基于SG的Boost变换器T-S模糊建模与控制

针对Boost变换器的非线性特性,基于分段线性电子电路仿真(PLECS)软件建立了电感电流连续状态下Boost变换器的仿真模型,采用T-S模糊控制方法设计了控制器,提出了一种用SG对Boost变换器控制器实现的新方法,并分析了负载及电源电压变动下的控制性能.仿真结果表明PLECS建模的正确性和T-S模糊控制器优越性,且验证System Generator设计开发的有效性.     

基于MIT MRAC的Buck变换器电压控制仿真研究

为改善Buck变换器的控制性能,对基于模型参考自适应控制的Buck变换器电压控制方案进行了研究．首先基于小信号扰动法建立了Buck变换器的小信号解析模型;然后分析给出了自适应参考模型的设计依据,总结了设计步骤,并对自适应控制器的设计方法、关键参数的取值和影响进行了归纳;最后在Matlab/Simulink平台下搭建了频域和时域仿真模型,对文中重要结论进行了验证,证实了MIT MRAC用于Buck变换器控制的可行性和有效性．     

DC/DC变换器的动态建模及负载扰动抑制研究

本文针对Buck DC/DC变换器的强非线性和负载变化的特点,建立了近似线性化模型,基于H ∞控制理论设计了扰动抑制鲁棒控制器.给出了求解Buck变换器反馈系统广义被控对象的方法,并利用Matlab鲁棒控制工具箱给出了求解控制器的一般步骤,最后的数字仿真验证了所提方法的有效性和正确性,H∞控制器能大大改善电力电子装置的抗干扰能力.     

基于U模型的Buck变换器控制器设计

为了提高Buck变换器控制系统的设计精度和效率,对Buck变换器非线性建模及控制器的设计进行了研究。结合U模型不损失NARMAX模型非线性特性且易于设计控制器的特点,提出了一个基于输入输出数据的NARMAX建模方法,根据模型的残差,进行了相关性检验以验证模型的有效性。通过将NARMAX模型转化为U模型并基于U模型设计了Buck变换器的极点配置控制器。仿真结果显示,基于U模型的Buck变换器极点配置控制器在调节时间和超调量控制上优于传统的PID控制器。     

基于交错Buck变换器的光伏系统MPPT控制

具有最大功率点跟踪(MPPT)功能的控制器常采用单相Buck变换器,输出功率稳定性差;鉴于此,提出将交错并联技术应用于Buck变换器,并以两相交错并联Buck变换器为例进行研究,减小输出电压、电流纹波,提高了输出功率稳定性,有利于实现MPPT控制;然后针对固定步长扰动观察法跟踪速度与精度之间的矛盾,研究了一种自适应变步长的算法,MATLAB仿真结果表明,改进算法具有较好的跟踪速度和稳态性能。     

电压型Buck电路控制器的复频域设计及仿真研究

通过分析电压型Buck电路控制器,在复频域下用模拟和数字两种方法对Buck变换器的补偿网络进行了设计.应用PSpice软件建立了系统的模拟电路模型,对系统的频率特性进行了仿真,并用Mathcad仿真软件对复频域下系统连续模型的频率特性进行了验证.然后运用根轨迹理论讨论了一种基于根轨迹和零极点配置的数字控制器设计方法,根据系统性能指标设计了补偿网络,并用Matlab仿真软件对控制系统离散数学模型进行了特性仿真.仿真结果表明用该方法所设计的控制器性能与模拟电路建立的控制器性能完全相同.经过离散化的控制器数学模型可以方便地在DSP等数字信号处理器上实现,为开关变换器的数字化控制提供了简便的实现方法.     

基于事件触发的Buck型DC-DC变换器有限时间控制

针对Buck型DC-DC变换器输出电压跟踪控制问题,提出了一种基于事件触发机制的有限时间控制方案。首先,将Buck变换器建模成一类反馈型非线性系统。然后,为能有效地避免通信资源的浪费,通过构造一种状态变换设计了一种事件触发机制;同时,利用反步法,设计了系统的状态反馈控制器,该控制器在事件触发时刻更新;然后,基于所设计的事件触发控制器,利用有限时间Lyapunov稳定性理论分析了系统的稳定性,并证明了所设计的控制方案不会发生Zeno现象;最后,通过Buck变换器仿真实例验证了所提出的事件触发控制方案的有效性,仿真结果表明了在所设计的控制方案下,Buck型DC-DC变换器的输出在有限时间内可以达到期望值,同时还能减少通信资源的浪费。     

