For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself ...For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.展开更多
利用系统已知信息构造的扩张状态观测器(extended state observer,ESO)称之为修改型扩张状态观测器.本文完善了修改型扩张状态观测器(modified extended state observer,MESO)的假设条件,并分析了相对于常规的扩张状态观测器,修改型扩...利用系统已知信息构造的扩张状态观测器(extended state observer,ESO)称之为修改型扩张状态观测器.本文完善了修改型扩张状态观测器(modified extended state observer,MESO)的假设条件,并分析了相对于常规的扩张状态观测器,修改型扩张状态观测器估计精度更高的原因.针对系统模型参数不确定情况,提出了基于最小二乘法的修改型扩张状态观测器实现方法,并详细论述了最小二乘法在线辨识系统模型的原理.仿真结果与理论分析结果一致,验证了修改型扩张状态观测器理论分析的正确性和实现方法的可行性.展开更多
基金funded by Prince Sultan University,Riyadh,Saudi Arabia。
文摘For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.
文摘利用系统已知信息构造的扩张状态观测器(extended state observer,ESO)称之为修改型扩张状态观测器.本文完善了修改型扩张状态观测器(modified extended state observer,MESO)的假设条件,并分析了相对于常规的扩张状态观测器,修改型扩张状态观测器估计精度更高的原因.针对系统模型参数不确定情况,提出了基于最小二乘法的修改型扩张状态观测器实现方法,并详细论述了最小二乘法在线辨识系统模型的原理.仿真结果与理论分析结果一致,验证了修改型扩张状态观测器理论分析的正确性和实现方法的可行性.
