飞行模拟器数字飞行控制系统建模与辅助训练的实现研究
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摘要
飞行模拟器能够在地面逼真再现飞机的空中飞行,是航空领域新机研制和飞行训练不可或缺的重要设备。我国在飞行模拟器的研制和技术储备上均远远落后于国外发达国家,在当前我国民航运输业高速发展和大飞机研制项目的背景下,开展模拟器关键技术研究更具重要意义。数字飞行控制系统作为民航飞机关键的子系统,在全自动航迹控制及协助航迹控制方面发挥重要作用,对其进行高质量的模拟是研制高等级模拟器的必然要求。不仅如此,数字飞行控制系统的协助航迹控制功能可被加以扩展,进而在飞行模拟器中实现辅助教员进行各种训练科目示范飞行的辅助训练功能,提高飞行模拟器的训练效能,解决目前在模拟器训练中暴露出的问题。
为实现上述目标,首先对数字飞行控制系统的建模进行了研究。通过对系统的功能、组成及工作原理的分析,将整个系统划分为系统接口、逻辑处理、倾斜控制律和俯仰控制律四个功能模块。针对系统运行逻辑的高度复杂性,通过合理设置逻辑变量,提出一种快速列写系统逻辑表达式的方法,提高了系统开发效率。针对数字飞行控制系统的工作特点,基于飞行控制系统的典型结构,以姿态指令计算为中间环节建立了一个能够满足多飞行方式需求的控制结构,在此结构下,结合每一飞行方式的实际要求给出了相应的衔接准则和控制律算法,满足了飞行模拟器的训练要求。
为在模拟器训练时能够快速准确的进行重定位及回转等操作,提高训练效率和效果,并在辅助训练中为控制器提供正确的初始条件,对飞机定常飞行状态的求解问题进行了研究。飞机定常状态作为最理想的仿真初始状态在模拟器训练中被广泛使用,但现有的定常状态求解方法准确度不高,求解过程中容易陷入局部最优以致求解失败,极大的影响了训练质量。因此提出一种新的混合遗传算法用于飞机定常状态的求解,该算法将现有遗传算法中的拉马克学习机制和鲍德温学习机制有效结合,使个体学习即局部搜索的次数在两种学习机制间合理的动态分配,提高了局部搜索资源的利用率。经实际测试,该算法有利于避免陷入局部最优、减少迭代次数并提高了最终结果的精度。
数字飞行控制系统虽然具备了在正常情况下的飞行控制能力,但其在特殊情形下对飞行的控制是无法满足辅助训练要求的。为了能够协助甚至替代教练员进行飞行训练,对辅助训练的飞行决策方法展开了研究。通过对飞行决策的分析,将决策知识转化为一组根据飞行信息进行飞行方式转换的规则,采用基于规则推理的方法实现飞行模拟器辅助训练的自主决策行为。为保证规则推理的有效性,需要对飞行模式进行准确识别。对此,构建了一种双隐层小波过程神经网络用于飞行模式识别,小波激励函数改善了网络的收敛性,非时变神经元隐层的引入提高了网络的非线性映射能力和知识存储能力。基于规则推理和飞行模式识别的飞行决策方法赋予了数字飞行控制系统一定的自主飞行能力,满足了辅助训练的要求。
通过对整个飞行模拟器系统的组成原理、硬件构成和计算机体系结构的分析,对模拟器系统的软件结构进行了研究。从软件模块划分、类层次规划与运行机制设计三方面出发设计了一个新的飞行模拟器软件结构,该结构能够极大的提高飞行模拟器软件系统的灵活性、可扩展性、可维护性及可重用性。在该软件结构下,对所建立的数字飞行控制系统模型、飞行模式识别算法和一个完整的示范飞行过程分别进行了仿真,结果表明了数字飞行控制系统模型的正确性和合理性、飞行模式识别的准确性和模拟器辅助训练的有效性,同时也验证了基于面向对象的模拟器软件结构的适用性和优越性。
Flight simulator which is the indispensable simulation equipment for the development of new aircraft and flight training in aviation industry can safely and realistically reproduce the behaviour of aircraft flying in the air. At present, the research and technology of flight simulator in our country are far behind the oversea level. Thus, it is significant to research key technology in flight simulator under the background of rapid development of civil aviation and the large civil aircraft develop program. As a key subsystem of civil aeroplane, the digital flight control system plays a significant role in auto-control of flight trajectory and assisting control of flight path. Thus, the high fidelity simulation for the digital flight control system is an essentially requirement in the development of high level simulator. Furthermore, the digital flight control system’s function of assisting the control flight trajectory can be extended to assist instructor to demonstrate the diversified flight training, improve the training efficiency, and solve the problems discovered in training using flight simulators.