DC／DC变换单神经元自适应PID控制器仿真研究

为解决传统PI控制对具有时变、非线性特性的DC/DC变换器动态控制性能不佳的问题,将人工神经网络与PID控制理论相结合,为DC/DC变换器设计了一种单神经元自适应PID控制器。该控制器算法简单,通过对加权系数的在线调整来实现自适应、自学习功能,从而满足DC/DC变换器的时变及非线性特性。以BUCK型变换器为例,建立了DC/DC变换器智能控制系统的仿真模型,在不同负载及参数变化的情况下与常规PI控制效果进行对比分析,结果表明,单神经元自适应PID控制器对DC/DC变换器具有很好的控制效果和鲁棒性。     

基于Riemann-Liouville分数阶模型的Buck变换器改进分数阶互补滑模控制

主要研究分数阶Buck变换器的互补滑模控制(CSMC)方法.首先,基于电子元件实际非整数阶的特性与Riemann-Liouville(R-L)定义相比,Caputo定义更能准确描述Buck变换器模型的结论,建立基于R-L定义的分数阶Buck变换器数学模型.然后,将参数不确定性和外部扰动统一为匹配干扰和不匹配干扰,建立两个分数阶干扰观测器(FDOB)分别实现对干扰及其分数阶导数的跟踪.进而,设计新型分数阶互补滑模面,利用CSMC的高精度和分数阶微积分的记忆特性提升滑模运动的鲁棒性和稳态精度;设计新型趋近律,提升趋近速度的同时保证滑模面邻域内的鲁棒性.最后,基于Mittag-Leffler稳定性理论证明滑模控制器的稳定性.仿真结果验证了所提出FDOB的优越性,控制器相比传统滑模方法能够得到更好的动态性能和更低的稳态误差.     

静电悬浮的NARX网络自适应逆控制

静电悬浮控制系统中存在建模不准确及对象扰动,传统控制器只能在动态控制精度和扰动消除性能之间折衷;为了克服其对控制器精度的影响,研究了带扰动消除的自适应逆控制算法.以非线性自回归动态神经网络进行正模型、逆模型以及扰动消除控制器的实时辨识,利用基于遗传算法的改进粒子群算法进行神经网络的更新,以提高自适应收敛速度和精度.设计了基于DSP与PC的仿真环境,分别部署静电悬浮虚拟被控对象和自适应逆控制算法,实现对控制算法的实时验证.结果表明所设计的控制结构与算法可以实现对静电悬浮的稳定控制与扰动消除.利用PC和相应的I/O接口,以及所部署的实时控制算法可以实现快速控制原型,为控制器的工程实现提供基础.     

Adaptive observer backstepping control using neural networks

This paper extends the application of neurocontrol approaches to a new class of nonlinear systems diffeomorphic to output feedback nonlinear systems with unmeasured states. A neural-based adaptive observer is introduced for state estimation as well as system identification using only output measurements during online operation. System identification is achieved via the online approximation of a priori unknown functions. The controller is designed using the backstepping control design procedure. Leakage terms in the adaptive laws and nonlinear damping terms in the backstepping controller are introduced to prevent instability from arising due to the inherent approximation error. A primary benefit of the online function approximation is the reduction of approximation errors, which allows reduction of both the observer and controller gains. A semi-global stability analysis for the proposed approach is provided and the feasibility is investigated by an illustrative simulation example.     

具有非线性不确定参数的电液伺服系统自适应backstepping控制

针对电液伺服系统中存在非线性不确定参数的问题,提出了一种采用积分型Lyapunov函数的自适应backstepping控制方法.首先定义积分型Lyapunov函数,将电液伺服系统中的非线性不确定参数转化为线性表示;然后逐步递推设计backstepping控制器,同时在控制律中加入阻尼项,从而补偿外界干扰对控制性能的影响;基于Lyapunov稳定性方法,设计了参数自适应律,并且在自适应律中引入充分光滑投影算子,实现对电液伺服系统中不确定参数漂移的抑制作用.搭建了AMESim与MATLAB的联合仿真平台,对所设计的自适应backstepping控制器进行仿真,作为对比,设计了不带有非线性参数估计的自适应backstepping控制器和PID算法.仿真表明,本文所设计的控制器具有良好的跟踪性能和补偿非线性不确定参数变化的能力.     

