针对离散不确定模糊模型，提出了一种鲁棒非线性模型预测控制方法。
A robust model predictive control (MPC) scheme based on fuzzy modelling is proposed for a nonlinear system.
建立了基于模型方程的SOFC非线性模型预测控制算法(NMPC)应用。
Develop a nonlinear model predictive control (NMPC) for SOFC application. NMPC is well suited in the nonlinear control environment with specified constraints.
提出基于非线性模型预测控制的快速汽门控制器设计方法并推导出控制规律解析式。
The design method based on nonlinear model predictive control is put forward, and analytical solutions are drawn from it.
针对化工过程某些非线性系统的不对称动态特性，提出了一种基于自校正模型的多模型预测控制算法。
To handle the unsymmetrical dynamic characteristics of some nonlinear systems in chemical process, a multi-model predictive control method was proposed based on self-tuning model.
与采用线性化模型相比，采用非线性派克模型可保证灰色预测控制输入数据的非负性，简化预测算法。
Compared with the commonly used linearized model, the nonlinear Park model guarantees the nonnegative input data in grey prediction control, simplifies the prediction algorithm.
针对具有高度非线性特性的连续搅拌反应釜(CSTR)控制过程，研究了基于神经模糊模型的预测控制策略。
In this paper, a predictive control strategy based on neuro-fuzzy model is applied to Continuous Stirred Tank Reactor (CSTR) process, which has characteristic of highly nonlinearity.
针对一类具有特殊模型的非线性系统本文提出了一种新型神经网络预测控制算法。
A novel neural network predictive control algorithm is proposed for a class of nonlinear system with special model.
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。
It is difficult to keep favorable controlling performance of model predictive control (MPC) by linear model due to controlled plant nonlinearity in propylene rectifier.
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。
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.
然后在每一个采样点对系统进行局部动态线性化，根据得到的系统线性化模型对系统采取广义预测控制（GPC）方法得到当前的控制动作。
Local dynamic linearization is applied to the system at each sampling point. Then control action is gained using generalized predictive control (GPC) based on the linearized model.
非线性复杂系统的预测控制是一种高性能的控制方法，其关键在于非线性预测器模型的实现。
The prediction control is an excellent method for the control of non-linear complex system. The key unit for predicting control is to design a suitable prediction model.
对于复杂的离散时间非线性系统，提出一种基于多模型的广义预测控制方法。
A multiple model based generalized predictive control is provided for complex nonlinear discrete time system.
针对一类基于模糊T-S模型的输入受限非线性系统提出了鲁棒模型预测控制。
Robust model predictive control is proposed for a class of nonlinear systems with constraint inputs based on fuzzy T-S model.
提出了一种新的基于T_S模糊模型的非线性预测控制策略。
A nonlinear model predictive control (NMPC) strategy based on T_S fuzzy model is proposed.
提出了C -R模糊模型的辩识递推算法和C - R模糊模型的非线性预测控制算法。
It is presented the identity conclusion method of C-R fuzzy model and the nonlinear predictive control of C-R fuzzy model.
研究了基于人工神经元网络模型的非线性预测控制。
The study of nonlinear predictive control based on artificial neural networks is carried out.
然后从简单的单输入单输出模型到二输入二输出模型，再到一些相对特殊的线性系统的模型，由浅入深的研究了广义预测控制理论的应用；
Secondly, from single input single output model to two inputs two outputs model, and then to some relatively complex linear model, the application of GPC theory is studied.
基于线性模型的模型预测控制研究已经相当成熟并得到了广泛的工业应用。
Research on linear predictive control has become mature and linear MPC has gained wide application in industrial processes.
提出了不确定非线性系统多模型预测控制的新方法，它结合了针对非线性的基于系统输出的切换方法和针对不确定性的模型切换方法。
For the uncertain nonlinear system, the multiple-model predictive control is studied which combines the switching method for the nonlinearity and the one for the model uncertainty.
由于T_S模糊模型每条规则的结论部分是一个线性模型，因此整个模糊模型可以看作一个线性时变系统，从而将模糊预测控制器中的非线性优化问题转化为一个线性二次寻优问题，以方便求解。
Since the conclusion part is linear, the T_S fuzzy model can be treated as a linear time_varying system, the nonlinear program in NMPC turns into a linear quadratic problem that can be easily solved.
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。
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.
论文以具有大范围工况特点的实际系统为背景，结合混合逻辑动态模型来研究分段线性系统的模型预测控制。
Through logic variables, piecewise linear systems can be modified into a single-linear model and affiliated constraints with mixed logical dynamic systems.
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。
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.
文中依据非线性舰船模型，应用模糊神经网络简化出适应于舵减横摇控制器设计的模糊线性模型，并设计了广义预测控制器。
According to the nonlinear model of ship, a predigested fuzzy linear model is built adapted to rudder roll stabilization using fuzzy neural networks.
仿真结果表明了该线性时变模型和参数估计算法的可行性，表明该自适应预测控制方法具有优良的控制品质。
Simulation results demonstrate the feasibility of the model and the parameter estimation algorithm, and show that the adaptive predictive control method has excellent control quality.
详细的介绍了基于局部线性化状态空间模型的预测控制算法。
Model predictive control based on the local linearization state-space model is introduced in detail.
在DLF模型的基础上，本文研究了一种非线性预测控制算法，它的显著特点是在线计算量小。
Based on the DLF model, a nonlinear predictive control algorithm is proposed in the paper. The significant feature of the suggested control strategy lies in its low online computing burden.
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。
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.
其实质是用神经网络作为预测模型，产生预测信号，用滚动优化算法求出控制律，从而实现对非线性系统的预测控制。
Actually it USES the neural networks as the predictive models to produce the predictive signals, the control law is solved by optimized algorithm, accordingly control nonlinear system.
其实质是用神经网络作为预测模型，产生预测信号，用滚动优化算法求出控制律，从而实现对非线性系统的预测控制。
Actually it USES the neural networks as the predictive models to produce the predictive signals, the control law is solved by optimized algorithm, accordingly control nonlinear system.
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