振系统CMAC与PID复合控制器,仿真分析了双层混合隔振系统在不同低频正弦激励信号下的加速度和加速度功率谱。仿真结果表明,采用CMAC与PID复合控制的双层混合隔振系统的隔振效果要优于被动双层隔振系统的隔振效果。