Adaptive backstepping‐based control design for uncertain nonlinear active suspension system with input delay

This paper addresses the control problem of adaptive backstepping control for a class of nonlinear active suspension systems considering the model uncertainties and actuator input delays and presents a novel adaptive backstepping‐based controller design method. Based on the established nonlinear active suspension model, a projector operator–based adaptive control law is first developed to estimate the uncertain sprung‐mass online, and then the desirable controller design and stability analysis are conducted by combining backstepping technique and Lyapunov stability theory, which can not only deal with the actuator input delay but also achieve better dynamics performances and safety constraints requirements of the closed‐loop control system. Furthermore, the relationship between the input delay and the state variables of this vehicle suspension system is derived to present a simple and effective method of calculating the critical input delay. Finally, a numerical simulation investigation is provided to illustrate the effectiveness of the proposed controller.     

B-spline backstepping control with derivative matrix estimation and its applications

A B-spline backstepping controller is proposed for a class of multiple-input multiple-output (MIMO) nonlinear systems. The control scheme incorporates the backstepping design technique with a B-spline neural network which is utilized to estimate the system dynamics. The B-spline neural network has the advantage of locally controlling its output behavior compared with other neural networks; therefore, it is very suitable to online estimate the system dynamics by tuning its interior parameters, including control points and knot points. Based on the mean-value theorem, the derivative of B-spline basis functions in relation to parameters can be estimated to online adjust these parameters. In addition, the validity of the proposed scheme is verified through an experiment on a servo motor system which is controlled by the output voltage of the Buck DC-DC converter.     

Event‐triggered adaptive backstepping control for parametric strict‐feedback nonlinear systems

This paper proposes a novel adaptive backstepping control method for parametric strict‐feedback nonlinear systems with event‐sampled state and input vectors via impulsive dynamical systems tools. In the design procedure, both the parameter estimator and the controller are aperiodically updated only at the event‐sampled instants. An adaptive event sampling condition is designed to determine the event sampling instants. A positive lower bound on the minimal intersample time is provided to avoid Zeno behavior. The closed‐loop stability of the adaptive event‐triggered control system is rigorously proved via Lyapunov analysis for both the continuous and jump dynamics. Compared with the periodic updates in the traditional adaptive backstepping design, the proposed method can reduce the computation and the transmission cost. The effectiveness of the proposed method is illustrated using 2 simulation examples.     

干扰下的无人直升机自适应反步法鲁棒跟踪控制

针对无人直升机干扰下的鲁棒轨迹跟踪问题,设计了一种自适应反步控制方法.鉴于作用在直升机上的干扰是产生跟踪误差的主要原因,该方法的主要思想是寻求一种方法来补偿这种干扰.首先,将未建模动态如外部阵风干扰、配平误差、机身、垂尾、平尾以及其他可忽略的动态产生的力和力矩看成一种组合干扰,从而建立了一个方便反步法控制器设计的简化模型.当设计好反步法控制器后,设计了一个非线性自适应律来估计这种组合干扰,并通过将干扰估计值整合到反步控制器中,使得闭环跟踪系统的鲁棒稳定性得到了保证,即基于李雅普诺夫稳定性理论证明了所设的控制器对于干扰主动阻隔,特别是低频干扰的主动阻隔是有效的.最后,两个仿真研究验证了该方法是优于常规反步法和积分反步法的.     

Adaptive neural network asymptotical tracking control for an uncertain nonlinear system with input quantisation

In this paper, the problem of adaptive neural network asymptotical tracking is investigated for a class of nonlinear system with unknown function, external disturbances and input quantisation. Based on neural network technique, an adaptive asymptotical tracking controller is provided for an uncertain nonlinear system via backstepping method. In order to reduce complexity of the control algorithm in the backstepping design process, a sliding mode differentiator is employed to estimate the virtual control law and only two parameters need to be estimated via adaptive control technique. The stability of the closed-loop system is analysed by using Lyapunov function method and zero-tracking error performance is obtained in the presence of unknown nonlinear function, external disturbances and input quantisation. Finally, an application example is employed to demonstrate the effectiveness of the proposed scheme.