无模型控制方法非常适用于实际的阶数难以知道或难以辨识,且是时变的非线性系统。
The model-free control is especially useful for real nonlinear systems whose orders and modeling are very difficult to be known and time varying.
此无模型控制方法非常适用于实际的模型参数难以辨识,且是时变的非线性系统。
The model-free control is especially useful for real nonlinear systems whose model parameter are very difficult to be identified and time varying.
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.
本文针对一类广泛的非线性控制系统,我们构造了一些控制器,这些判据在工程实际问题中将具有一定的指导意义。
In this paper, for a wide class of nonlinear control systems, we construct some controllers concretely. This will give some instruction to application in engineering.
针对实际多变量强耦合非线性温控系统,设计了基于PC104嵌入式系统的拟人智能温度控制系统。
Simulating Human Intelligent Controller based on PC104 system is presented for a nonlinear temperature control system, which is multivariable and strongly coupling.
基于发动机力矩控制的思想,通过对发动机实际物理模型和相关脉谱图数据的分析,建立了一个非线性的发动机模型。
In this paper, based on the idea of engine torque control, after analyzing the practical physical model and some of the engine runtime map data, a nonlinear engine model was built.
然而,磁悬浮轴承系统是一个复杂非线性的系统,将它抽象成为一个线性系统,无法体现实际系统本身的动态特性,也无法满足更高的控制精度的要求。
But the Magnetic bearing is a complicated nonlinear system. Linearization of the bearing makes it hard to express the behavior of the system and to control it in higher precision.
针对双线性模型与实际系统之间的差异,提出一种基于双线性模型求解非线性动态系统最优控制的迭代算法。
This paper presents an iterative algorithm of optimal control for discrete time nonlinear systems, according to the differences of the real system and its bilinear model.
非线性动态系统的内模控制要求建立精确的对象正模型和逆模型,这对于大多数实际对象是难以做到。
In the internal model control design for nonlinear systems, the precise forward and inverse models of plant are required, but it is impossible in the majority of practical plants.
本文介绍带有未建模动态的倒立单摆非线性模型的全局自适应实际跟踪输出控制器的设计。
This paper introduces an adaptive practical output tracking control algorithm for inverted pendulum which is a nonlinearly parameterized system with unmodeled dynamics.
针对非线性离散动态大系统最优控制问题,在二维系统理论的基础上,对一种基于模型求解实际问题最优解的递阶算法作了分析。
Based on two -dimensional system theory, an analysis of a hierarchical optimal control algorithm for discrete nonlinear large - scale system is made.
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.
由于该算法对结构的不依赖性,对难以建模的柔性结构和非线性结构的振动主动控制具有实际意义。
By reason of its no-dependency on structure, the algorithm has importance for nonlinear structure and large flexible structure of which the model is difficult to obtain.
实际的控制系统都是非线性系统,完全线性的系统是不存在的。
All practical control systems are nonlinear, so the absolute linear systems don't exist.
饱和是很多实际控制系统中最为常见的非线性特性之一,大多数执行器都会不可避免地出现饱和。
Saturation is one of the most common nonlinearities in many practical control systems, which is unavoidable in a majority of actuators.
宏观经济中的卡尔多模型是典型的非线性经济模型,实际的经济系统是一个巨大的混沌系统,应用混沌控制具有很好的应用前景。
Kaldor model is a typical nonlinear economic model in the macroeconomic field, the existing economic system is a huge chaotic system, so the chaotic control has a good application prospect.
但是,由于抽象的微分几何理论缺乏面向非线性控制的系统理论,且它必须依赖于系统的精确数学模型,这严重制约了它在实际工程中的广泛应用。
However, the abstract differential geometric method is lack of system theory for nonlinear system and relies on the precise of the system, which to some extent limited its application.
然而在实际控制系统中,被控对象往往具有非线性、时变性和不确定性,因此,关于非线性预测控制的研究已成为控制工程界的重要研究课题。
However, most processes in industry are nonlinear, time-variant and bear uncertainty, thus research on nonlinear predictive control has become an important issue in control field.
论文运用数理理论对非线性动力系统的分岔和混沌的基础理论和控制进行了较为系统和深入的研究,为应用于工程实际奠定了理论基础。
The paper did a systematic and profound research in control of bifurcation and chaos based on mathematical theory, thus, set a theoretical foundation for its application in engineering projects.
工业中的实际系统大多具有非线性、不确定等特性,基于模型的经典控制理论和现代控制理论难以适用,而采用智能控制方法会取得较好的控制效果。
In Industry, most systems are characterized by nonlinear, uncertainty etc. Thus the control effect of model-based typical control theory and modem control theory is not as good as intelligent control.
然而,实际的生产过程也往往具有非线性、时变不确定性,应用常规pid控制不能达到理想的控制效果。
However, the actual production process is often non-linear, time-varying uncertainty, the application of conventional PID control can not achieve the desired control effect.
非线性研究系统在理论研究上具有巨大的复杂性,并考虑到在实际中各种非线性控制问题的不断涌现。
The non-linear research system has high complexity in the fundamental research, and lots of non-linear control problems unceasing emerge in real fact.
非线性研究系统在理论研究上具有巨大的复杂性,并考虑到在实际中各种非线性控制问题的不断涌现。
The non-linear research system has high complexity in the fundamental research, and lots of non-linear control problems unceasing emerge in real fact.
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