倒立摆是非线性、不稳定的系统。
倒立摆是智能控制的理想实验对象。
Inverted-pendulum is an ideal experimental object in intelligent control study.
对单级倒立摆系统的平衡控制问题进行了研究。
The balance control of a single inverted pendulum system is focused.
本文通过智能控制算法实现倒立摆的起摆控制。
This paper will describe the artificial Intelligence method to swinging up the pendulum.
本文介绍了一个倒立摆微机控制装置的设计方法。
This paper presents a design method of an inverted pendulum microcomputer control device.
给出了单级倒立摆的一种神经网络逆模控制方案。
An inverse controller based on neural network for single inverted pendulum is proposed.
控制器不依赖于倒立摆系统和混沌系统的模型函数。
The controllers do not rely on the models of the inverted pendulum and chaotic systems.
在倒立摆试验中的应用结果表明了该方法的有效性。
The appliance result in inverted pendulum shows that it is effective.
基于直线一级倒立摆系统完成了摆上舞蹈的实物控制。
Based on linear single inverted-pendulum system, this controller achieves real-time dance controlling.
然后，以这些数据为基础训练倒立摆的神经网络逆模型；
Then the neural network based inverse model of the inverted pendulum is trained.
应用非线性系统跟踪控制方法对倒立摆系统的控制进行研究。
The stable control of the inverted pendulum system is studied with the nonlinear tracking control method.
本文首先对倒立摆系统的结构和数学模型进行了分析、建模。
First of all, this thesis analyses the structure and mathematic model of Inverted pendulum system.
针对倒立摆的位移控制，提出一种非线性滑模变结构控制方法。
A nonlinear sliding mode control method is presented for single inverted pendulum position tracking control.
介绍了双簧倒立摆试验台的测试原理，试验台参数的标定方法。
The measuring theory and demarcation method about parameters of the test rig were introduced.
本文介绍了一个小车放在倾斜轨道上的平行倒立摆数字控制系统。
The control system design for a parallel inverted pendulum on an inclined rail has been successfully implemented.
基于一级倒立摆系统线性模型的不确定性，建立了灰色预测模型。
Based on the uncertainty of linear model for single - inversed pendulum, a gray prediction model is built up.
研究基于动态查询表的模糊控制策略及其在转臂式倒立摆中的应用。
A fuzzy strategy based on dynamic query table and its application on rotary inverted pendulum are studied.
完成单级倒立摆系统的调试和实验，实现了倒立摆位置随动以及定位功能。
Debugging and experiment of single inverted pendulum is accomplished, function of following and positioning was realized.
该方法采用环路成形技术设计倒立摆控制器，对权函数采用遗传算法进行优化。
The controller of inverted pendulum is designed by using the loop shaping technique. GA is adopted to optimize the weighing functions.
通过对单级倒立摆系统的简化模型分析，设计了带有积分环节的数字最优控制器。
Firstly, the simplified model of the single inverted pendulum system is analyzed, and then designed a digital optimal controller with integral function.
在倒立摆小车轨道较短的条件下实现倒立摆快速稳定的摆起，是摆起控制的难点。
Fast and stable swinging up at the situation of a shot track is the difficulty of swing-up control.
为倒立摆建模。通常倒立摆系统建模成一个线形系统，因此模型只对小幅度摆动的摆才有效。
Modeling an inverted pendulum. Generally the inverted pendulum system is modeled as a linear system, and hence the modeling is valid only for small oscillations of the pendulum.
范例教学是实践证明很成功的教学方法，为了达到教学目的，设计了一个倒立摆调节器范例。
Example Pedagogics is proved to be a good method, so an example of inverted pendulum regulator is designed for this purpose.
为了实现对具有弹性的不稳定机械系统的控制，对弹性倒立摆的动态稳定性控制进行了研究。
For realizing the control of elastic unstable mechanical system, the dynamic stability control of an inverted flexible pendulum has been studied.
通过对一级倒立摆的不确定模糊非脆弱控制器设计的实例，表明了设计方法的可行性和有效性。
An inverted pendulum example of non-fragile controller design of uncertainty T-S fuzzy systems shows the feasibility and the effectiveness of the method.
最后通过编程实现了各种智能控制算法对倒立摆系统中的实物控制，均取得了令人满意的控制效果。
Finally realized the inverted pendulum system's practicality control by each kind of intelligent control algorithm through programming, and obtained the satisfying control effect.
针对二级倒立摆系统的平衡控制问题，对其进行数学建模，应用二次型最优控制理论设计了控制器。
The double inverted pendulum is modeled and the controller is designed by using quadratic optimal control theory to its equilibrium control question.
基于人工神经网络BP算法的倒立摆小车实验仿真训练模型，其倒立摆BP网络为4输入3层结构。
The training model of test simulation for car of inverted pendulum based on BP algorithm of artificial neural networks (ANN) is a BP network that has 4-input and 3-layer structure.
基于人工神经网络BP算法的倒立摆小车实验仿真训练模型，其倒立摆BP网络为4输入3层结构。
The training model of test simulation for car of inverted pendulum based on BP algorithm of artificial neural networks (ANN) is a BP network that has 4-input and 3-layer structure.
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