摘 要II无人飞艇的路径规划研究摘无人飞艇的路径规划研究摘 要要无人飞艇的路径规划问题是指在特定的约束条件下, 寻找飞艇从初始点到目标点满足某种性能指标最优的运动轨迹，从某种意义上讲,路径规划问题就是使明确的目标与各种限制函数相匹配的最优化问题，其中完整、准确的飞艇运动模型是无人飞艇路径规划问题需要考虑的最重要的限制条件，其次，还有控制量的变化界限也是极其重要的约束条件。基于运动模型和控制量变化的共同约束作用，选择适当的路径规划算法，求取性能目标函数的最小值，得到描述飞艇运动空间位置的轨迹曲线和控制量的变化曲线。飞艇是一种不同于常规飞行器的飞行器，运动情况比较复杂，有自身的运动特点，建立完整、准确的非线性模型是研究无人飞艇的路径规划问题的前提条件，遗传算法为解决非线性系统的路径规划问题提供了有力的应用前景，在模型线性化理论的基础上，最优控制理论中的最小值原理也为解决无人飞艇的路径规划问题提供了便利。这些研究方法，是解决无人飞艇的路径规划问题的大胆尝试，对实际情况下飞艇的路径规划具有一定的参考意义，也对研究飞艇运动的制导律和运动轨迹的跟踪控制技术具有一定的实际指导意义。概括来说，本文主要的研究工作有以下几个方面：1.首先介绍了飞艇发展的历史和现状，给出了无人飞艇路径规划的目的、意义以及研究现状，指出建立飞艇运动完整准确的非线性摘 要III模型是研究无人飞艇路径规划问题的基础，从无人飞艇的建模技术的发展现状出发剖析了建立非线性模型过程中会遇到的难点和问题。2.由于飞艇与无人机、机器人在结构、运动特性方面的相似性和异同点，借鉴已经被广泛研究的无人机和机器人的路径规划问题的解决算法，针对无人飞艇路径规划问题的特殊性注重飞艇自身的运动特性，不必考虑对飞艇周围环境的建模分析，提出了无人飞艇的路径规划问题的解决算法基于非线性模型的遗传算法和基于模型线性化后的最小值原理。3.由于缺乏建模实验数据，本文采用机理分析法，利用一定的假设理想条件，具体分析飞艇运动过程中的受力情况，考虑附加质量、推力矢量等因素影响，在机体坐标系中，根据动量定理和动量矩定理写出飞艇运动的动力学和运动学方程，得到了描述飞艇运动完整的六自由度非线性模型。4.对于飞艇运动初始点和目标点在空间有一定高度差的路径规划问题，选择飞艇运动的非线性模型作为约束限制条件，路径规划问题的性能目标函数是运动过程中能量、时间消耗量的最小值。基于权重系数法将双目标优化问题转化为单目标优化问题，把时间区间等份化，在各小段时间区间内用线性函数、正余弦函数分别表示各控制量的变化规律，采用遗传算法和单步 Runge-Kutta 法求取控制量和飞艇空间位置变化的轨迹曲线。在 Matlab 遗传算法工具箱下进行多次仿真，仿真结果说明：增加时间区间的等份数可以改善路摘 要IV径规划问题的解。5.对于飞艇运动的非线性模型，利用传统的线性化方法小干扰法将模型线性化，线性化的前提是把飞艇的运动看成是纵向的基准运动和横侧向的扰动运动，由于采用的是线性化后的运动模型，来源于最优控制理论的最小值原理成为了解决路径规划问题的比较便利的方法，这时的性能目标函数是从初始点到目标点运动过程中能量消耗的最小值。利用 Matlab 软件平台编写仿真程序，实现了在空间中不同高度上的路径规划，论证了运用最小值原理解决路径规划问题的合理性。关键词：无人飞艇，路径规划，遗传算法，最小值原理，模型：无人飞艇，路径规划，遗传算法，最小值原理，模型ABSTRACTVResearch on Path Planning of an Unmanned AirshipABSTRACTThe problem on path planning of an unmanned airship is to find outoptimum trajectory from start point to destination point under certainlimitation and performance index. In a way, the above problem is also anoptimunm problem to match definite destination with some limitationfunction. In the first place, intact and correct flighting model is the mostimportant limitation factor. In the second place, the boundary ofcontrolled variables is also restriction term. So that, the solving on pathplanning of an unmanned airship can acquire the plots of flightingtrajectories in the air and controlled variables after getting the minimumvalue of performance index objective function with the help of some pathplanning algorithms.Airship is different from usual flying vehicle with complex motionand particular characters. Intact and correct flighting nonlinear model isthe premise condition. Genetic algorithm owns potential applicationprospect. Based on linearization of the nonlinear model, minimizeABSTRACTVIprinciple may simplify the solving of path planning problem. Theseresearch methods are brave attempt to solve the path planning problemand are guided to study the guidance laws and tracking the desiredtrajectories. In summarization, the thesis mainly contains some task in afew fields below.1. At first the development history about airship and the purpose on pathplanning on unmanned airship are introduced.The thesis points out anidea that intact and correct flighting nonlinear model is the base tosolve the path planning problem on unmanned airship.Besides, themethod and difficulties in builting nonlinear model is analysized.2. Becaused of similarities and differences with unmanned vehicle androbot in structure and motion nature，the thesis borrows some solvingideas from path planning on unmanned vechile to introduce a few pathplanning algorithm. Two solving algorithsgenetic algorithm onnonlinear model and minimize principle on simplified linear model,are extracted to solve the path planning problem on unmanned airshipagainst the special characters of unmanned airship.3. Being short of experiment data, the thesis uses mechanics method tobuild intact six-freedom nonlinear model with twelve state variablesand three controlled variables in terms of momentum theorem andmoment of momentum theorem in mechanics coordinate system. Inthe course, the thesis adopts assumption, takes some considerationABSTRACTVIIabout additive mass and thrust force vector to analysize the physicalforces.4. As to the path planning of the problem about altitude difference fromthe start point to destination point, The nonlinear model is choosed tobecome main limitation. Genetic algorithm and Runge-Kutta methodare used to get the plots of flighting trajectories in the air andcontrolled variables. The performance function is minimal value ofcombined consumption of energy and time from the start point todestination point. Multiply objective optimization is converted to oneobjective. Each controlled variable is equal to linear function, sinefunction and cosine function seperately in the same time span.GAtoolbox in Matlab is used to realize simulation, which shows thatincreasing the number of time span can better the solving of pathplanning.5. The nonlinear model may be linearized on the condition of littledisturbance, and divided into longitudinal and lateral motion.Minimize principle is adopted to slove the path planning. Under thiscondition the performance function i