基于模型预测控制的自航耙吸挖泥船.doc
约77页DOC格式手机打开展开
基于模型预测控制的自航耙吸挖泥船,摘要随着科技的不断进步，我国疏浚作业能力已取得长足发展，疏浚设备也随之得到更新。我国疏浚设备朝着大型化和自动化方面发展，但是对疏浚高效化方面还缺少研究，在现有的疏浚设备基础上提高疏浚性能、提升疏浚效率是我国疏浚事业亟待研究和发展的方向。基于此开展耙吸挖泥船疏浚机理研究，探讨疏浚优化工况方法，对提高挖泥船疏浚效率具有重大...
内容介绍
此文档由会员 违规屏蔽12 发布
摘 要
随着科技的不断进步，我国疏浚作业能力已取得长足发展，疏浚设备也随之得到更新。我国疏浚设备朝着大型化和自动化方面发展，但是对疏浚高效化方面还缺少研究，在现有的疏浚设备基础上提高疏浚性能、提升疏浚效率是我国疏浚事业亟待研究和发展的方向。基于此开展耙吸挖泥船疏浚机理研究，探讨疏浚优化工况方法，对提高挖泥船疏浚效率具有重大意义。
本课题从耙吸挖泥船疏浚机理出发，建立基于控制的耙吸挖泥船数学模型，通过对疏浚过程和生产效率的智能评估与分析，采用模型预测控制（MPC）技术，获取疏浚优化的最佳策略，实现在不同土质和不同疏浚装备工况条件下的优化控制。
首先在考虑土壤等因素的影响条件下，论文建立基于控制的耙吸挖泥船挖掘装舱过程的数学模型，对耙头挖掘过程和泥舱沉积过程进行了机理分析与数学建模，并采用挖泥船实测数据对模型进行验证。验证结果表明模型具有很高的准确性，可用于MPC控制设计。
其次在系统分析和研究挖泥船疏浚过程的基础上，提出一种基于模型预测控制的在线疏浚优化的方法，优化的目的是使挖泥船在完整疏浚周期内的产量最大化。MPC控制器由数学模型、目标函数和优化器三个部分构成。
疏浚优化过程是一个复杂的多系统耦合，多约束条件问题。本文将遗传算法运用于MPC控制器的优化器，使优化器能在大搜索空间中以相对较少的时间达到最优值，寻找到最佳的可控疏浚参数。然后采用“新海凤”号自航耙吸式挖泥船工程实测数据对该MPC控制器及其优化算法进行了仿真验证，并与现有控制技术进行性能比较，结果表明MPC优化方法能够使挖泥船的疏浚周期缩短10%～18%，周期产能提高10%。
最后采用LabVIEW开发了基于MPC的疏浚优化控制系统人机界面。系统根据周期产量和时间效率对疏浚性能进行评估，给出历史最优疏浚周期以及与疏浚工况条件相适应的最优可控参数，以达到疏浚产量最大化，提高挖泥船的疏浚效率和性能。
关键字：自航耙吸挖泥船；模型预测控制；疏浚优化；遗传算法；性能评估
Abstract
With the rapid development of science and technology, our dredging cause has made great progress and dredging equipment has also got updated. Great progress has made in dredging equipment in the aspect of its large scale and automation, while the high efficiency of dredging is still lack of study. On the basis of dredging equipments, the improvement of dredging performance and elevating dredging efficiency are the aspects what our dredging cause should be researched and developed. Research on the mechanism of trailing suction hopper dredger dredging and search of the optimal ways will mean to the improvement of the dredger’s efficiency of construction and the market competitiveness.
On the foot of the mechanism of trailing suction hopper dredger dredging, a control-based trailing suction hopper dredger dredging mathematics model was researched and built, and intelligent analysis and eva luation was made to dredging process and production efficiency. Model-based predicative control strategy (MPC) was adopted to obtain the best strategy for dredging optimization and to achieve dredging optimization control under different soil conditions and different dredging equipment.
Firstly, taking the soil and other factors into account, the thesis presents a mathematics model of hopper process. Besides, the thesis models and simulates drag-head and hopper and verifies these models by real measured data. The result indicates the model is of high accuracy and can be used in MPC control design.
Secondly, on the basis of system analysis and study of dredgers’ dredging process, an online dredging optimization method developed by model predictive control was brought up. The purpose of optimization is to maximize the dredgers’ production within a complete dredging cycle. MPC controller consists of three parts: mathematics model, target function and optimizer.
Dredging optimization process is a complex, multi-system coupling and a multi-constraint conditions problem. Genetic Algorithm was adapted to MPC controllers’ optimizer in the thesis. Then the optimized values, the best controllable dredging parameters can be found by optimizer in a large search room and within less time. The optimization algorithm and MPC controller were stimulated and validated, using the project data measured on “Xinhai feng” trailing suction hopper dredger, and performance contrast with updated control technology. The result shows that MPC optimization shortens the dredging time of 10% --18%, and increases dredging efficiency of 10%.
