开关变换器周期窗内吸引子共存现象仿真研究

开关DC-DC变换器是一种强非线性系统,存在各种非线性现象,切分叉是其中的一种特殊分叉.开关变换器因切分叉而引发了阵发混沌,阵发混沌使得系统的非线性动力学特性变得更加复杂.对电流控制型Boost变换器中产生的复杂动力学现象进行了仿真研究,揭示了参数变化分叉图中存在着周期窗、周期窗内共存吸引子和不完全倍周期费根鲍姆树等现象,通过构造相应的切分叉离散迭代映射曲线,说明了这些现象都足由于系统发生切分叉后形成的.研究结果对开关变换器的稳定设计具有重要的指导意义.     

电压模式Buck变换器混沌建模与控制研究

DC-DC变换器混沌现象与控制是复杂的非线性问题,而大部分该问题的仿真模型也是抽象的数学模块仿真模型.为了方便直观地研究,利用Matlab中simulink电路仿真模块建立了电压模式Buck变换器电路仿真模型.推导获得了变换器非线性系统的精确离散模型,并在经典OGY方法的机理上,给出了应用上述方法控制变换器混沌状态的控制序列.结合所建电路仿真模型仿真分析Buck变换器由周期分岔直至混沌与OGY控制混沌回到稳定的一周期轨道的过程.仿真结果表明,所建仿真模型准确而直观地验证了离散模型的正确性及OGY方法的有效性.     

周期性扩频的Boost变换器中非线性现象的研究

在分析了Boost变换器精确离散迭代模型的基础上,首次研究了采用周期性扩频技术后Boost变换器中的分叉和混沌现象。通过M文件编程得到了输出电压随着电路参数变化的分叉图,验证了它含有丰富的非线性动力学行为,而且研究了采用周期性扩频技术对变换器中非线性现象的影响。同时,在变换器中电路参数不变的情况下,研究了周期扩频技术的频率在不同范围内变化时,其中的分叉与混沌现象。本研究为更好地设计Boost变换器电路提供了一定理论基础和应用价值。     

DC-DC开关变换器的混沌现象及其应用

目前,DC-DC开关功率变换器的非线性现象的研究已发展到了混沌的控制与应用研究.本文论述了对DC-DC开关变换器的混沌现象及其应用,并展望了混沌开关变换器的应用前景.     

DC-DC Boost变换器的固定斜坡补偿技术研究

开关DC-DC变换器是典型的非线性时变系统,其中存在各种不稳定现象。本文以DC-DC变换器中的Boost电路为基础,通过在开关变换器反馈电路中引入适当的固定斜坡补偿,实现系统的稳定控制。     

软开关Boost变换器的精确数学建模与混沌仿真

该文详细分析了电压反馈型零电流开关Boost变换器的工作过程，建立了变换器工作在不连续运行模式(DCM)下的精确离散映射，对此数学模型进行了稳定性分析，确定了变换器中元件参数与其分岔稳定性的关系，在此基础上准确计算出了系统稳定运行的典型参数范围，利用Natlab6．5软件中的电力系统仿真模块(SimPowerSystems)对电路进行仿真分析，针对ZCS Boost变换器的特点建立仿真模型，并在Matlab／Simulink软件包环境下对该模型进行模拟仿真，得到了满意的结果，与理论分析取得一致。     

Buck变换器频率引起的混沌及其控制

大多数关于变换器分岔和混沌的研究都是以直流电源电压作为变量进行分析和控制的。为了研究变换器开关频率对系统动力学行为和混沌控制效果的影响,以PWM模拟电压控制Buck变换器为研究对象,首先通过计算机仿真电路得到分岔图,分析了开关频率对系统的影响;然后利用硬件实验电路对单周期稳定、倍周期分岔和混沌3种不同状态进行相图分析,对分岔图进行验证;最后,采用参数扰动法实施混沌控制,研究了正弦谐波扰动信号幅值对控制效果的影响。通过选取特定的幅值比,经过计算机仿真电路得到新的分岔图,与混沌控制前的分岔图形成了鲜明的对比;通过选取特定的开关频率,经过混沌控制前后的电容电压和电感电流时域图,验证了混沌控制有利于提高变换器的稳态和暂态性能。有利于加深对变换器非线性动力学行为及其控制的认识和研究。     