In order to achieve the above aim, the digital flight control system was modeled firstly. Based on the analysis of the function, constitution and operational principle, the whole system was divided into four parts, which are system interface, logic management, roll control and pitch control. Due to the extreme complexity of system, a method that can fast list the expression of system logic by logically setting variables was given. And this method enhances efficiency in the system development. On the basis of the characteristics of digital flight control system and its typical configuration, a control configuration to satisfy the requirement of various flight mode was built using attitude command calculation. Under this configuration, the training requirements of flight simulator is satisfied by providing corresponding link rules and control algorithm according to each flight mode.
In order to reposition and slew operations rapidly and exactly in training, enhance training efficiency and effect, provide exact initialized condition for controller in auxiliary training, this paper studied on the solving problem of steady flight state. The steady flight state, as a most perfect initialized simulation condition, is widely used. However, there are several problems in the solving algorithm for the steady flight state presently, that is, it easy to get local optimization and its precision is not enough, which may affect flight training seriously. Thus, a new mixed genetic algorithm was given for resolving steady flight state. Lamarckian learning and Baldwinina learning are combined together organically in the algorithm. The algorithm could distribute the number of the local search in the population reasonably, make the advantage of the learning into full play and make the disadvantage into inhibitory. Actual test shows that, using the proposed algorithm the local optimum is avoided, the number of iteration is reduced and accuracy of solution is improved.
Although the digital flight control system possesses the ability of flight control under normal conditions, it is insufficient to meet the auxiliary training requirements for some particular cases. In order to assist or even take the place of instructors in flight training, the flight decision making method in auxiliary training is investigated. Decision knowledge is converted into a set of flight mode transformational rules according to flight information through the analysis of flight decision. Then, decision-making in autonomous of auxiliary training in flight simulator is achieved by the method of rule based reasoning. In order to ensure validity of rule reasoning, flight mode should be identified exactly. Thus, the two hidden-layer wavelet process neural networks is built to identify flight mode. The convergence of network is improved by wavelet excited functions and the ability of the non-linear mapping and knowledge storage is enhanced by introducing the time-invariant hidden neurons. The digital flight control system possesses a certain degree of autonomous flight ability by the flight decision making method based on the rule based reasoning and flight mode identification makes. Thus, the requirement of auxiliary training is satisfied.
The software framework of flight simulator was investigated by analyzing the principles of composition, hardware constitution and computer system configuration. Based on module division, class-hierarchy plan and operating mechanism design, a new flight simulator software structure is developed. The structure can greatly increase the software system of flight simulator flexibility, expansibility and maintainability and reusability. In the software architecture,digital flight control system model, pattern recognition algorithm and a full demonstration flight process is simulated. Results show that the digital flight control system model is correct and rational, the flight pattern recognition is accurate and the auxiliary training is valid. Moreover, the applicability and superiority of the object-oriented simulator software architecture is also verified.
为实现上述目标,首先对数字飞行控制系统的建模进行了研究。通过对系统的功能、组成及工作原理的分析,将整个系统划分为系统接口、逻辑处理、倾斜控制律和俯仰控制律四个功能模块。针对系统运行逻辑的高度复杂性,通过合理设置逻辑变量,提出一种快速列写系统逻辑表达式的方法,提高了系统开发效率。针对数字飞行控制系统的工作特点,基于飞行控制系统的典型结构,以姿态指令计算为中间环节建立了一个能够满足多飞行方式需求的控制结构,在此结构下,结合每一飞行方式的实际要求给出了相应的衔接准则和控制律算法,满足了飞行模拟器的训练要求。
为在模拟器训练时能够快速准确的进行重定位及回转等操作,提高训练效率和效果,并在辅助训练中为控制器提供正确的初始条件,对飞机定常飞行状态的求解问题进行了研究。飞机定常状态作为最理想的仿真初始状态在模拟器训练中被广泛使用,但现有的定常状态求解方法准确度不高,求解过程中容易陷入局部最优以致求解失败,极大的影响了训练质量。因此提出一种新的混合遗传算法用于飞机定常状态的求解,该算法将现有遗传算法中的拉马克学习机制和鲍德温学习机制有效结合,使个体学习即局部搜索的次数在两种学习机制间合理的动态分配,提高了局部搜索资源的利用率。经实际测试,该算法有利于避免陷入局部最优、减少迭代次数并提高了最终结果的精度。
数字飞行控制系统虽然具备了在正常情况下的飞行控制能力,但其在特殊情形下对飞行的控制是无法满足辅助训练要求的。为了能够协助甚至替代教练员进行飞行训练,对辅助训练的飞行决策方法展开了研究。通过对飞行决策的分析,将决策知识转化为一组根据飞行信息进行飞行方式转换的规则,采用基于规则推理的方法实现飞行模拟器辅助训练的自主决策行为。为保证规则推理的有效性,需要对飞行模式进行准确识别。对此,构建了一种双隐层小波过程神经网络用于飞行模式识别,小波激励函数改善了网络的收敛性,非时变神经元隐层的引入提高了网络的非线性映射能力和知识存储能力。基于规则推理和飞行模式识别的飞行决策方法赋予了数字飞行控制系统一定的自主飞行能力,满足了辅助训练的要求。
通过对整个飞行模拟器系统的组成原理、硬件构成和计算机体系结构的分析,对模拟器系统的软件结构进行了研究。从软件模块划分、类层次规划与运行机制设计三方面出发设计了一个新的飞行模拟器软件结构,该结构能够极大的提高飞行模拟器软件系统的灵活性、可扩展性、可维护性及可重用性。在该软件结构下,对所建立的数字飞行控制系统模型、飞行模式识别算法和一个完整的示范飞行过程分别进行了仿真,结果表明了数字飞行控制系统模型的正确性和合理性、飞行模式识别的准确性和模拟器辅助训练的有效性,同时也验证了基于面向对象的模拟器软件结构的适用性和优越性。
