针对实际工业生产过程中的非线性、时变不确定性，提出了一种基于线性化误差模型的自适应控制系统。
In order to overcome the nonlinearity and time-varying uncertainty of actual industrial processes, an adaptive control system based on linearization error model is proposed.
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.
直接针对飞机的非线性模型设计的自抗扰控制器在很大的包线范围内不需要改变控制器的结构和参数，简化了设计过程。
Furthermore, the design of flight control system is simplified by the scheme proposed without changing the structure and parameters of controller in big flight envelope.
直接针对飞机的非线性模型设计的自抗扰控制器在很大的包线范围内不需要改变控制器的结构和参数，简化了设计过程。
Furthermore, without changing the structure and parameters of controller in big flight envelope, our scheme can simplify the design of flight control system.
在系统模型中考虑了TCSC的动态过程，同时保留了系统的非线性特性，运用自适应增益控制律进行参数的实时估计。
The dynamic process of TCSC is considered in the system model and the nonlinear characteristic is reserved. An adaptive gain control law is applied to real-time para-meter estimation.
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.
针对非线性时变系统在自适应控制过程中瞬态响应差的问题，提出了一种基于多模型自适应控制的模型切换算法。
A model switching algorithm based on multi-model adaptive control is presented to solve the problem of poor transient response in the adaptive control of nonlinear time-varying system.
针对工业过程和实际控制对象的慢时变非线性的特点，设计了一种预测模型的单神经元PI控制器。
A single neuron PI controller with predictive model is designed according to nonlinear systems of many industry processes and practical plants.
用自适应过程减小参数不确定性的影响，并通过动态滑模控制器的高鲁棒性抑制机器人模型中的未知非线性动力学及测量噪声等的影响。
The adaptive process extenuates the influence of parameter uncertainty, and the robustness of the dynamic sliding mode control inhibits the influence of unknown dynamics and measurement noise.
本文提出了基于多模型的内模控制方法，它对非线性过程的控制具有良好的性能。
Multiple internal model control design strategy was developed for a class of nonlinear systems.
针对生物发酵过程中温度控制难以建模的问题，基于非线性自回归滑动平均（NARMA）模型，设计了神经网络自回归滑动平均（NN-NARMA）模型。
To solve the problem of modeling temperature control in the fermentation process, a neural network nonlinear auto regressive moving average(NN-NARMA) modeling method for nonlinear system is proposed.
为了研究蒸发过程的非线性控制，对六效逆流蒸发系统的稳态工作点附近建立了动态模型，并将模型转化为仿射非线性系统的形式。
The dynamic model is presented for the steady state operation point of six-effect countercurrent evaporation system. And then the model is converted to an affine nonlinear system.
通用模型控制是一种有效的非线性控制方法，但该方法的应用条件是过程一阶微分模型必须要有显式解。
Common model control (CMC) is an effective nonlinear control method, but its application condition requests explicit solution for the first order differential model of the process.
人工神经网络从植物数据中学习非线性基本过程的能力可有助于发酵过程的控制。本文旨在研究柠檬发酵过程，并建立了两个柠檬发酵过程的人工神经网络模型。
The ability of artificial neural networks to leam essential process of non linearities from plant data may provide a means by which to assist fermentation process in being controlled.
人工神经网络从植物数据中学习非线性基本过程的能力可有助于发酵过程的控制。本文旨在研究柠檬发酵过程，并建立了两个柠檬发酵过程的人工神经网络模型。
The ability of artificial neural networks to leam essential process of non linearities from plant data may provide a means by which to assist fermentation process in being controlled.
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