基于传输线模型的开关变换器数字仿真

瞬变过程和动态特性分析在开关变换器的研究设计中占有重要地位.阐述了一种以传输线理论为基础的开关变换器离散数字建模方法-传输线建模方法及其无条件数值稳定性,以Boost变换器为例建立了仿真模型,并对仿真和实验结果进行了比对.仿真结果精确地描述了实际电路的动态过程,在步长从0.02μs到0.2μs范围内变化时,结果保持稳定.这种方法简单实用,物理概念清晰,能有效地对开关变换器进行仿真研究.     

软开关Buck变换器混沌现象的研究

鉴于软开关DC／DC变换器的应用越来越广泛，由于谐振元件的存在使得变换器的混沌现象变得复杂。该文以BuckZVSPWM变换器为具体研究对象，建立了其精确离散数学模型，并基于此模型分析了BuckZVSPWM变换器的混沌现象。利用稳定性判据求出给定系统的临界稳定值，通过电路仿真证明了混沌图与变换器运行状态基本一致。该文建立的精确离散数学模型是在数学通用公式的基础上建立起来的。因此可以应用于其它软开关DC／DC变换器混沌现象的研究。     

恒功率负载下DC-DC双向变换器分岔现象研究

研究了基于多级功率变换器下的直流微网结构,提出恒功率负载研究方法,建立了直流微网简化模型;根据简化模型,建立了DC-DC双向变换器在不连续运行模式下的离散映射,给出了系统稳定运行状态下迭代方程和反馈系数的计算公式;根据迭代方程得出电压与反馈系数的分岔波形图。电路仿真和实验结果表明,随着反馈系数的增大,系统将从稳定状态到分岔状态并逐渐过渡到混沌状态。     

Experimental Implementation of New Sliding Mode Control Law applied To a DC–DC Boost Converter

The real implementation of sliding mode controllers (SMCs) to a DC–DC boost converter is a challenge due to the nonminimum phase behavior of these kinds of converters and the SMCs chattering problem. In this paper, new integral sliding mode control laws with linear and proposed nonlinear sliding surfaces are developed to overcome these problems. An experimental comparative study between these SMCs and the classical SMC applied for a DC–DC boost converter is presented.     

Robust constrained stabilization of boost DC–DC converters through bifurcation analysis

This paper proposes a new methodology for designing robust affine state-feedback control laws, so that wide-range safe and efficient operation of switched-mode DC–DC boost converters is guaranteed. Several undesirable nonlinear phenomena such as unstable attractors and subharmonic oscillations are avoided through bifurcation analysis based on the bilinear averaged model of the converter. The control design procedure also relies on constrained stabilization principles and the generation of safety domains using piecewise linear Lyapunov functions, so that robustness to supply voltage and output load variations is ensured, while input saturation is avoided and additional state constraints are also respected. The technique has been numerically and experimentally validated.     

Decentralized adaptive neural network control of cascaded DC–DC converters with high voltage conversion ratio

Decentralized output voltage tracking of cascaded DC–DC converters is an interesting topic to obtain a high voltage conversion ratio. The control purpose is challenging due to the load resistance changes, renewable energy supply voltage variations and interaction of the individual converters. In this paper, four novel decentralized adaptive neural network controllers are designed on the cascaded DC–DC buck and boost converters under load and DC supply voltage uncertainties. In the beginning, individual buck and boost converter average models that can operate in both continuous and discontinuous conduction modes are derived. Then, the interconnected and decentralized state-space models of cascaded buck and boost converters are extracted. These models are highly nonlinear with unknown uncertainties which can be estimated by neural networks. Further, two decentralized adaptive backstepping neural network voltage controllers are proposed on cascaded buck converters to deal with uncertainties and interactions. However, these control strategies are not applicable to a boost converter due to its non-minimum phase nature. Then, two novel decentralized adaptive neural network with a conventional proportional–integral reference current generator are developed on the cascaded boost converters. Practical stability of the overall system is guaranteed for the proposed controllers using Lyapunov stability theorem. Finally, four control strategies provide good quality of output voltage in the presence of uncertainties and interactions. Comparative simulations are carried out on cascaded buck and boost converters to validate the effectiveness and performance of the designed methods.     

