本文的主要内容之一是重点研究了线性时变模型的核心——脉冲敏感函数。
One of the most important content in this thesis is research on the core of linear time varying model - impulse sensitivity function.
仿真结果表明了该线性时变模型和参数估计算法的可行性，表明该自适应预测控制方法具有优良的控制品质。
Simulation results demonstrate the feasibility of the model and the parameter estimation algorithm, and show that the adaptive predictive control method has excellent control quality.
当模型复杂且非线性时（换句话说，在线性方式下参数不共变），或者当模型涉及多于两三个不确定参数时，使用该方法。
The method is used when the model is complex and nonlinear (that is, parameters do not co-vary in a linear manner), or when the model involves more than just two or three uncertain parameters.
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.
烧结是一个典型的时变、非线性、大滞后的系统，很难用一个确定的数学模型去描述整个过程。
As sinter is a typical time varying and nonlinear system with large delay time, it is difficult to describe the whole process using a mathematical model.
针对其存在非线性、参数时变和大延迟等难以控制的特性，提出基于T - S模糊模型的预测函数控制新方法。
As the nonlinearity, time-varying parameters and large lag make the control difficult, a predictive functional control method based on T-S (Takagi-Sugeno) fuzzy model is presented.
基于复杂工艺过程的时变、非线性、大滞后的系统，很难用一个确定的数学模型去描述整个过程。
Based on a typical time varying and nonlinear system with large delay time, it is difficult to describe the whole process using a mathematical model.
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.
信道模型为一线性时变系统。
本文利用最小二乘估计给出噪声为ARMA序列线性模型中时变参数估计，并讨论了估计量的相容性问题。
In this paper we get the estimation of time-varying parameters in linear regression model with ARMA noise by the least square method and discuss the consistency of the estimation at the same time.
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.
多模型控制是解决系统时变、非线性、参数不确定性等复杂问题得一种有效方法。
Multiple model control is an effective way for solving complicated problems such as time varying, nonlinear and parameters uncertainty.
针对高速公路可变速度控制是一个非线性时变系统，难于用数学模型准确建模这一特点，提出了神经网络控制方法。
The variable speed control for freeway traffic is a nonlinear and time variable system, it is difficult to model with a mathematical model. A neural network control method is put forward.
在考虑齿轮时变啮合刚度的情况下，建立了齿轮耦合的转子-轴承系统的非线性动力学模型。
Considering the time varying meshing stiffness of gear pair, the nonlinear dynamic model of a geared rotor bearing system is established.
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.
由于T_S模糊模型每条规则的结论部分是一个线性模型，因此整个模糊模型可以看作一个线性时变系统，从而将模糊预测控制器中的非线性优化问题转化为一个线性二次寻优问题，以方便求解。
Since the conclusion part is linear, the T_S fuzzy model can be treated as a linear time_varying system, the nonlinear program in NMPC turns into a linear quadratic problem that can be easily solved.
氧乐果合成过程具有非线性、时变和不确定性的特点，难以采用常规的建模方法建立模型。
Omethoate synthesis process has the characteristic of the time-variant, nonlinear and uncertainties. It is difficult to model using the conventional modeling methods.
有许多复杂的系统是无法用传统方法对它定义，特别是那些非线性的动态时变系统，还不能建立有效的数学模型和控制方法。
Many complicated systems, especially nonlinear dynamic time-variable system, can not be defined by conventional methods which haven't been built effective mathematic model and control method.
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.
针对火电厂锅炉过热系统的时变和非线性的特点，提出了模糊多模型的控制方法。
A fuzzy multi-model control method is proposed for the characteristics of the super-heated system with time varying and nonlinearity in the power plant.
作者提出了用虚拟时变噪声统计，补偿线性化模型误差的新思想。
A new approach of compensating linearized model errors by the fictitious time-varying noise statistics is proposed.
针对非线性、时变的帆船航行系统，提出了一种基于T - S模糊模型的帆船模糊自适应控制新方法。
A new fuzzy adaptive control method based on a T-S fuzzy model is proposed for the nonlinear and time-variant navigation systems of sailboats.
模糊控制适用于数学模型未知的，复杂的非线性、时变、滞后系统的控制。
Fuzzy control is suit for nonlinear and hysteretic system whose model is unknown.
针对高速公路限速控制是一个非线性时变系统、难以用数学模型准确建模这一特点，提出了R BF神经网络控制方法。
The control for speed limitation on freeway is a nonlinear and time variable system, it is difficult to model with a mathematical model. A control method based on RBF Neural Network is put forward.
针对液压弯辊系统数学模型的非线性、时变特性，本文设计了一种模糊神经网络模型参考自适应控制器。
In view of the time-variable and nonlinear characteristics of mathematical model of hydraulic bending roll system, this thesis design a new nonlinear adaptive controler based on fuzzy neural network.
高速公路限速控制是一个非线性时变系统，难于用数学模型准确建模，提出一种模糊神经网络实现限速控制。
The control for speed limit on expressway is a nonlinear and time variable system, it is difficult to simulate with a mathematical model. A neuro-fuzzy network is proposed to solve the problem.
针对非线性时变的发酵过程，建立了用于产物浓度预估的支持向量机(SVM)模型。
In accordance with the features of non-linear and time varying for ferment process, a support vector machines (SVM) model is established for estimating the concentration of product.
在传统的算子理论基础上，建立了电路的算子模型，提出了求解线性非时变系统全响应的一种新方法。
Basing on traditional operator theory, operator madel for a circuit is established, and a new method for the solution of response of linear time invariance system is introduced.
水轮机调速系统是典型的具有非最小相位、非线性、时变特性的复杂控制系统，难以建立精确的数学模型。
To the question of the hydraulic turbine regulating system, this paper discusses fuzzy neural network control (FNNC) based on the character of fuzzy logic and neural network theory.
捷联惯导系统的卡尔曼滤波模型在传递对准时，为线性时变系统，而线性时变系统的可观测性分析比较困难。
During a transfer alignment of strapdown inertial navigation system, Kalman filter models are linear time varying system whose analysis of observability is difficult.
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