本文選題：未知觀測輸入 + 觀測丟失�。� 參考：《黑龍江大學》2015年碩士論文

【摘要】：含未知輸入干擾隨機系統(tǒng)的狀態(tài)估計問題廣泛存在于控制、信號處理和故障診斷等應(yīng)用中。在許多情況下,外界擾動往往是無法測量的(即未知輸入),如果不能很好地對干擾或故障進行有效的檢測和分離,可能會造成人員和生產(chǎn)上的損失。此外,在網(wǎng)絡(luò)化控制系統(tǒng)(NCSs)中,由于網(wǎng)絡(luò)的帶寬和承載能力有限,數(shù)據(jù)在傳輸時發(fā)生隨機時滯和觀測丟失的現(xiàn)象不可避免。除了以上干擾,由乘性噪聲描述的參數(shù)不確定性也可能存在于系統(tǒng)的模型中。以往的文獻大都分別對帶有未知輸入、時滯、觀測丟失和乘性噪聲的系統(tǒng)開展研究,但同時考慮以上不確定性的文獻鮮見。因此,考慮到這些問題,本文研究帶未知觀測輸入隨機不確定網(wǎng)絡(luò)化系統(tǒng)的狀態(tài)融合估計問題。主要研究內(nèi)容如下:對同時具有未知觀測輸入、觀測數(shù)據(jù)丟失和參數(shù)乘性噪聲的隨機不確定系統(tǒng),提出了與未知觀測輸入解耦的具有Kalman形式的分布式和集中式融合濾波器,包括先驗濾波器(一步預(yù)報器)和后驗濾波器。給出了任意兩個傳感器子系統(tǒng)之間的濾波誤差互協(xié)方差陣。對于相應(yīng)的定常系統(tǒng),分別給出了分布式和集中式融合穩(wěn)態(tài)濾波器存在的充分條件,并證明了任意兩個傳感器子系統(tǒng)之間的互協(xié)方差陣穩(wěn)態(tài)解的存在性。最后,給出了未知輸入的次優(yōu)估計算法。對同時具有未知觀測輸入和一步隨機時滯的隨機不確定系統(tǒng),通過引入新的變量,將帶未知觀測輸入和隨機時滯的系統(tǒng)轉(zhuǎn)化為等價的帶隨機參數(shù)的系統(tǒng),提出了與未知觀測輸入解耦的分布式和集中式融合濾波器,包括先驗濾波器(一步預(yù)報器)和后驗濾波器。給出了任意兩個傳感器子系統(tǒng)之間的濾波誤差互協(xié)方差陣。最后,給出了未知輸入的次優(yōu)估計算法。
[Abstract]:The problem of state estimation for stochastic systems with unknown input disturbances is widely used in control, signal processing and fault diagnosis. In many cases, external disturbances are often unmeasurable (I. e., unknown inputs). If interference or fault can not be effectively detected and separated, it may result in loss of personnel and production. In addition, in networked control system (NCSs), due to the limited bandwidth and carrying capacity of the network, the phenomenon of random delay and observation loss in data transmission is inevitable. In addition to the above disturbances, the parametric uncertainties described by multiplicative noise may also exist in the system model. Most of the previous literatures have studied the systems with unknown input, time delay, observation loss and multiplicative noise, but there are few papers considering the uncertainties mentioned above. Therefore, considering these problems, the problem of state fusion estimation for networked systems with unknown observation inputs is studied in this paper. The main research contents are as follows: for stochastic uncertain systems with unknown input, data loss and parametric multiplicative noise, a distributed and centralized fusion filter with Kalman form is proposed, which is decoupled from unknown observation input. A priori filter (one-step predictor) and a posteriori filter are included. The filter error mutual covariance matrix between any two sensor subsystems is given. For the corresponding time-invariant systems, sufficient conditions for the existence of distributed and centralized fusion steady-state filters are given, and the existence of the steady-state solutions of the cross-covariance matrix between any two sensor subsystems is proved. Finally, a suboptimal estimation algorithm for unknown inputs is presented. For stochastic uncertain systems with both unknown observation input and one step stochastic delay, the system with unknown observation input and stochastic delay is transformed into an equivalent system with random parameters by introducing new variables. A distributed and centralized fusion filter, which is decoupled from unknown observation inputs, is proposed, including a priori filter (a one-step predictor) and a posteriori filter. The filter error mutual covariance matrix between any two sensor subsystems is given. Finally, a suboptimal estimation algorithm for unknown inputs is presented.
【學位授予單位】：黑龍江大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN713

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