中文摘要：

追踪控制框架的新柔韧的 proportional-integral-derivative (PID ) 与基于 B 花键神经网络近似和 T-S 模糊模型鉴定可变的 non-Gaussian 为随机的系统被考虑。追踪的目标是给定的目标概率密度的统计信息功能(PDF ) ，而非一个确定的信号。后面的 B 花键近似到综合性能函数，担心的问题被变成追踪给定的重量。与以前的相关工作不同，有外长的骚乱的时间延期 T-S 模糊模型被使用识别非线性的 weighting 动力学。同时，概括 PID 控制器结构和改进凸的线性矩阵不平等(LMI ) 算法被建议完成追踪的问题。而且，以便提高柔韧的表演， peak-to-peak 措施索引被使用优化追踪的表演。模拟被给表明建议途径的效率。

英文摘要：

A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.