基于SG的Buck变换器自适应反步法控制

针对Buck变换器的非线性特性,考虑在电感电流连续导通模式下的数学模型,采用自适应反步法设计其闭环控制器,同时基于System Generator提出了数字控制器的实现方法,并分析了其负载扰动和电源扰动特性,将仿真结果与PI控制方式相比较,结果表明自适应反步控制的优越性和System Generator设计开发的有效性,为FPGA实现Buck变换器的数字控制器提供了新的设计流程,也为进一步研究其他DC-DC变换器的非线性控制提供了新思路.     

Adaptive sliding mode control of interleaved parallel boost converter for fuel cell energy generation system

This paper deals with the problem of controlling energy generation systems including fuel cells (FCs) and interleaved boost power converters. The proposed nonlinear adaptive controller is designed using sliding mode control (SMC) technique based on the system nonlinear model. The latter accounts for the boost converter large-signal dynamics as well as for the fuel-cell nonlinear characteristics. The adaptive nonlinear controller involves online estimation of the DC bus impedance ‘seen’ by the converter. The control objective is threefold: (i) asymptotic stability of the closed loop system, (ii) output voltage regulation under bus impedance uncertainties and (iii) equal current sharing between modules. It is formally shown, using theoretical analysis and simulations, that the developed adaptive controller actually meets its control objectives.     

Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

Smart grids and their technologies transform the traditional electric grids to assure safe, secure, cost-effective, and reliable power transmission. Non-linear phenomena in power systems, such as voltage collapse and oscillatory phenomena, can be investigated by chaos theory. Recently, renewable energy resources, such as wind turbines, and solar photovoltaic (PV) arrays, have been widely used for electric power generation. The design of the controller for the direct Current (DC) converter in a PV system is performed based on the linearized model at an appropriate operating point. However, these operating points are ever-changing in a PV system, and the design of the controller is usually accomplished based on a low irradiance level. This study designs a fractional-order proportional-integrated-derivative (FOPID) controller using deep learning (DL) with quasi-oppositional Archimedes Optimization algorithm (FOPID-QOAOA) for cascaded DC-DC converters in micro-grid applications. The presented FOPID-QOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter. In addition, the proposed model develops a FOPID controller using a stacked sparse autoencoder (SSAE) model to regulate the converter output voltage. To tune the hyper-parameters related to the SSAE model, the QOAOA is derived by the including of the quasi-oppositional based learning (QOBL) with traditional AOA. Moreover, an objective function with the including of the integral of time multiplied by squared error (ITSE) is considered in this study. For validating the efficiency of the FOPID-QOAOA method, a sequence of simulations was performed under distinct aspects. A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.     

Control of DC–DC power converters in the presence of coil magnetic saturation

Engineers generally design control for power converters based on standard models that assume the involved passive components to be linear. In reality, the magnetic characteristics of these components are nonlinear (especially in the presence of large magnetic flux density in the ferromagnetic core). This nonlinearity, which is of the saturation type, is particularly pronounced in higher power coils. Therefore, a controller obtained from the standard models may not achieve the performance it is designed for under operational conditions where the nonlinearity of the components is not negligible. In this paper, the authors investigate the effect of coil magnetic saturation for certain converters is investigated. It is shown that the control performances actually deteriorate if such a feature is not accounted for in the converter modeling. A solution that explicitly accounts for the nonlinearity of coil characteristics is developed for two converters.     

PWM开关变换器积分函数非线性闭环控制系统稳定性的分析

以Buck电路为例,提出一种积分 九闭环控制策略以实现PWM开关变换器控制目标,为保证该非线性控制系统工作稳定,本文对积分函数闭环控制系统的稳定性进行分析。