【作者】 黄国现；

【导师】 罗晓曙；

【作者基本信息】 广西师范大学 ， 电路与系统， 2004， 硕士

【摘要】 本文主要研究了强流离子加速器中束晕-混沌和DC/DC开关功率变换器中混沌的控制，主要做了以下工作,获得了良好的结果.第一，研究了强流离子束在周期磁场聚焦通道中传输时产生的束晕-混沌动力学行为,采用周期磁场聚焦强度形式为与实际相近的余弦函数形式.然后利用神经网络方法对非线性复杂系统控制的优越性,提出前馈反传神经网络方法对强流离子束中束晕-混沌进行控制.通过适当选择的神经网络控制结构和线性反馈系数以及自适应调整神经网络的权系数,可将强流离子束的包络半径达到束匹配半径的控制目标,且束包络的抖动大小明显减少；控制后束晕强度因子为零,束均方根半径、束横向动量平方和平均值和相对发射度比控制前都减少,能有效地束晕-混沌进行控制,消除了束晕及其再生现象.第二，DC/DC开关功率变换器是一种典型的分段光滑动力学系统,在一定的工作和参数条件下,系统会出现混沌运动现象.本文研究了基于径向基神经网络的控制方法对DC/DC开关功率变换器混沌系统进行控制,并以Buck功率变换器作为控制对象.数值仿真结果表明:该方法能有效地实现Buck功率变换器混沌系统的控制,系统在参数发生摄动和存在噪声的情况下,控制仍然有效.进一步说明神经网络方法对非线性复杂系统控制的优越性.更多还原

【Abstract】 In this paper,we mainly studied on controlling chaos in high-intensity accelerator and DC/DC switching power converter.Those pieces of research results are finished.Better results are obtained. At first,Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels of high intensity accelerators is studied by feed-forward back-propagating neural network method. The envelope radius of high-intensity proton beam is reached to the matched beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network.. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied.After control,the halo intensity factor become zero,other statistical physical quantities and relative average emittances are more than reduced.The beam halo and its regeneration can be eliminated perfectly. Secondly, DC/DC switching power converter is a typical piecewise-smooth dynamical system. In this circuit system, chaotic motion can be generated at certain work and parameters condition. In this paper, the control method based on RBF neural network is proposed for chaos control in DC/DC switching power converter. The control system is Buck power converter. Simulation results show that the chaotic system can be controlled effectively in Buck power converter by this method. The method is still effective when there are parameter perturbation and noise.It makes furter show that the advantages of neural network control method for nonlinear complex systems.更多还原