p-c-a

一种未知输入/输出的解耦方法，可用于控制器的设计、故障诊断系统设计等(An unknown input/output decoupling method can be used for controller design, fault diagnosis system design)

基于MATLAB的BUCK电路的仿真，内环是电流环，用无源控制，外环电压环，用PI控制(Based on the passive control of MATLABBUCK circuit)

This is a collection of simulink blocks implementing simple nonlinear dynamical systems that generate attractors. Two types of general 3D simulink scopes (coded in the s-functions sfunxyz.m and sfunxyzd.m) are used to display in 3D, the evolution of the system during the simulation. Currently (March 2007), the featured systems are Lorenz, Van Der Pol, simple Lotka-Volterra, competitive N-D Lotka Volterra and Henon.

遗传算法（Genetic Algorithm，GA）是通过对自然界中生物的遗传和优胜劣汰的进化过程进行模拟与抽象，进而形成的一种自适应全局随机优化搜索方法。遗传算法只需提供目标函数作为寻优信息，它从某一随机生成的初始群体出发，经过选择、交叉和变异等遗传操作后对个体进行适应度评价，保留适应度较强的个体遗传到子代种群中，经过多次的迭代计算求得最优个体，即问题的最优解。本程序采用遗传算法可求解微网优化运行。(Genetic Algorithm is an adaptive global by nature, genetic and biological survival of the fittest evolutionary process simulation and abstract, and thus the formation of stochastic optimization search method. Just provide genetic algorithm optimization objective function as an information, which is generated from a random initial population of departure, after genetic manipulation, such as selection, crossover and mutation of individuals fitness evaluation, retained a strong individual genetic fitness to child on behalf of the population, after several iterations of computing the optimal solution to get the optimal individual, that is the problem. This program uses a genetic algorithm to solve the optimal operation of the micro-network.)

Taflove的FDTD书的附带程序一维FDTD算法源代码！(Taflove the FDTD program book with one-dimensional FDTD algorithm source code!)

离散正交多项式拟合例子，可自定义离散正交多项式次数。附有简单清晰的说明。(Examples of discrete orthogonal polynomial fitting)

rotordynamics simulation example

提供大量matlab的源代码，供初学者学习掌握(Provide a large number of matlab source code, for beginners to learn to master)

Boost converter for voltage boost and buck.

对模型预测控制原理的详细讲解，强烈推荐新手如门学习(description for MPC in detail)