The scheme does not rely on the mathematical model of the controlled plant.
该方案不依赖于系统的数学模型。
A completely new, computer controlled plant was set up and went on stream by 1990.
一个全新的，计算机控制的工厂于1990年建立，并且开始投产。
The nonlinear offline model of the controlled plant is built by LS-SVM with the radial basis function (RBF) kernel.
首先，用具有RBF核函数的LS-SVM离线建立被控对象的非线性模型；
But whether classical control theory or modern control theory need to know the mathematical model of the controlled plant.
但无论是经典控制理论还是现代控制理论，都需要事先知道被控对象的数学模型。
When a high order controlled plant contains several small time constant inertia units, it can be simplified to low order time-delay system.
一个高阶被控对象含有几个小时间常数惯性环节时，可简化成低阶时滞系统。
The controller formed by this control logic consists of both the feed-forward of the reference model system states and the feedback of the controlled plant states.
该控制方案所形成的控制器由参考模型系统状态的前馈和被控系统的状态反馈两部分组成。
The optimal moving average filter for the linear stochastic controlled plant is developed from the viewpoint of the internal model of the deterministic disturbance.
从确定性干扰的内模观点导出了适用于线性随机受控对象的最优滑动滤波器。
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。
It is difficult to design the closed loop gain shaping controller when the controlled plant is not a square matrix, because it is involved in the pseudo inverse of matrix.
在被控对象为非方阵的系统中，由于求解控制器时涉及到矩阵求伪逆问题，很大程度上增加了闭环增益成形算法的难度。
A continuous reheating industrial furnace is large inertia, pure delay, multi-variables, time-varying, intercoupling complicated controlled plant, and has many uncertain factors.
连续式加热炉是个典型具有大惯性、纯滞后、多变量、时变并且相互耦合的复杂被控对象，而且有许多不确定因素。
The control system can adjust the weights of hybrid neural network and the parameters of controller timely to keep good control performance when the character of controlled plant varies.
当被控对象的特性发生变化时，可对混合神经网络权值及时进行修正并调整控制器参数使控制系统始终保持良好的控制性能。
Simulation results show that the presented method can accurately construct the inverse dynamic model of the continuous nonlinear system even without prior knowledge about the controlled plant.
仿真结果表明，在没有被控对象先验知识的情况下，利用该方法能准确地建立连续非线性系统的逆模型。
For overcoming the big inertia and large time delay of the controlled plant, grey predictor is introduced to predict future output value, the predictive result is used as tuning information of DNN.
为了克服系统的大惯性和大迟延，引入灰色预测器对未来信号进行预测，预测结果作为DNN使用的整定信息。
For overcoming the big inertia and large time delay of the controlled plant, grey predictor is introduced to predict future output value, the predictive result is used as tuning information of DNN.
为了克服系统的大惯性和大迟延，引入灰色预测器对未来信号进行预测，预测结果作为DNN使用的整定信息。
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