絞吸式挖泥船.doc
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絞吸式挖泥船,摘要挖泥船是現(xiàn)代疏浚工程中最常用的疏浚工具之一，而其中的將土壤挖掘和輸送一次性完成，具有較高的工作效率和廣泛的適應(yīng)性，但是有設(shè)備投資高、工作時間長等缺點(diǎn)，并且其實(shí)際疏浚過程系統(tǒng)動態(tài)特性極其復(fù)雜，這就使得的疏浚優(yōu)化變得非常重要。傳統(tǒng)優(yōu)化方法是一種離線的靜態(tài)優(yōu)化方法，它是根據(jù)設(shè)備參數(shù)進(jìn)行...
內(nèi)容介紹
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摘 要
挖泥船是現(xiàn)代疏浚工程中最常用的疏浚工具之一，而其中的絞吸式挖泥船將土壤挖掘和輸送一次性完成，具有較高的工作效率和廣泛的適應(yīng)性，但是絞吸式挖泥船有設(shè)備投資高、工作時間長等缺點(diǎn)，并且其實(shí)際疏浚過程系統(tǒng)動態(tài)特性極其復(fù)雜，這就使得絞吸式挖泥船的疏浚優(yōu)化變得非常重要。
傳統(tǒng)優(yōu)化方法是一種離線的靜態(tài)優(yōu)化方法，它是根據(jù)設(shè)備參數(shù)進(jìn)行離線理論計算來確定最優(yōu)的作業(yè)控制量，但因作業(yè)環(huán)境是時變的，故這種離線優(yōu)化方法精度很低，針對于此，本文研究了一種能夠?qū)崿F(xiàn)作業(yè)產(chǎn)量和動作規(guī)劃優(yōu)化的在線作業(yè)方法。
該優(yōu)化方法的核心包括兩個方面：策略優(yōu)化與系統(tǒng)控制。策略優(yōu)化是根據(jù)在線檢測的作業(yè)參數(shù)和設(shè)備信息建立了一個策略中心，該策略中心以產(chǎn)量最大化為目標(biāo)，疏浚過程泥漿濃度和流速所受限制為約束建立了優(yōu)化策略模型，并通過知識處理將優(yōu)化歸結(jié)為非線性規(guī)劃問題，并采用遺傳算法來求解，以得到目前環(huán)境下使產(chǎn)量最大化的濃度和流速；系統(tǒng)控制主要是指對濃度和流速的穩(wěn)定控制，它以策略中心得到的濃度和流速工藝值為期望值，送到各自的控制系統(tǒng)，濃度控制系統(tǒng)采用的是遞推最小二乘法和最小方差控制結(jié)合的最小方差自校正控制器，流速控制系統(tǒng)采用的是單神經(jīng)元網(wǎng)絡(luò)和PID控制結(jié)合的單神經(jīng)元PID控制器。
該優(yōu)化方法能夠?qū)崟r檢測挖泥船所處環(huán)境的參數(shù)，從而得到目前情況下使產(chǎn)量最大的最佳控制量，并送到控制系統(tǒng)進(jìn)行優(yōu)化控制。最后本文以900型絞吸式挖泥船為基礎(chǔ)通過計算機(jī)仿真的方法測試優(yōu)化方法的性能，取得了滿意的效果。
關(guān)鍵詞 疏浚；遺傳算法；最小方差自校正控制；單神經(jīng)元PID控制
Abstract
Dredger was one of the most commonly used dredging tool in the modern dredge project,The Cutter suction dredger that one of it can excavate and transport the soil at same time, so it had high efficiency and a widely range of adaptability. But it’s equipment investment was high and it’s work time was long,also the dynamic characteristics in the actual dredging process was extremely complex,all thouse made the optimization of Cutter suction dredger to particularly important.
The conventional optimization method was off-line,it carried though the theory calculate accordding to the equipment parameter, to catch the best optimizate control value,but the operating environment changed every moment,so it’s productivity was very low,for thouse,this paper reaserch a online-optimize method which can optimize the dredge output and programme the dredge actions.
The core of the optimizate method contained two aspects: the strategy optimization and the system control.According to the parameters that measured online and the equipment informations, the strategy optimization made a strategy center,it took the maximum dredge output as the target,used the limit of the concentration and the flow of velocity for the restrict.established a optimize strategy model,and turn the optimization to a nonlinear programming problem, we used the genetic algorithm to solve it , got the value of concentration also the flow of velocity.The system control was a stability control that mainly for the concentration and the flow of velocity.it took the values of the concentration and the flow of velocity that the strategy center calculated as the expect value,and send them to the control system,the concentration control system used the Minimum variance self-tuning controller which united the Recursive least squares algorithm and the Minimum variance control. the flow of velocity control system used the single-neuron network PID controller which united neural network and the conventional PID control.
