本文提出一种由自校正调节器—最小方差控制器（以下简记为STR—MV）的双模控制和在线修正不辨识参数所构成的新的自校正控制方法。
This paper presents a new self-tuning control consisting of a dual mode control by use of STR-MV and the on-line regulation parameter which is unnecessary to be identified.
采用了具有积分性质的切换指标函数作为切换法则和最小方差的控制方法构成了多模型自适应控制器。
A switching function with integral property and minimum variance algorithm are used to set up the multiple model adaptive controller.
分析结果表明，最小方差准则是控制器的性能评价方面一个有效的和可行的基准。
The results reveal that minimum variance benchmark acts as an efficient and feasible measurement in controller performance assessment.
用最小方差准则来评价控制器的性能，仅需过程变量的常规闭环操作数据和过程的时间延迟特性。
It only needs routine closed-loop operating data and knowledge of the process time delay using minimum variance as a benchmark in controller performance assessment.
本文针对未知参数的最小方差控制问题，提出了一种在谨慎控制器基础上加入探测信号的控制策略。
This paper presents a dual control strategy with a probing signal for the minimum variance control problems with unknown parameters.
针对最小方差自适应控制器的固有缺点 ，在标准最小方差控制中引入糊模控制算法来弥补其不足 。
Aiming at the intrinsic defects of minimum variance adaptive controllers, fuzzy controy algorithm is introduced into standard minimum variance control.
针对最小方差自适应控制器的固有缺点 ，在标准最小方差控制中引入糊模控制算法来弥补其不足 。
Aiming at the intrinsic defects of minimum variance adaptive controllers, fuzzy controy algorithm is introduced into standard minimum variance control.
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