PWM型DC/DC变换器混沌机理及控制研究
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摘要
混沌是发生在非线性系统内部的一种内在随机运动,它是非线性系统的一种性能表征,近二十年来引起人们的广泛关注。开关式 DC/DC 变换器是一个强非线性系统,在一定的参数条件下,DC/DC 变换器会产生混沌现象。因此,仅仅从线性论和确定论角度去分析 DC/DC 变换器中的不稳定及次谐波现象已不能满足对 DC/DC 变换器控制性能日益提高的要求。在某些特定的条件下,从混沌理论的角度去解释 DC/DC 变换器中的不稳定现象显得更加合理和易于接受。在混沌理论的基础上,研究相应的控制手段,对提高 DC/DC 变换器的控制性能具有重要的意义。本文研究了 PWM 型 DC/DC 变换器中混沌产生的机理并在混沌控制方面作了进一步的探索。
    采用分段线性数值仿真法分别对 Buck 及 Boost 变换器的动力学行为进行仿真,对 DC/DC 变换器在某些特定的参数下出现的混沌现象及从稳态通向混沌的倍周期分岔行为进行分析,并分别在时域和频域对 DC/DC 变换器处于各周期态及混沌态时输出电压特性进行比较、分析。针对如何识别变换器中混沌现象问题,首先提出用付里叶描述符法提取 DC/DC 变换器输出信号特征值,在此基础上,提出采用 BP 神经网络进行信号识别,并对 BP 神经网络算法加以改进,最终测试结果表明 BP 神经网络对变换器输出信号识别率达到 100%。在理论研究的基础上,通过实验展示了 DC/DC 变换器的工作状态随电路参数变化从稳态通向混沌态的演化过程,验证了理论分析的正确性。
    研究 PWM 型 DC/DC 变换器处于混沌态时特点,分别采用 OGY 法、外力反馈控制法及延时反馈控制法对 DC/DC 变换器进行混沌控制研究。分析、比较每种控制方法的特点、控制效果及局限性。实验中,以外力反馈控制法为指导思想,设计控制电路并对 DC/DC 变换器进行混沌控制,实验结果证实了 DC/DC 变换器混沌控制的可行性。
    将混沌反控制技术应用到 DC/DC 变换器控制中,分别采用外部噪声混沌反控制法、延时反馈控制法及利用混沌信号对处于高周期态或混沌态的变换器进行混沌反控制,使变换器进入新的混沌态。从几何学的角度分析 DC/DC 变换器在混沌反控制前、后混沌吸引子在相空间中的变化特点,对混沌吸引子的结构参数以量值化加以定义,通过分析混沌吸引子在相空间中结构的变化折射出 DC/DC 变换器输出电压中交流分量变化特性,从而对 DC/DC 变换器在混沌反控制前、后输出质量作定量评价。
Chaos is a kind of internal random movement, which appears in non-linearsystems. In recent 20 years, it has been paid great attention as a property token ofnon-linear system. A switched-mode DC/DC converter is a high non-linear controlsystem, which can produce chaotic phenomenon under certain parameters. Soanalyzing the phenomenon of unstable action and subharmonic of DC/DC converterfrom the angle of linear theory and definiteness theory can not meet the increasingrequirement of control performance of DC/DC converter. Under some specialconditions, explaining unstable phenomenon of DC/DC converter from chaotic theorycan be more reasonable and easily accepted. Based on chaotic theory, researching anddesigning corresponding control method can give great help to increase controlperformance of DC/DC converter. This dissertation makes more studies on chaosmechanism and chaotic control of PWM DC/DC converters.
     Theoretically analyzes and emulates chaos and period-doubling bifurcation ofBuck and Boost converters with piecewise linear numerical emulation. Performancesof the output ripples of DC/DC converter from stable state to chaotic state areanalyzed in time domain and frequency domain respectively. Aim at identifying chaosof DC/DC converter, the method of using Fourier Descriptor to extract output signalscharacteristics is put forward, based on which an improved BP neural network is thenused to identify the output signals of DC/DC converter. Finally, the testing resultindicates the identification rate of the output signals of DC/DC converter using thisBP neural network can reach to 100%. Based on theoretical research, an experimentis done to exhibit chaos and evolution from stable state to chaotic state of DC/DCconverter, which validate the correctness of theoretical analysis.
     Study the features of DC/DC converter in chaotic state, OGY method, outsideforce feedback control method and delay feedback control method are usedrespectively to control chaos in DC/DC converter. Then features, effects andlimitations of every control method are analyzed and compared one another.According to the principle of outside force feedback control method, a chaotic controlcircuit is designed to control chaos in DC/DC converter. The result of experimentvalidates the feasibility of chaotic control in DC/DC converter.
     Apply the technology of chaotic anti-control to DC/DC converter. Externalnoise driving method, delay feedback control method and chaotic signal drivingmethod are used respectively to give a chaotic anti-control over DC/DC converter inunstable or chaotic state in order to make it come into a new chaotic state. Analyzethe feature of chaotic attractor in phase space of DC/DC converter before and after
    anti-control from the angle of geometry. Define two structure parameters of chaoticattractor in order to reflect the trait of output ripples of DC/DC converter according tothe change of these two structure parameters, so the quality of the output performanceof DC/DC converter can be estimated quantificationally.
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