MPC solved a constrained convex quadratic optimization by defining reference trajectories, constraint limits, prediction horizon, control horizon and weighting matrices.
通过设定参考轨迹、输入输出约束、控制步长、预测步长及加权矩阵,解决了系统的凸二次型优化问题。
参考来源 - 变风量空调系统的模型预测控制及仿真研究·2,447,543篇论文数据，部分数据来源于NoteExpress
In the classical linear quadratic problems, weighting matrices are usually choosed on trial and error to get good responses.
在传统的线性二次型问题中，一般是通过试错法选择加权矩阵来获得良好的动态响应。
The analytical relation among the weighting matrices and open loop and optimal closed-loop characteristic polynomials is derived.
推导出加权矩阵与开环、最优闭环特征多项式系数之间的解析关系式。
The design procedure is as simples as a conventional optimal regulator, but the problem of choosing weighting matrices can be avoided.
在算法上它与规范的最优调节器问题的算法一样简单、但却避免了选择加权阵的麻烦。
应用推荐