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LQR
- 单级倒立摆LQR控制仿真 最优控制理论主要通过对性能的优化寻找可以使目标极小的控制器-Inverted Pendulum Control Simulation of LQR optimal control theory, mainly through the optimization of the performance objectives can find a very small controller
pendulum_system
- 倒立摆全套仿真程序，里面包含极点配置控制，LQR控制等两种控制算法的控制器，和无静差跟踪器，支持算法扩充。可用于控制算法的检验。-A whole set of simulation of a pendulum systerm, which contains controler designing. It can be used to test control algrithm.
boostson
- This the model of boost converter. The control of this converter is PID. With changing the cofficient of the P-I and D a good replay can be supplied.-This is the model of boost converter. The control of this converter is PID. With changing the coffic
truckcontrol
- 利用LQR对带两个拖车的卡车进行控制。控制能到达较好的效果。为了实时，具体的观察卡车运行的轨迹，本程序提供了简单的动画-The use of LQR trailer truck with two pairs of control. Control can reach better results. For real-time observation of a truck running on a specific trajectory, this program provides a simpl
amd
- matlab程序语言，三自由度模型对象，主动控制，lqr算法，求解位移与速度-the matlab programming languages, three degrees of freedom model objects, active control, lqr algorithm solving the displacement and velocity
mainlqr
- matlab编的lqr控制三层结构的在地震作用下的响应-matlab series LQR control the response of the three-tier structure under seismic action
chapter5
- 基于遗传算法的LQR控制器优化设计的Matlab实现-LQR controller design based on genetic algorithm optimization
LQR
- these are some matlab codes which may help to understand and design an LQR controller and solution of differential ricaati equation.
Inverted_Pendulum_Control
- 用matlab中simulink仿真，模拟对一级倒立摆实现PID，极点配置和LQR控制(Using MATLAB Simulink simulation, the PID, pole assignment and LQR control for the inverted pendulum are simulated.)
源代码
- 1 基于遗传算法的TSP算法（王辉） 2 基于遗传算法和非线性规划的函数寻优算法（史峰） 3 基于遗传算法的BP神经网络优化算法（王辉） 4 设菲尔德大学的MATLAB遗传算法工具箱（王辉） 5 基于遗传算法的LQR控制优化算法（胡斐）(1 TSP algorithm based on genetic algorithm (Wang Hui) 2 Function optimization algorithm based on genetic algorithm and non
MPC
- 模型预测控制matlab程序，与带约束优化下的LQR问题。(Model predictive control of MATLAB programs, and LQR problems with constrained optimization.)