模型预测控制.m函数代码

研究quickfast/quickfix/mfast，生成vs工程文件sln需要的工程管理的MPC包

永磁同步电机（PMSM）无刷直流电机（BLDC）有限集模型预测控制（MPC）matlab/simulink仿真模型（2017），能很好的运行，并且能达到比较理想的效果，比传统的PWM控制要精确，反应速度更快。

In this thesis we consider the problem of designing and implementing Model Predictive Controllers (MPC) for stabilizing the dynamics of an autonomous ground vehicle. For such a class of systems, the non-linear dynamics and the fast sampling time limit the real-time implementation of MPC algorithms to local and linear operating regions. This phenomenon becomes more relevant when using the limited computational resources of a standard rapid prototyping system for automotive applications. In this thesis we first study the design and the implementation of a nonlinear MPC controller for an Active Font Steering (AFS) problem. At each time step a trajectory is assumed to be known over a finite horizon, and the nonlinear MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We demonstrate that experimental tests can be performed only at low vehicle speed on a dSPACE rapid prototyping system with a frequency of 20 Hz. Then, we propose a low complexity MPC algorithm which is real-time capable for wider operating range of the state and input space (i.e., high vehicle speed and large slip angles). The MPC control algorithm is based on successive on-line linearizations of the nonlinear vehicle model (LTV MPC). We study performance and stability of the proposed MPC scheme. Performance is improved through an ad hoc stabilizing state and input constraints arising from a careful study of the vehicle nonlinearities. The stability of the LTV MPC is enforced by means of an additional convex constraint to the finite time optimization problem. We used the proposed LTV MPC algorithm in order to design AFS controllers and combined steering and braking controllers. We validated the proposed AFS and combined steering and braking MPC algorithms in real-time, on a passenger vehicle equipped with a dSPACE rapid prototyping system. Experiments have been performed in a testing center equipped with snowy and icy tracks. For both controllers we showed that vehicle stabilization can be achieved at high speed (up to 75 Kph) on icy covered roads. This research activity has been supported by Ford Research Laboratories, in Dearborn, MI, USA.

MATLAB WITH MPC SPRINGER 书籍 国内自身

本资源包含三个函数 Main.m为主函数，包含给定轨迹、MPC参数设置和初始状态 fun_trajforship.m为轨迹计算函数，根据Main.m中设定的轨迹以及采样周期，计算出每个采样周期对应的轨迹数值 nmpc_m.m为模型预测控制器函数 图片为欠驱动船舶动力学模型（在Main.m函数中用system描述）

广义预测控制作为一种新型的远程预测控制方法，集多种算法的优点为一体，具有较好的性能，受到人们的重视，现有多种修正算法，大体上可分为显式算法和隐式算法两种，显式算法是先辨识对象模型参数，然后利用Diophantine方程作中间运算，最后得到控制律参数，由于要作多步预测, 就必须多次求解 Diophantine方程，因要经过繁琐的中间运算，所以计算工作量较大，占线时间太长。隐式算法，它不辨识对象模型参数，而是根据输入输出数据直接辨识求取最优控制律中的参数, 因而避免了在线求解Diophantie方程所带来的大量中间运算，减少了计算工作量，节省了时间

本文为一类连续时间非线性不确定系统提出了一种在线性矩阵不等式框架下设计鲁棒模型预测控制。这个控制器设计是用“最坏情况”目标函数在无限时间滚动窗口下的最优控制问题。一个充分的状态反馈综合条件是提供LMI的优化形式并且在每一个时间步上都被在线解决。一个仿真例子显示了提出的方法的效果

博客中对应的MATLAB算法程序，https://blog.csdn.net/qq_35379989/article/details/115265010?spm=1001.2014.3001.5501

控制算法MPC算法讲解，另外还包含了自动驾驶学习资料的获取： 涵盖感知，规划和控制，ADAS，传感器； 1. apollo相关的技术教程和文档； 2.adas（高级辅助驾驶）算法设计（例如AEB,ACC,LKA等） 3.自动驾驶鼻祖mobileye的论文和专利介绍 4.自动驾驶学习笔记 5.自动驾驶专项课程（可能是目前最好的自动教师教程），是coursera上多伦多大学发布的自动驾驶专项课程，应该是目前为止非常火非常好的教程了，包含视频，ppt，论文以及代码 6.国家权威机构发布的adas标准，这是adas相关算法系统的标准，也是开发手册。 7.规划控制相关的算法论文介绍 8. 控制理论及应用实践