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.     

励磁输入动态不确定的同步发电机鲁棒控制

非线性励磁控制如果没有考虑励磁输入存在的不确定性,可能影响其实际的控制效果。为了减小励磁输入动态不确定对发电机系统的影响,利用积分反推方法设计名义系统的虚控制,并加入非线性动态阻尼项对虚控制进行再设计,得到系统的鲁棒非线性控制率。控制率的设计未采用任何线性化方法,充分利用了系统的非线性特性。通过仿真试验,证实了该方法在输入动态不确定子系统满足最小相位、相对阶为零的条件下,可以保证发电机的功角、输出电压稳定。     

Robust stabilization of nonlinear cascaded systems

This paper considers the problem of robust stabilization for an uncertain nonlinear system which is a cascaded interconnection of two subsystems. Both of the subsystems are allowed to be nonlinear, multi-variable, and containing uncertain parameters. We present a new approach to designing stabilizing controllers which assure both robust global asymptotic stability and local quadratic stability. Compared with existing results, the assumptions required for such robust stabilizing controllers to exist are significantly simplified.     

一种经过模糊改良的smith控制器

对传统基于精确对象模型的smith预估控制器进行改良。传统smith预估控制器在估计模型与实际对象有差异时,控制效果不尽人意。针对这一缺点,该文将这种差异视为增益的差异所引起,通过引入估计模型增益修正环节,利用模糊控制器,尽可能将差异消除。并对传统smith预估控制器的结构进行改进,去掉外反馈回路,增加稳定性;加入积分校正环节。达到减小稳态误差的目的,使改良后的smith控制器在一定的模型误差范围内得到较好的控制品质。经仿真研究证明了该方法的有效性。     

Decentralized adaptive control of electrically-driven manipulators

This paper presents two new decentralized strategies for motion control of uncertain electrically-driven manipulators. The first controller is an adaptive position regulation scheme which ensures semiglobal asymptotic convergence of the position error if no external disturbances are present and semiglobal convergence of the error to an arbitrarily small neighborhood of zero in the presence of bounded disturbances. It is shown that the regulation scheme can be modified to provide accurate trajectory tracking control through the introduction of adaptive feedforward elements in the control law; this second control strategy retains the simple decentralized structure of the first controller and ensures arbitrarily accurate tracking in the presence of bounded disturbances. Each of the adaptive schemes is very efficient computationally and requires virtually no information concerning either the manipulator or actuator models. The results of computer simulations and laboratory experiments with both terrestrial and space manipulators demonstrate that accurate and robust motion control can be achieved by using the proposed approach.     

鲁棒自适应反推控制器的降阶设计

For a class of systems with unmodeled dynamics, robust adaptive stabilization problem is considered in this paper. Firstly, by a series of coordinate changes, the original system is re-parameterized. Then, by introducing a reduced-order observer, an error system is obtained. Based on the system, a reduced-order adaptive backstepping controller design scheme is given. It is proved that all the signals in the adaptive control system are globally uniformly bounded, and the regulation error converges to zero asymptotically. Due to the order deduction of the controller, the design scheme in this paper has more practical values. A simulation example further demonstrates the efficiency of the control scheme.     

基于Smith预估补偿的网络控制系统仿真研究

针对网络控制系统中前向通道和反馈通道同时存在随机延迟的问题,提出了一种基于神经元控制器的Smith预估器的设计方案。利用根轨迹法分析了随机延迟对网络控制系统稳定性的影响。结合不完全微分先行PID控制算法,建立了Smith预估模型,在模型不完全匹配的情况下得到较好的控制性能。以直流电机为被控对象进行仿真研究,仿真结果表明该方法的有效性。     

执行器有故障的多输入单输出系统的自适应输出反馈控制

对一类控制增益符号未知且执行器有故障的输出反馈多输入单输出非线性系统,提出了一种后推容错控制方案.该方案在系统状态不可量测的情况下,利用Nussbaum函数处理符号未知的常数增益,并通过构造K-滤波器来估计了系统不可量测的状态.在容错控制器设计过程中,引入变能量函数来处理利用虚拟控制律所无法抵消的部分.与现有研宄成果相比,放宽了未知增益需要上下界均为已知的假设条件.最后,通过选取合适的李雅普诺夫函数,证明了闭环系统所有信号半全局一致终结有界,且跟踪误差收敛到原点的一个小邻域内.仿真结果表明了所提控制方法的有效性.     

