瞬态动力学分析

对连续波雷达系统从数据建模到后续处理最后检测目标(For continuous wave radar system, from data modeling to subsequent processing, final detection target.)

说明：  MATLAB OFDM仿真代码，采用QPSK调制(MATLAB OFDM simulation code, using QPSK modulation)

说明：  针对带储能的光伏系统出力不稳定问题，基于储能变流器提出了一种带有灰色补偿的Back-stepping控制方法，以实现对并网点上层规划层面有功功率P和无功功率Q指令的跟踪控制，同时保证并网点电压的 稳定。(Aiming at the problem of output instability of photovoltaic system with energy storage, a back stepping control method with gray compensation is proposed based on energy storage converter to realize the tracking control of active power P and reactive power Q command at the upper planning level of the parallel network, and to ensure the stability of the voltage of the parallel network.)

基于matlab 编程，利用一维圣维南方程组，模拟洪水演进过程(Using one-dimensional Saint-Venant equations to simulate flood routing process)

说明：  三相桥式全控整流电路仿真，使用晶闸管进行三相桥式全控整流电路仿真。(Three-Phase Full-Bridge Controlled Rectifier)

光纤存在非线性效应，SPM、XPM、FWM，本算法为啁啾高斯光束在光线中传播的波形变化，采用了分步傅里叶法(simulation of fiber nonlinearity)

对地面固定目标设计了一种仅依赖测距传感器的制导律，不再需要传统的视线角信号以及目标和无人机自身的定位信息，再将该制导律推广到对地面匀速和变速移动目标的跟踪制导。相比于现有制导律，本课题提出的制导律结构更为简洁，仅有一个设计参数，并且制导策略更为合理。仿真结果表明所提出的制导律能够实现无人机对地面固定目标的稳定跟踪。(A guidance law that only depends on the ranging sensor is designed for the ground fixed target. The traditional line-of-sight signal and the target and the positioning information of the UAV are no longer needed. The guidance law is extended to the uniform and variable speed moving target Tracking guidance. Compared with the existing guidance law, the guidance law structure proposed by this subject is more concise, with only one design parameter, and the guidance strategy is more reasonable. The simulation results show that the proposed guidance law can achieve stable tracking of UAV fixed targets on the ground.)

直流配网模型，非常基础的直流配网模型，便于对直流配网的基本掌握和学习(DC distribution network model, very basic DC distribution network model, easy to grasp and learn the basic DC distribution network)

说明：  使用python语言实现复杂网络SIR和SIS模型编程(SIR AND SIS model with python language)

说明：  混合粒子群求解TSP问题，重新定义了离散粒子群算法DPSO的速度和位置公式,使其适宜求解离散问题.针对DPSO易早熟,收敛慢的缺陷,建立局部极小区域的扰动机制,在结合局部搜索算法PSEC后,提出了一种混合离散粒子群算法HDPSO.(Hybrid particle swarm optimization for TSP)