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1.
探讨了含多状态时滞连续时间迭代学习控制系统的稳定性分析问题, 尤其是当系统参数带有多面体不确定性时的鲁棒稳定性分析问题. 通过引入一个扩展算子, 利用迭代学习控制中的二维分析方法给出了时滞系统整个学习动态过程的连续离散Roesser系统描述. 基于所得的Roesser系统, 首先利用二维系统理论给出了保证迭代学习控制系统渐近稳定的充要条件, 然后结合鲁棒H控制理论提出了以线性矩阵不等式形式描述的充分条件来保证迭代学习控制系统的单调收敛性. 结果表明, 通过求解线性矩阵不等式确定的学习增益可以使控制输入误差随着迭代次数的增加单调收敛于零. 仿真结果表明, 通过增加满足一组线性矩阵不等式条件的P型学习增益能够使得一个鲁棒渐近稳定的迭代学习控制方案变为鲁棒单调收敛的, 同时还可以大大提高收敛速率.  相似文献
2.
In iterative learning control schemes for linear discrete time systems, conditions to guarantee the monotonic convergence of the tracking error norms are derived. By using the Markov parameters, it is shown in the time-domain that there exists a non-increasing function such that when the properly chosen constant learning gain is multiplied by this function, the convergence of the tracking error norms is monotonic, without resort to high-gain feedback.  相似文献
3.
This paper presents a stability analysis of the iterative learning control (ILC) problem for discrete-time systems when the plant Markov parameters are subject to interval uncertainty. Using the so-called super-vector approach to ILC, vertex impulse response matrices are employed to develop sufficient conditions for both asymptotic stability and monotonic convergence of the ILC process. It is shown that the stability of such interval ILC systems can be determined by checking the stability of the system using only the vertex points of the interval Markov parameters.  相似文献
4.
针对偏序情形,构造一类适用于多处理机系统的求解线性与非线性方程组的异步迭代并行算法,并对其单调收敛性条件进行了严格的理论分析。此外,还用数值试验证实了这些结果。  相似文献
5.
Zhong-Zhi Bai 《Calcolo》1995,32(3-4):207-220
This paper reveals the inner links between two known frameworks of multisplitting relaxation methods as completely as possible. By meticulously investigating the specific structures of these two frameworks, the asymptotic convergence rates as well as the monotone convergence rates of them are compared theoretically. This then definitely answers the question that which converges faster between these two frameworks of parallel matrix multisplitting relaxation methods from the standpoint of pure mathematics. At last, an example further confirms the correctness of the theoretical results.  相似文献
6.
医学图像和实际手术空间的配准问题是计算机辅助外科手术技术的一个重要的研究热点,它能够帮助医生选择最佳手术路径和减小手术损伤,实现手术的方便快捷和微创,提高手术成功率。主要研究了一种基于ICP算法的计算机辅助外科手术中空间配准技术,通过获取实验模型的空间坐标信息,进行点集配准,找到最优旋转矩阵和最优平移向量。并通过仿真实验给出了模拟的配准结果,仿真试验结果表明,ICP算法配准精度高,适用于计算机辅助外科手术。  相似文献
7.
This paper presents a general method to formulate monotonically convergent algorithms for identifying optimal control fields to manipulate quantum dynamics phenomena beyond the linear dipole interaction. The method, facilitated by a field-dependent dipole moment operator, is based on an integral equation of the first kind arising from the Heisenberg equation of motion for tracking a time-dependent, dynamical invariant observable associated with a reference control field.  相似文献
8.
针对一类线性时不变系统, 提出了具有反馈信息的PD-型(Proportional-derivative-type)迭代学习控制律, 利用卷积的推广的Young不等式, 分析了控制律在Lebesgue-p范数意义下的单调收敛性. 分析表明, 收敛性不但决定于系统的输入输出矩阵和控制律的微分学习增益, 而且依赖于系统的状态矩阵和控制律的比例学习增益; 进一步, 当适当选取反馈增益时, 反馈信息可加快典型的PD-型迭代学习控制律的单调收敛性. 数值仿真验证了理论分析的正确性和控制律的有效性.  相似文献
9.
In this paper, a novel high‐order optimal terminal iterative learning control (high‐order OTILC) is proposed via a data‐driven approach for nonlinear discrete‐time systems with unknown orders in the input and output. The objective is to track the desired values at the endpoint of the operation cycle. The terminal tracking errors over more than one previous iterations are used to enhance the high‐order OTILC's performance with faster convergence. From rigor of the analysis, the monotonic convergence of the terminal tracking error is proved along the iteration direction. More importantly, the condition for a high‐order OTILC to outperform the low‐order ones is first established by this work. The learning gain is not fixed but iteratively updated by using the input and output (I/O) data, which enhances the flexibility of the proposed controller for modifications and expansions. The proposed method is data‐driven in which no explicit models are used except for the input and output data. The applications to a highly nonlinear continuous stirred tank reactor and a highly nonlinear fed‐batch fermentater demonstrate the effectiveness of the proposed high‐order OTILC design.  相似文献
10.
This paper discusses first‐ and second‐order fractional‐order PID‐type iterative learning control strategies for a class of Caputo‐type fractional‐order linear time‐invariant system. First, the additivity of the fractional‐order derivative operators is exploited by the property of Laplace transform of the convolution integral, whilst the absolute convergence of the Mittag‐Leffler function on the infinite time interval is induced and some properties of the state transmit function of the fractional‐order system are achieved via the Gamma and Bata function characteristics. Second, by using the above properties and the generalized Young inequality of the convolution integral, the monotone convergence of the developed first‐order learning strategy is analyzed and the monotone convergence of the second‐order learning scheme is derived after finite iterations, when the tracking errors are assessed in the form of the Lebesgue‐p norm. The resultant convergences exhibit that not only the fractional‐order system input and output matrices and the fractional‐order derivative learning gain, but also the system state matrix and the proportional learning gain, and fractional‐order integral learning gain dominate the convergence. Numerical simulations illustrate the validity and the effectiveness of the results.  相似文献
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