Flight simulator which is the indispensable simulation equipment for the development of new aircraft and flight training in aviation industry can safely and realistically reproduce the behaviour of aircraft flying in the air. At present, the research and technology of flight simulator in our country are far behind the oversea level. Thus, it is significant to research key technology in flight simulator under the background of rapid development of civil aviation and the large civil aircraft develop program. As a key subsystem of civil aeroplane, the digital flight control system plays a significant role in auto-control of flight trajectory and assisting control of flight path. Thus, the high fidelity simulation for the digital flight control system is an essentially requirement in the development of high level simulator. Furthermore, the digital flight control system’s function of assisting the control flight trajectory can be extended to assist instructor to demonstrate the diversified flight training, improve the training efficiency, and solve the problems discovered in training using flight simulators.
In order to achieve the above aim, the digital flight control system was modeled firstly. Based on the analysis of the function, constitution and operational principle, the whole system was divided into four parts, which are system interface, logic management, roll control and pitch control. Due to the extreme complexity of system, a method that can fast list the expression of system logic by logically setting variables was given. And this method enhances efficiency in the system development. On the basis of the characteristics of digital flight control system and its typical configuration, a control configuration to satisfy the requirement of various flight mode was built using attitude command calculation. Under this configuration, the training requirements of flight simulator is satisfied by providing corresponding link rules and control algorithm according to each flight mode.
In order to reposition and slew operations rapidly and exactly in training, enhance training efficiency and effect, provide exact initialized condition for controller in auxiliary training, this paper studied on the solving problem of steady flight state. The steady flight state, as a most perfect initialized simulation condition, is widely used. However, there are several problems in the solving algorithm for the steady flight state presently, that is, it easy to get local optimization and its precision is not enough, which may affect flight training seriously. Thus, a new mixed genetic algorithm was given for resolving steady flight state. Lamarckian learning and Baldwinina learning are combined together organically in the algorithm. The algorithm could distribute the number of the local search in the population reasonably, make the advantage of the learning into full play and make the disadvantage into inhibitory. Actual test shows that, using the proposed algorithm the local optimum is avoided, the number of iteration is reduced and accuracy of solution is improved.
Although the digital flight control system possesses the ability of flight control under normal conditions, it is insufficient to meet the auxiliary training requirements for some particular cases. In order to assist or even take the place of instructors in flight training, the flight decision making method in auxiliary training is investigated. Decision knowledge is converted into a set of flight mode transformational rules according to flight information through the analysis of flight decision. Then, decision-making in autonomous of auxiliary training in flight simulator is achieved by the method of rule based reasoning. In order to ensure validity of rule reasoning, flight mode should be identified exactly. Thus, the two hidden-layer wavelet process neural networks is built to identify flight mode. The convergence of network is improved by wavelet excited functions and the ability of the non-linear mapping and knowledge storage is enhanced by introducing the time-invariant hidden neurons. The digital flight control system possesses a certain degree of autonomous flight ability by the flight decision making method based on the rule based reasoning and flight mode identification makes. Thus, the requirement of auxiliary training is satisfied.
The software framework of flight simulator was investigated by analyzing the principles of composition, hardware constitution and computer system configuration. Based on module division, class-hierarchy plan and operating mechanism design, a new flight simulator software structure is developed. The structure can greatly increase the software system of flight simulator flexibility, expansibility and maintainability and reusability. In the software architecture,digital flight control system model, pattern recognition algorithm and a full demonstration flight process is simulated. Results show that the digital flight control system model is correct and rational, the flight pattern recognition is accurate and the auxiliary training is valid. Moreover, the applicability and superiority of the object-oriented simulator software architecture is also verified.
引文
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