The simulation example verifies the effectiveness of closed-loop iterative learning control law.
仿真实例说明闭环迭代学习律的有效性和快速性。
The design scheme of the classical D-type iterative learning control law depends on the relative degree of the controlled systems.
传统的D型迭代学习控制的控制律设计方案依赖于被控系统的相对度。
Some simulation results show the rationability of stable in the conditional expectation and the efficiency of improved iterative learning control law.
一些仿真结果表明给出的依条件期望稳定的合理性，改进的迭代学习控制律和稳定性定理的有效性。
The simulation test verify the controlled system with random disturbance can reached to stability by using improved iterative learning control law but not the traditional control law.
通过仿真试验检验了改进的迭代学习控制律能够使得具有随机干扰的被控系统达到稳定，而传统的控制律则不能。
A new iterative learning control (ILC) updating law is proposed for the tracking control of continuous linear system over a finite time interval.
提出了一个新的迭代学习控制(ilc)更新律用于连续线性系统的有限时间区间跟踪控制。
D-type iterative learning control (ILC) law is one of the main laws of ILC.
型迭代学习控制律是迭代学习控制的一种主要学习律。
Based on the model, the Taylor series coefficients of control function are adjusted by an iterative learning law and the learning gain matrix is designed via LMI optimization.
模型的基础上，泰勒级数的系数调整控制功能的迭代学习法律，学习增益矩阵，通过LMI优化设计。
Based on the model, the Taylor series coefficients of control function are adjusted by an iterative learning law and the learning gain matrix is designed via LMI optimization.
模型的基础上，泰勒级数的系数调整控制功能的迭代学习法律，学习增益矩阵，通过LMI优化设计。
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