【摘要】：系統(tǒng)級的標定技術與單個儀表在實驗室中的測試很不一樣。儀表的一些誤差系數安裝到系統(tǒng)中后,往往會隨著環(huán)境條件改變而發(fā)生改變,需要再重新標定�；趹T性穩(wěn)定平臺的慣導平臺系統(tǒng)包含了具有三個旋轉自由度的框架系統(tǒng),這種旋轉功能使得它能夠不必依賴于外部測試設備或基準實現自標定。連續(xù)翻滾測試方法與當前廣泛使用的多位置翻滾測試方法相比,由于平臺一直處于伺服工作狀態(tài),不僅能夠充分利用翻滾過程中全部的觀測信息,辨識出更多的誤差項系數,而且有更高的標定精度,測試過程也簡單高效。但是誤差模型方程和試驗設計過程也很復雜。本文對慣導平臺系統(tǒng)連續(xù)翻滾自標定試驗設計和辨識相關問題展開研究,首先選用加速度計和陀螺儀共計30個誤差項系數,狀態(tài)方程和觀測方程分別使用ψ角和加速度計輸出建立。為了提高系統(tǒng)誤差參數的可觀測度,得到連續(xù)翻滾試驗中平臺的最優(yōu)旋轉軌跡,使用D最優(yōu)化試驗設計方法,得到相應的數學表達式。通過適當的數學描述和工程簡化,將最優(yōu)連續(xù)旋轉軌跡的設計問題轉化為最優(yōu)控制問題進行求解。針對最優(yōu)試驗設計的求解問題,采用全局智能優(yōu)化算法求解。同時為了提高最優(yōu)軌跡的計算效率和精度,將非線性約束最優(yōu)化問題轉換為無約束最優(yōu)化問題求解,引入了壁壘函數和改進的RSSA算法。新方法的求解性能優(yōu)于傳統(tǒng)的遺傳算法。仿真結果表明,算法通過合理的參數配置,不僅極大地提高了D最優(yōu)設計求解的計算效率,并且得到的最優(yōu)軌跡適應度值精度要好于傳統(tǒng)的遺傳算法。在平臺連續(xù)旋轉最優(yōu)軌跡設計結果的基礎上,對連續(xù)翻滾自標定試驗的誤差辨識方法進行研究。在之前建立的基于ψ的系統(tǒng)誤差模型基礎上,引入余弦變換矩陣,得到加速度計測量誤差的垂直分量作為新息對加速度計進行辨識,加速度誤差的水平分量作為新息對陀螺儀進行辨識,提出了雙卡爾曼濾波的辨識方法,將陀螺儀和加速度計通過解耦分開辨識,通過仿真結果驗證了雙卡爾曼濾波辨識方法的有效性。
文內圖片：
圖片說明：遺傳算法仿真圖
[Abstract]:The calibration technology at the system level is very different from the test of a single instrument in the laboratory. After some error coefficients of the instrument are installed in the system, they often change with the change of environmental conditions and need to be re-calibrated. The inertial navigation platform system based on inertial stabilization platform contains a frame system with three rotating degrees of freedom, which enables it to realize self-calibration without relying on external test equipment or benchmark. Compared with the multi-position rolling test method, which is widely used at present, the continuous rolling test method is not only able to make full use of all the observed information in the rolling process, identify more error coefficients, but also has higher calibration accuracy and simple and efficient testing process because the platform has been in a servo working state. However, the error model equation and the experimental design process are also very complex. In this paper, the design and identification of continuous roll self-calibration test for inertial navigation platform system are studied. Firstly, a total of 30 error term coefficients of accelerometer and gyroscope are selected, and the equation of state and observation equation are established by using 蠁 angle and accelerometer output, respectively. In order to improve the observable measure of the system error parameters, the optimal rotation trajectory of the platform in the continuous rolling test is obtained, and the corresponding mathematical expression is obtained by using the D optimization test design method. Through proper mathematical description and engineering simplification, the design problem of optimal continuous rotation trajectory is transformed into the optimal control problem. In order to solve the problem of optimal experimental design, the global intelligent optimization algorithm is used to solve the problem. At the same time, in order to improve the computational efficiency and accuracy of the optimal trajectory, the nonlinear constrained optimization problem is transformed into an unconstrained optimization problem, and the barrier function and the improved RSSA algorithm are introduced. The performance of the new method is better than that of the traditional genetic algorithm. The simulation results show that the algorithm not only greatly improves the computational efficiency of D optimal design and solution, but also improves the accuracy of the optimal trajectory fitness value better than the traditional genetic algorithm through reasonable parameter configuration. Based on the design results of the optimal trajectory of continuous rotation of the platform, the error identification method of continuous roll self-calibration test is studied. On the basis of the previous systematic error model based on 蠁, the cosine transform matrix is introduced, and the vertical component of accelerometer measurement error is used as innovation to identify the accelerometer, and the horizontal component of acceleration error is used as innovation to identify the gyroscope. The identification method of double Kalman filter is put forward. The gyroscope and accelerometer are identified separately by decoupling. The effectiveness of the double Kalman filter identification method is verified by the simulation results.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN96

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本文編號：2512120