This optimizate method can measure the real-time parameters, catch the best control value quickly, send them to the control system to operate optimizate control. Finally,we tested the control method’s performance based on the 900 number cutter suction dredger by computer simulink, and the results were satisfy.
Key Words Dredge; Genetic algorithm; Minimum variance self-tuning control; Single- neural network PID control
目 錄
摘 要 I
Abstract II
第1章 緒論 1
1.1 課題研究背景 1
1.1.1 絞吸式挖泥船的現(xiàn)狀與發(fā)展 4
1.1.2 絞吸式挖泥船的原理介紹 5
1.2 疏浚優(yōu)化的現(xiàn)狀 7
1.3 本文研究的主要內(nèi)容 7
第2章 絞吸式挖泥船疏浚數(shù)學(xué)模型的建立 9
2.1 疏浚過程數(shù)學(xué)模型 9
2.1.1 各模型之間聯(lián)系 9
2.1.2 電動機(jī)—絞刀模型 10
2.1.3 泥泵---柴油機(jī)模型 15
2.1.4 管路模型 18
2.2 優(yōu)化策略數(shù)學(xué)模型 21
2.2.1 影響優(yōu)化的因素 21
2.2.2 工況點(diǎn)的研究 22
2.2.3 優(yōu)化策略模型的建立 22
2.3 本章小結(jié) 24
第3章 優(yōu)化策略的研究 25
3.1 遺傳算法簡介 25
3.2 遺傳算法的流程 26
3.2.1 參數(shù)編碼 26
3.2.2 初始群體的設(shè)定 27
3.2.3 適應(yīng)度函數(shù)的設(shè)計 28
3.2.4 遺傳操作的設(shè)計 30
3.2.5 控制參數(shù)設(shè)定 32
3.3 遺傳算法的改進(jìn) 34
3.4 優(yōu)化控制策略仿真 36
3.5 本章小結(jié) 38
第4章 疏浚過程控制 39
4.1 控制對象與控制方案 39
4.2 基于遺傳算法的控制策略 40
4.3 流速的控制 40
4.3.1 引言 40
4.3.2 單神經(jīng)元PID控制器 42
4.3.3 流速控制系統(tǒng)仿真 44
4.4 濃度的控制 49
4.4.1 引言 49
4.4.2 最小方差自校正控制器 49
4.4.3 濃度控制系統(tǒng)仿真 50
4.5 流速和濃度的相互影響 53
4.5.1 不加控制器與控制決策的情況..
挖泥船是現(xiàn)代疏浚工程中最常用的疏浚工具之一，而其中的絞吸式挖泥船將土壤挖掘和輸送一次性完成，具有較高的工作效率和廣泛的適應(yīng)性，但是絞吸式挖泥船有設(shè)備投資高、工作時間長等缺點(diǎn)，并且其實(shí)際疏浚過程系統(tǒng)動態(tài)特性極其復(fù)雜，這就使得絞吸式挖泥船的疏浚優(yōu)化變得非常重要。
傳統(tǒng)優(yōu)化方法是一種離線的靜態(tài)優(yōu)化方法，它是根據(jù)設(shè)備參數(shù)進(jìn)行離線理論計算來確定最優(yōu)的作業(yè)控制量，但因作業(yè)環(huán)境是時變的，故這種離線優(yōu)化方法精度很低，針對于此，本文研究了一種能夠?qū)崿F(xiàn)作業(yè)產(chǎn)量和動作規(guī)劃優(yōu)化的在線作業(yè)方法。
該優(yōu)化方法的核心包括兩個方面：策略優(yōu)化與系統(tǒng)控制。策略優(yōu)化是根據(jù)在線檢測的作業(yè)參數(shù)和設(shè)備信息建立了一個策略中心，該策略中心以產(chǎn)量最大化為目標(biāo)，疏浚過程泥漿濃度和流速所受限制為約束建立了優(yōu)化策略模型，并通過知識處理將優(yōu)化歸結(jié)為非線性規(guī)劃問題，并采用遺傳算法來求解，以得到目前環(huán)境下使產(chǎn)量最大化的濃度和流速；系統(tǒng)控制主要是指對濃度和流速的穩(wěn)定控制，它以策略中心得到的濃度和流速工藝值為期望值，送到各自的控制系統(tǒng)，濃度控制系統(tǒng)采用的是遞推最小二乘法和最小方差控制結(jié)合的最小方差自校正控制器，流速控制系統(tǒng)采用的是單神經(jīng)元網(wǎng)絡(luò)和PID控制結(jié)合的單神經(jīng)元PID控制器。
該優(yōu)化方法能夠?qū)崟r檢測挖泥船所處環(huán)境的參數(shù)，從而得到目前情況下使產(chǎn)量最大的最佳控制量，并送到控制系統(tǒng)進(jìn)行優(yōu)化控制。最后本文以900型絞吸式挖泥船為基礎(chǔ)通過計算機(jī)仿真的方法測試優(yōu)化方法的性能，取得了滿意的效果。
關(guān)鍵詞 疏浚；遺傳算法；最小方差自校正控制；單神經(jīng)元PID控制
Abstract
Dredger was one of the most commonly used dredging tool in the modern dredge project,The Cutter suction dredger that one of it can excavate and transport the soil at same time, so it had high efficiency and a widely range of adaptability. But it’s equipment investment was high and it’s work time was long,also the dynamic characteristics in the actual dredging process was extremely complex,all thouse made the optimization of Cutter suction dredger to particularly important.