基于神经网络的机电伺服系统非线性控制

针对机电伺服系统精确动力学模型难以获取以及系统状态信息的测量易受噪声影响的问题,设计一种基于指令滤波与神经网络相结合的非线性反步控制算法,该算法能够有效地补偿未建模动态和外部扰动对机电伺服系统的影响.首先,引入指令滤波器来获取已知信号的微分估计并处理噪声;其次,利用神经网络估计未知的系统动态,包括未建模的摩擦和外部干扰;然后,神经网络权值的更新律通过梯度下降算法在线实现,没有离线学习阶段;最后,利用李雅普诺夫函数分析方法证明闭环系统的稳定性.为验证所提出算法的有效性,在机电伺服实验平台上进行大量对比实验,实验结果表明,所提出的算法具有良好的控制效果,对系统不确定性和外部干扰具有良好的鲁棒性.     

改进型Smith预估器在工业中的应用

本文以大时滞温度控制系统为研究对象，提出一种改进型Smith预估控制算法，从而解决在工业生产过程中被控量的纯滞后问题．     

四维混沌系统的递归反步非线性控制

针对四维超混沌Lü系统,Lorenz-Stenflo（LS）系统,Qi混沌系统,利用递归反步非线性控制方法,对系统的各状态进行了控制。同时对每个系统,运用Lyapunov稳定性理论,并通过V函数对受控系统进行了分析。最后通过数值示例进行仿真,对文中论述进行了强有力的验证。     

Lorenz系统的分数阶控制算法

引入了分数阶微积分理论及其在控制和混沌系统研究中的应用.基于此种理论,针对参数未知的Lorenz系统利用backstepping的方法,设计出了作用在第三个状态上的分数阶控制器,此种控制算法能够避免奇异点出现并且使得整个闭环系统稳定.它还可以通过分数阶阶次的选择得到不同的控制效果来满足控制工程中的各种需要.仿真试验验证了此算法的有效性.     

串联模糊控制器在位置控制中的应用

模糊控制在控制工程领域越来越得到广泛的应用,尤其是在对象为非线性系统时。文章介绍了模糊控制在位置控制中的应用,与一般的模糊控制系统不同,其模糊控制器由两个模糊控制单元串联而成,实验结果表明控制效果好。     

Robust adaptive tracking control of uncertain systems with time-varying input delays

In this paper, the problem of robust adaptive tracking control of uncertain systems with time-varying input delays is studied. Under some mild assumptions, a robust adaptive controller is designed by using adaptive backstepping technique such that the system is globally stable and the system output can track a given reference signal. At the same time, a root mean square type of bound is obtained for the tracking error as a function of design parameters and thus can be adjusted. Finally, one numerical example is given to show the effectiveness of the proposed scheme.     

含未建模动态的非线性系统输出反馈DSC设计

针对一类含有未建模动态和未知控制增益符号的非线性系统,提出了一种输出反馈自适应跟踪控制方案.首先利用Kreisselmeier观测器实现了不可测状态的估计,在此基础上以回归设计方式设计了输出反馈动态面控制系统,通过引入Nussbaum函数解决了控制方向未知问题.该方案解克服了传统反推控制方法中的微分爆炸现象,并且所有未...     

舱温控制系统模型及算法研究

数字式闭环控制在机载机电系统中应用广泛,选取典型的舱温控制系统作为研究对象;在Matlab中使用Simulink搭建系统模型,采用针对纯滞后系统的Smith控制算法进行仿真研究,并根据仿真模型建立系统原型平台,平台包括模型仿真机、仿真目标机以及控制器软件、人机交互界面;仿真结果与现场运行实验结果均表明Smith控制算法可以满足舱体温度系统控制要求,同时通过对闭环数字控制技术的方法和流程进行摸索,掌握了控制律软件设计与实现的快速原型技术,便于后续项目的工程应用。