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.
并应用基于状态空间模型的模型预测控制算法，推导了间歇精馏塔多个浓度反馈时的模型预测控制策略。
Furthermore, using a state space model based predictive control algorithm, a multi-composition feedback model predictive control strategy is developed.
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.
论文的主要结果有(1)对双速率采样系统，分别给出了基于状态空间模型和输入输出模型下的预测控制器的设计方法。
The main results are: (1) For a dual-rate sampled system, the design methods of predictive controller based on state-space model and input-output model are given respectively.
本文中提出了基于ARMAX模型的新型广义预测控制，揭示了其控制策略与模型算法控制(MAC)之间的内在联系。
In this paper, a new type of generalized predictive control based on ARMAX model is proposed. The internal relations between the control strategy and model Algorithmic control (MAC) are revealed.
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.
本文设计了基于神经网络技术的预测控制系统，包括控制对象模型的设计、预测模型的设计和控制器的设计。
The design of the predictive control system based on Neural Network include designing control object model, designing predictive model and designing controller model.
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.
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.
为了解决切换多模型预测控制的抖动问题，提出了加权多模型模糊预测控制。
To solve the problem of perturbation of witching multi-model predictive control, weighted multi-model fuzzy predictive control is put forward.
系统全面地阐述了模型预测控制的概念、特点及模型预测控制系统的结构型式。
The author comprehensively expatiated on the concept, characteristics of the model predictive control and structural form of the model predictive control system.
简单介绍了模糊预测控制系统的原理，详细介绍了如何建立基于模糊关系的模糊预测控制系统中的预测模型以及如何实现模糊预测控制的模糊预测模型。
The mechanism of the fuzzy predictive control is introduced briefly and how to established fuzzy predictive model base on the fuzzy relation and how to realize the model are introduced in details.
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.
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.
本文首先对模型失配问题进行了归类和定义，对一种已有的基于状态空间描述模型预测控制系统的模型失配评估方法进行了分析。
In this paper, the model plant mismatch problem is classified and defined. An existed method of model plant mismatches assessment formulated in terms of discrete time state space model is introduced.
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.
模型预测控制(MPC)是一种根据系统动态模型和历史信息，通过对系统未来行为的预测来优化当前输入的控制策略。
Model predictive control (MPC) refer to a class of control algorithms that optimize the current input by predicting the future behavior of system based on system dynamic model.
作为基于模型的优化控制算法，如果模型预测控制算法的预测模型与实际对象的失配程度很严重，则仅靠整定控制器参数将难以改善控制器性能。
As a model-based control algorithm, the performance of controller will be hard to be improved only by re-tuning the parameters of controller if there is serious mismatch between model and plant.
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.
模型预测控制对模型精度要求不高，对模型失配、非最小相位系统、不确定干扰的影响具有较强的鲁棒性，具有较高的控制性能。
Model predictive control the accuracy of the model do not ask for much on the model mismatch, non-minimum phase systems, the impact of uncertain interference robustness, higher performance.
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.
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.
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