The conventional optimization method was off-line,it carried though the theory calculate accordding to the equipment parameter, to catch the best optimizate control value,but the operating environment changed every moment,so it’s productivity was very low,for thouse,this paper reaserch a online-optimize method which can optimize the dredge output and programme the dredge actions.
The core of the optimizate method contained two aspects: the strategy optimization and the system control.According to the parameters that measured online and the equipment informations, the strategy optimization made a strategy center,it took the maximum dredge output as the target,used the limit of the concentration and the flow of velocity for the restrict.established a optimize strategy model,and turn the optimization to a nonlinear programming problem, we used the genetic algorithm to solve it , got the value of concentration also the flow of velocity.The system control was a stability control that mainly for the concentration and the flow of velocity.it took the values of the concentration and the flow of velocity that the strategy center calculated as the expect value,and send them to the control system,the concentration control system used the Minimum variance self-tuning controller which united the Recursive least squares algorithm and the Minimum variance control. the flow of velocity control system used the single-neuron network PID controller which united neural network and the conventional PID control.
This optimizate method can measure the real-time parameters, catch the best control value quickly, send them to the control system to operate optimizate control. Finally,we tested the control method’s performance based on the 900 number cutter suction dredger by computer simulink, and the results were satisfy.
Key Words Dredge; Genetic algorithm; Minimum variance self-tuning control; Single- neural network PID control
目 錄
摘 要 I
Abstract II
第1章 緒論 1
1.1 課題研究背景 1
1.1.1 絞吸式挖泥船的現(xiàn)狀與發(fā)展 4
1.1.2 絞吸式挖泥船的原理介紹 5
1.2 疏浚優(yōu)化的現(xiàn)狀 7
1.3 本文研究的主要內(nèi)容 7
第2章 絞吸式挖泥船疏浚數(shù)學(xué)模型的建立 9
2.1 疏浚過程數(shù)學(xué)模型 9
2.1.1 各模型之間聯(lián)系 9
2.1.2 電動機(jī)—絞刀模型 10
2.1.3 泥泵---柴油機(jī)模型 15
2.1.4 管路模型 18
2.2 優(yōu)化策略數(shù)學(xué)模型 21
2.2.1 影響優(yōu)化的因素 21
2.2.2 工況點(diǎn)的研究 22
2.2.3 優(yōu)化策略模型的建立 22
2.3 本章小結(jié) 24
第3章 優(yōu)化策略的研究 25
3.1 遺傳算法簡介 25
3.2 遺傳算法的流程 26
3.2.1 參數(shù)編碼 26
3.2.2 初始群體的設(shè)定 27
3.2.3 適應(yīng)度函數(shù)的設(shè)計 28
3.2.4 遺傳操作的設(shè)計 30
3.2.5 控制參數(shù)設(shè)定 32
3.3 遺傳算法的改進(jìn) 34
3.4 優(yōu)化控制策略仿真 36
3.5 本章小結(jié) 38
第4章 疏浚過程控制 39
4.1 控制對象與控制方案 39
4.2 基于遺傳算法的控制策略 40
4.3 流速的控制 40
4.3.1 引言 40
4.3.2 單神經(jīng)元PID控制器 42
4.3.3 流速控制系統(tǒng)仿真 44
4.4 濃度的控制 49
4.4.1 引言 49
4.4.2 最小方差自校正控制器 49
4.4.3 濃度控制系統(tǒng)仿真 50
4.5 流速和濃度的相互影響 53
4.5.1 不加控制器與控制決策的情況..
