MATLAB_Programming_book_ppt

Direct Adaptive Control Algorithms

Comparison of C++, Java, Python, Ruby and MATLAB Using Object Oriented Example _comparelanguages

An implementation of Unscented Kalman Filter for nonlinear state estimation. (Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system. The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF. For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a)

OKUMURA-HATA MODEL using MATLAB program.

前推后代潮流计算 把节点按不同类型定义为三类节点，方便计算(Forward offspring flow calculation node according to the different types of definitions for the three types of nodes, to facilitate the calculation)

This code is related to the course Digital Communications. It simulates a communication system which uses AWGN and 16QAM

实现了自适应信号处理RLS自适应均衡算法，比较了不同信道的学习曲线的差别(The difference learning curve to achieve the adaptive signal processing RLS adaptive equalization algorithm, compares the different channels)

自适应代码，根据论文写得，结果和论文一样，可供参考。(Adaptive code, according to papers written, and papers as a result, for reference.)

一种差分进化算法的变体，收敛性能及其优秀，大家可以参考(a variant based on differential evolution algorithm,which has a better convergence performance.)

说明：  利用在 MATLAB Simulink 中的 SimMechanics 工具箱，在 Matlab 中搭建了机器人的机构模型，结合运动学数学模型，实现的机器人的运动模拟实验，并且通过末端执行器输入运动参数与输出运动参数的对比，验证了所建立的运动学模型的正确性。最后根据机构的实际限制条件，限制了两个主动臂的最大转动角度，结合正向运动学模型，得到了整个机器人末端执行器的极限运动位置坐标以及整个运动空间(Using the SimMechanics toolbox in MATLAB Simulink, the mechanism model of the robot is built in MATLAB. Combined with the kinematics mathematical model, the motion simulation experiment of the robot is realized. The correctness of the kinematics model is verified by comparing the input and output motion parameters of the end actuator. Finally, according to the actual constraints of the mechanism, the maximum rotation angle of the two active arms is limited. Combined with the forward kinematics model, the limit motion position coordinates and the whole motion space of the end effector of the whole robot are obtained)