Description: 为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani 型模糊推理规则控制器控制倒立摆系统稳定的基础上,
设计了一种更有效率的基于Sugeno 型模糊推理规则的模糊神经网络控制器。该控制器使用BP 神经网络和最小二乘法的混
合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani 型控制器的仿真对比,
表明该Sugeno 型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。-In order to improve the three-level control of inverted pendulum system response speed and stability, in the design of Mamdani-type fuzzy inference rules of the system controller to control the stability of inverted pendulum on the basis of a more efficient design based on Sugeno-type fuzzy inference rules of fuzzy neural network controller. The controller is the use of BP neural network and hybrid least squares training algorithm parameters can be accurately summed up the amount of input and output fuzzy membership function and fuzzy logic rules. Mamdani-type controller with a simulation comparison shows that the Sugeno-type fuzzy neural network controller for the three-tier control of inverted pendulum system with good stability and fast, as well as a higher control precision. Platform: |
Size: 551936 |
Author:月到风来AA |
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Description: 基于BP神经网络的二级倒立摆控制器选择小车的位移和速度及两个摆杆偏离铅锤线的角度和角速度为输入数据。并经训练可得到一个神经网络控制器。-The controller of the double inverted pendulum based on the BP neural network chooses displacement and speed of the car, the angle between the two pendulums bar and vertical line and angle speed as the input date. A BP network controller can then be got by training. Platform: |
Size: 1386496 |
Author:durongmao |
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Description: 针对BP神经网络存在的缺点，本文利用遗传算法能够收敛到全局最优解而
且遗传算法鲁棒性强的特点将遗传算法同神经网络结合起来，不仅能发挥神经网
络的泛化映射能力，而且使神经网络具有很快的收敛性以及较强的学习能力。为
了验证遗传算法优化BP神经网络的有效性，本文将此算法应用到直线一级倒立
摆的稳定控制中，同时利用UbVIEW语言界面开发能力强，并且数据输入、网
络通信、硬件控制简单的优点，制作了倒立摆的仿真控制和实时控制软件。仿真
研究表明，遗传算法优化BP神经网络的控制器设计是可行的，可以很好的实现倒
立摆的稳定控制。
-BP neural network for the shortcomings, this paper genetic algorithm can converge to global optimal solution and
And genetic algorithm robustness characteristics of the genetic algorithm combined with neural networks, neural networks can not only play
The generalization ability of network mapping, and the neural network with fast convergence and a strong ability to learn. As
The validation of genetic algorithm to optimize the effectiveness of BP neural network, this algorithm is applied to this line a handstand
Put stability control, and interface development using UbVIEW language ability, and data entry, network
Network communications, hardware, the advantages of simple control, produced a simulation of the inverted pendulum control and real-time control software. Simulation
Studies have shown that genetic algorithm BP neural network controller design is feasible, can achieve a good fall
Li put stability control.
Platform: |
Size: 4796416 |
Author:高飞 |
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Description: ：针对能够采用仿射非线性表示的含有未建模动态的SISO非线性系统，讨论了一种基于神经网络的自适应
控制方法．该方法对受控对象的已知部分．采用反馈线性化方法设计控制器，用神经网络在线补偿未建模动态及
外部干扰等引起的误差，从而实现自适应控制。对具有未建模动态的双车倒立摆设计了输出反馈自适应控制系
统．仿真表明该方法是有效的。 -A discussion is devoted to design neural network adaptive control scheme of the SISO (single
input and single output)nonlinear system with unmodeled dynamics．According to the known part of
the plant．feedback Iinearization method iS used to design the controller．The error resulted from the un～
modeled dynamics and the external disturbance is compensated by online neural network．The neural
networks are designed as a five layer fuzzy neural network and its construction is optimized by genetic al—
gorithms．It has been used to approtimate the nonlinear function of system and to compesate the error of
unmodeled dynamic．The design of neural network adaptive controller has better performances．The
method is verified by the digital simulation of tWO—·cart with inverted·-pendulum system and unmodeled
dynamics． Platform: |
Size: 163840 |
Author: |
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Description: 倒立摆神经网络模型参考控制器设计,过程很繁琐，里面包括参考的模型，实际的倒立摆模型-Inverted pendulum neural network model reference controller design process is cumbersome, which includes the reference model, the actual inverted pendulum model Platform: |
Size: 16384 |
Author:作业本 |
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