灰色预测控制的特点就是少数据、高品质。
The characteristic of grey prediction controller is few data and high quality.
介绍了灰色预测控制系统软、硬件的设计；
Then, the design of hardware and software systems of GPC is introduced.
分析了转子系统灰色预测控制设计方法及应用。
The design procedures and application of grey forecasting control to rotor system are proposed and studied.
灰色预测控制是以灰色系统理论为依据的一种全新的控制模式。
The grey predicative control is a fully new control mode based grey system theory.
分析表明灰色预测控制适用于大跨预应力混凝土桥梁的施工控制。
The results indicate that the grey predictive control system is adapted to the construction control for the long-span PC Bridge.
灰色预测控制系统不仅可以对大跨径连续梁桥的施工进行控制，而且方法简单，效果显著。
Gray predict control system can be applied in construction control of long-span prestressed concrete continuous bridge, and the method is simple, the result is prominent.
通过计算机进行灰色预测控制仿真，显示超调量小，调节时间短，控制效果优于单一pid控制。
The results of computer simulation show that the maximum overshoot is small-er and the settling time required is shorter as compared with single PID control.
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.
本文结合新寨河特大桥工程实例，运用灰色预测控制系统，对该桥在施工过程中各施工状态进行预测控制。
In this paper, Xinzhaihe bridge Project, the use of gray forecasting control systems, during construction of the bridge construction in the state of the predictive control.
灰色预测控制理论中灰色建模和“超前控制”的思想较好地弥补了线性最优控制理论中精确线性化和“事后控制”的不足。
The grey modeling and the idea of pre-control in grey prediction control theory can well remedy the defects of exact linearization and after-control in linear optimal control theory.
本文介绍一种利用灰色预测原理设计的预测控制器。
This paper introduces a predicting controller designed by grey prediction principle.
基于多变量灰色系统模型，提出了一种MIMO系统的广义预测控制直接算法。
A direct generalized predictive control algorithm of MIMO systems based on multivariable grey model is presented.
基于多变量灰色系统模型，提出了一种MIMO系统的广义预测控制直接算法。
A direct generalized predictive control algorithm of MIMO systems based on multivariable grey model is presented.
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