Lastly, man-machine interface is developed based on LabVIEW Software. According to cycle production and time efficiency, dredging performance was eva luated by system. Then the optimal dredging cycle and the best controllable parameters suitable to dredging conditions were given in record to maximize the high dredging production and to improve the dredging efficiency and its performance.
Key words: Trailing Suction Hopper Dredger; Model Predictive Control; Dredging Optimization; Genetic Algorithm; Performance Assessment
-b..
随着科技的不断进步，我国疏浚作业能力已取得长足发展，疏浚设备也随之得到更新。我国疏浚设备朝着大型化和自动化方面发展，但是对疏浚高效化方面还缺少研究，在现有的疏浚设备基础上提高疏浚性能、提升疏浚效率是我国疏浚事业亟待研究和发展的方向。基于此开展耙吸挖泥船疏浚机理研究，探讨疏浚优化工况方法，对提高挖泥船疏浚效率具有重大意义。
本课题从耙吸挖泥船疏浚机理出发，建立基于控制的耙吸挖泥船数学模型，通过对疏浚过程和生产效率的智能评估与分析，采用模型预测控制（MPC）技术，获取疏浚优化的最佳策略，实现在不同土质和不同疏浚装备工况条件下的优化控制。
首先在考虑土壤等因素的影响条件下，论文建立基于控制的耙吸挖泥船挖掘装舱过程的数学模型，对耙头挖掘过程和泥舱沉积过程进行了机理分析与数学建模，并采用挖泥船实测数据对模型进行验证。验证结果表明模型具有很高的准确性，可用于MPC控制设计。
其次在系统分析和研究挖泥船疏浚过程的基础上，提出一种基于模型预测控制的在线疏浚优化的方法，优化的目的是使挖泥船在完整疏浚周期内的产量最大化。MPC控制器由数学模型、目标函数和优化器三个部分构成。
疏浚优化过程是一个复杂的多系统耦合，多约束条件问题。本文将遗传算法运用于MPC控制器的优化器，使优化器能在大搜索空间中以相对较少的时间达到最优值，寻找到最佳的可控疏浚参数。然后采用“新海凤”号自航耙吸式挖泥船工程实测数据对该MPC控制器及其优化算法进行了仿真验证，并与现有控制技术进行性能比较，结果表明MPC优化方法能够使挖泥船的疏浚周期缩短10%～18%，周期产能提高10%。
最后采用LabVIEW开发了基于MPC的疏浚优化控制系统人机界面。系统根据周期产量和时间效率对疏浚性能进行评估，给出历史最优疏浚周期以及与疏浚工况条件相适应的最优可控参数，以达到疏浚产量最大化，提高挖泥船的疏浚效率和性能。
关键字：自航耙吸挖泥船；模型预测控制；疏浚优化；遗传算法；性能评估
Abstract
With the rapid development of science and technology, our dredging cause has made great progress and dredging equipment has also got updated. Great progress has made in dredging equipment in the aspect of its large scale and automation, while the high efficiency of dredging is still lack of study. On the basis of dredging equipments, the improvement of dredging performance and elevating dredging efficiency are the aspects what our dredging cause should be researched and developed. Research on the mechanism of trailing suction hopper dredger dredging and search of the optimal ways will mean to the improvement of the dredger’s efficiency of construction and the market competitiveness.
On the foot of the mechanism of trailing suction hopper dredger dredging, a control-based trailing suction hopper dredger dredging mathematics model was researched and built, and intelligent analysis and eva luation was made to dredging process and production efficiency. Model-based predicative control strategy (MPC) was adopted to obtain the best strategy for dredging optimization and to achieve dredging optimization control under different soil conditions and different dredging equipment.
Firstly, taking the soil and other factors into account, the thesis presents a mathematics model of hopper process. Besides, the thesis models and simulates drag-head and hopper and verifies these models by real measured data. The result indicates the model is of high accuracy and can be used in MPC control design.
Secondly, on the basis of system analysis and study of dredgers’ dredging process, an online dredging optimization method developed by model predictive control was brought up. The purpose of optimization is to maximize the dredgers’ production within a complete dredging cycle. MPC controller consists of three parts: mathematics model, target function and optimizer.
Dredging optimization process is a complex, multi-system coupling and a multi-constraint conditions problem. Genetic Algorithm was adapted to MPC controllers’ optimizer in the thesis. Then the optimized values, the best controllable dredging parameters can be found by optimizer in a large search room and within less time. The optimization algorithm and MPC controller were stimulated and validated, using the project data measured on “Xinhai feng” trailing suction hopper dredger, and performance contrast with updated control technology. The result shows that MPC optimization shortens the dredging time of 10% --18%, and increases dredging efficiency of 10%.
Lastly, man-machine interface is developed based on LabVIEW Software. According to cycle production and time efficiency, dredging performance was eva luated by system. Then the optimal dredging cycle and the best controllable parameters suitable to dredging conditions were given in record to maximize the high dredging production and to improve the dredging efficiency and its performance.
Key words: Trailing Suction Hopper Dredger; Model Predictive Control; Dredging Optimization; Genetic Algorithm; Performance Assessment
-b..
