Minimum variance control based on nonlinear t - s fuzzy model 模糊模型的隨機(jī)最小方差控制
Design of identification experiment signal for minimum variance control 基于最小方差控制的閉環(huán)辨識(shí)信號(hào)設(shè)計(jì)
Thus finally we can realize the minimum variance control . simulations show that preferable parameter estimation and satisfactory control performance can be achieved using the derived dual adaptive control 利用卡爾曼濾波對(duì)未知的系統(tǒng)參數(shù)進(jìn)行估計(jì),最終實(shí)現(xiàn)隨機(jī)系統(tǒng)的對(duì)偶自適應(yīng)控制。
Abstract : in the nonminimum phase system , it contains the unstable zero , so the controller can " t be designed according to the ideal state . the mode of modified evaluate function addressed in this paper can promise the bounds and stability of the minimum variance control in nonminimum phase 文摘:對(duì)于非最小相位系統(tǒng),因其含有不穩(wěn)定的零點(diǎn),所以不可以按理想狀態(tài)的情況構(gòu)成控制器.提出修正目標(biāo)函數(shù)的方法,可確保非最小相位時(shí)最小方差控制輸入的有界性和穩(wěn)定性
In this thesis single input - single output and multiple input - multiple output stochastic systems are discussed respectively . innovations are introduced to reconstruct the original minimum variance control problem of stochastic system , which is unsolvable by means of dynamic programming . so it can be converted into multiple single - step control problems , in which kalman filter is used to estimate unknown system parameters 本文分別針對(duì)單輸入單輸出和多輸入多輸出的隨機(jī)系統(tǒng)進(jìn)行了研究,通過引入系統(tǒng)的新息對(duì)原不可解的動(dòng)態(tài)規(guī)劃問題進(jìn)行重構(gòu),將系統(tǒng)參數(shù)隨機(jī)變化的最小方差控制問題轉(zhuǎn)化成為多個(gè)基于新息的單步控制問題。