The controllability of both continuous and discrete periodic systems are evaluated. The controllability for continuous periodic systems is extended to unit period control, and these results are then directly extended to discrete periodic systems. An assessment is made on retaining periodic system controllability during discretisation, resulting in the conclusion that there is no loss in the controllability of linear periodic systems during discretisation when using a nonequidistant sampling pattern.

基于Volterra级数理论，计算出M IMO双线性系统的n阶冲激响应函数矩阵（或n阶Volterra核矩阵）。通过研究它的n阶冲激响应函数矩阵的性质，给出了一些简单的开环稳定性判据；并深入研究了它们的闭环稳定性，给出了直接利用其子系统的开环稳定性来判别其闭环系统稳定性的判据；最后，用仿真实例来验证其有效性。

This paper deals with data (or information) fusion for the purpose of estimation. Three estimation fusion architectures are considered: centralized, distributed, and hybrid. A unified linear model and a general framework for these three architectures are established. Optimal fusion rules based on the best linear unbiased estimation (BLUE), the weighted least squares (WLS), and their generalized versions are presented for cases with complete, incomplete, or no prior information. These rules are more general and flexible, and have wider applicability than previous results. For example, they are in a unified form that is optimal for all of the three fusion architectures with arbitrary correlation of local estimates or observation errors across sensors or across time. They are also in explicit forms convenient for implementation. The optimal fusion rules presented are not limited to linear data models. Illustrative numerical results are provided to verify the fusion rules and demonstrate how these fusion rules can be used in cases with complete, incomplete, or no prior information.

基于混沌现象普遍存在于各类非线性系统中这一事实，针对工业过程中的混沌运动行为，给出了混沌稳态的数学定义，证明了具有非线性机制的工业过程混沌稳态的存在性定理，并对一个化工过程的混沌稳态进行了仿真计算。

本文研究了在输入输出观测数据均含有噪声的情况下如何有效地进行鲁棒自适应滤波的问题。以总体均方误差（TMSE）最小为准则，基于最速下降原理，通过对总体均方误差梯度进行修正，提出了一种鲁棒的总体均方最小自适应滤波算法。通过与已有算法的对比分析表明，该算法能够有效地降低权向量的每步调整量对噪声的敏感程度.。仿真实验的结果进一步表明，该算法的鲁棒抗噪性能和稳态收敛精度明显地高于其它同类方法，而且可以使用较大的学习因子，在高噪声环境下仍然保持良好的收敛性。