为了进一步提高系统的动态性能，在非线性PI算法中加入了负载电流前馈补偿控制。
To improve further the dynamic characteristic, the non-linear PI arithmetic with the load-current forward compensating (LCFC) control is also improved.
由SVM辨识非线性系统的逆模型作为前馈控制器，形成直接逆控制。
SVM were used to identify the inverse model of nonlinear system, and this inverse model was used as feed-forward controller to design direct inverse control.
实验结果表明，逆模前馈补偿器有效地减小了系统的非线性误差，提高了执行器的控制精度。
The results of test show that this inverse model feed-forward compensator has effectively reduced the non-linear error of the system and has improved the control precision of GMA.
提出一种对非线性系统用线性控制的方法，通过前馈控制器进行控制。
This paper presents a linearization control methods of nonlinear system, which is realized by feed forward controller.
前馈神经网络由于具有理论上逼近任意非线性连续映射的能力，因而非常适合于非线性系统建模及构成自适应控制。
Because the feedforward neural network has an ability of approach to arbitrary nonlinear mapping, it can be used effectively in the modeling and controlling of nonlinear system.
得到的最优控制律由解析的线性前馈-反馈项和伴随向量序列极限形式的非线性补偿项组成。
The obtained optimal control law consists of analytical linear feedforward and feedback terms and a nonlinear compensation term which is the limit of the adjoint vector sequence.
双级神经网络的前馈解耦控制提出了具有时变，非线性，不确定性和多变量耦合发酵过程中。
A double-level neural network for feedforward decoupling control is proposed for the fermentation process characterized with time-variable, nonlinear, uncertain and multivariable coupling.
双级神经网络的前馈解耦控制提出了具有时变，非线性，不确定性和多变量耦合发酵过程中。
A double-level neural network for feedforward decoupling control is proposed for the fermentation process characterized with time-variable, nonlinear, uncertain and multivariable coupling.
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