Firstly, it considers the simple linear model predictive control algorithms.
首先考虑简单的线性预测控制。
Two-step model predictive control is applied, which decomposes the MPC problem into a dynamic optimization problem upon linear model and a static rooting problem of nonlinear algebraic equation.
采用两步法预测控制，即将预测控制问题分解为一基于线性模型的的动态优化问题及一非线性模型的静态求根问题。
The model of the nonlinear system is obtained by LS-SVM, the offline model is linearize at each sampling instant and uses linear predictive function control methods to obtain the control law.
该算法采用LS-SVM回归建立非线性系统的预测模型，然后，将离线模型在每个采样周期关于当前采样点进行线性化，同时利用线性预测函数控制方法求解解析的控制律。
Nonlinear predictive control is realized by the global linear model based roll optimizing, and on-time adjusting using neural network based nonlinear model of the nonlinear system.
利用全局线性模型进行滚动优化，利用非线性预测模型校正线性模型，实现非线性预测控制。
It is difficult to keep favorable controlling performance of model predictive control (MPC) by linear model due to controlled plant nonlinearity in propylene rectifier.
在丙烯精馏塔的控制中，由于被控对象的非线性特性，采用线性模型的模型预测控制器难以保持良好的控制性能。
Nonlinear predictive control is realized by the global linear model based roll optimizing, and ontime adjusting using neural network based nonlinear model of the nonlinear system.
全局线性模型用于滚动优化 ，非线性模型用于预测系统输出和校正线性模型 ，实现非线性预测控制。
Model predictive control (MPC), also known as receding horizon control (RHC), is a class of model-based control theories that use linear or nonlinear process models to forecast system behavior.
模型预测控制(MPC)，也称为滚动时域控制(RHC)，是一种基于模型的控制理论，采用线性或非线性模型预测系统的活动。
The paper presents an optimal control scheme based on moving optimization principle of model predictive control for linear system with constraints.
以预测控制的滚动优化原理为基础提出了一种约束线性系统的最优控制方法。
The paper presents an optimal control scheme based on moving optimization principle of model predictive control for linear system with constraints.
以预测控制的滚动优化原理为基础提出了一种约束线性系统的最优控制方法。
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