广义子系统的非线性控制及其在电力系统分散控制中的应用

针对一类关联可测非线性广义子系统,研究了其全局镇定控制问题。首先通过微分同胚变换实现了子系统模型的等价变换。然后纂于反推控制方法,在充分考虑大系统其余部分关联输入对予系统影响的情况下,给出了非线性控制器的设计方案,使得闭环子系统是渐近稳定的。最后将该方法应用于电力系统中的一台同步发电机的分散非线性汽门控制器设计,仿真结果验证了所提方案的有效性。     

同步发电机非线性广义子系统模型的解耦控制

针对基于非线性奇异子系统模型的同步发电机,本文利用逆系统方法,研究了其励磁汽门综合控制的输入输出线性化解耦问题.同步发电机本质上是电力大系统中的一个非线性奇异子系统,同步发电机的解耦控制是使系统达到优良性能的重要途径之一.本文提出一种递推算法,利用逆系统方法将被控系统转化为两个独立的积分型线性子系统,实现了系统的动态解耦.仿真结果表明了本文所提控制方法的有效性.     

不确定非线性系统的自适应反推高阶终端滑模控制

针对一类非匹配不确定非线性系统,提出一种神经网络自适应反推高阶终端滑模控制方案.反推设计的前1步利用神经网络逼近未知非线性函数,结合动态面控制设计虚拟控制律,避免传统反推设计存在的计算复杂性问题,并抑制非匹配不确定性的影响;第步结合非奇异终端滑模设计高阶滑模控制律,去除控制抖振,使系统对于匹配和非匹配不确定性均具有鲁棒性.理论分析证明了闭环系统状态半全局一致终结有界,仿真结果表明了所提出方法的有效性.     

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。     

一类非线性系统的故障诊断

讨论了一类非线性系统执行器故障的诊断问题.首先应用已有的状态和输出变换, 将一类非线性系统转化为两个低维子系统,然后利用滑模观测器设计理论和等价控制的概念 重构执行器故障.     

双电机驱动伺服系统径向基函数神经网络反推自适应控制

双电机驱动伺服系统中存在齿隙非线性环节,为了削弱齿隙非线性对系统的动态和稳态性能产生的不利影响,本文提出了一种新的自适应控制方法.首先给出了系统的状态空间模型并分析了双电机同步联动控制的原理,然后应用改进的反推方法,在考虑系统所有的状态变量都能收敛的基础上,引入虚拟控制量,通过逐步递推选择Lyapunov函数,利用径向基函数(radial basis function, RBF)神经网络在线逼近系统中的不确定函数,设计了基于状态反馈的RBF神经网络反推自适应控制器,并进行了稳定性分析.将单纯的反推控制和RBF神经网络反推自适应控制的仿真结果对比,发现后者的优越性高于前者.最后在实际系统中进行试验,验证了所提控制策略的可行性.     

一类输入饱和非仿射非线性系统的非线性动态逆控制

输入饱和是实际系统中经常遇到的问题,很多已有的控制方法要求被控系统具有仿射结构.本文针对一类具有输入饱和的非仿射纯反馈非线性系统提出了一种基于奇异值摄动理论的非线性动态逆控制方法.首先构建一个快变子系统,在慢时间尺度下将非仿射非线性系统转换为具有仿射结构的线性系统,从而应用已有的控制算法实现控制目的.为了消除输入饱和带...     

非线性微分——代数子系统的逆系统的构造

对于一类非线性微分-代数(Differential-algebraic equation, DAE)子系统, 将非线性常微分方程(Ordinary differential equation, ODE)系统的逆系统方法进行了完全扩展. 首先对此类非线性DAE子系统提出的物理背景和系统特性进行了详细阐述. 然后给出了非线性DAE子系统的逆系统定义, 包括单位右逆系统和 α 阶积分右逆系统. 接下来提出一种递归算法, 利用此算法给出了被控系统可逆的充要条件, 并构造了物理可实现的控制器, 实现了非线性DAE子系统的线性化解耦. 最后基于本文所提出的控制方法, 研究了电力系统同步发电机组励磁汽门综合控制的线性化解耦问题.     

一类非仿射非线性不确定系统自适应鲁棒控制

针对一类结构和参数均未知且控制方向未知的不确定非仿射非线性系统,提出了一种鲁棒自适应控制算法.基于中值定理将非仿射系统转化为具有线性结构的时变系统,在此基础上,利用参数投影估计算法对有界时变参数进行辨识,参数辨识误差和外界干扰采用非线性阻尼项进行补偿.同时将动态面控制(DSC)和反推法相结合,消除了反推法的计算膨胀问题,并采用Nussbaum型函数处理系统中方向未知的不确定控制增益函数,避免了可能存在的控制器奇异值问题.最后,采用解耦反推,基于李雅普诺夫稳定性定理证明了闭环系统的半全局一致最终有界.仿真结果验证了所设计控制方案的可行性与有效性.     

一类非线性微分代数子系统的逆系统控制及应用

对于指数1且关联可测的非线性微分-代数子系统,研究其逆系统控制方法,并将结果应用于电力系统元件分散控制.首先描述了此类非线性微分-代数子系统的物理背景和系统特性,并给出了非线性微分-代数子系统的α阶积分右逆系统和可逆的定义;然后给出了一种递归算法,以此来判别被控系统的可逆性,并构造出由状态反馈和动态补偿实现的α阶积分右逆系统,实现了复合系统的线性化解耦;最后针对多机电力系统中的一台同步发电机,应用所提出的方法研究其励磁控制电压问题.仿真结果验证了所提出方法的有效性.     

V1 non-linear properties emerge from local-to-global non-linear ICA

It has been argued that the aim of non-linearities in different visual and auditory mechanisms may be to remove the relations between the coefficients of the signal after global linear ICA-like stages. Specifically, in Schwartz and Simoncelli (2001), it was shown that masking effects are reproduced by fitting the parameters of a particular non-linearity in order to remove the dependencies between the energy of wavelet coefficients. In this work, we present a different result that supports the same efficient encoding hypothesis. However, this result is more general because, instead of assuming any specific functional form for the non-linearity, we show that by using an unconstrained approach, masking-like behavior emerges directly from natural images. This result is an additional indication that Barlow's efficient encoding hypothesis may explain not only the shape of receptive fields of V1 sensors but also their non-linear behavior.     

Structural identifiability of non-linear systems using linear/non-linear splitting

In this paper, the local state space isomorphism theorem for non-linear systems is used to analyse structural identifiability. In particular it is shown that, under certain restrictions, it is possible to perform a linear/non-linear splitting of the analysis. The relatively straightforward linear analysis then restricts the class of local diffeomorphic transformations as given by the non-linear state space isomorphism theorem. This, in turn, leads to possible simplifications to the subsequent non-linear analysis by providing an efficient means for calculating the local state diffeomorphism.     

Partially observed non-linear risk-sensitive optimal stopping control for non-linear discrete-time systems

In this paper we introduce and solve the partially observed optimal stopping non-linear risk-sensitive stochastic control problem for discrete-time non-linear systems. The presented results are closely related to previous results for finite horizon partially observed risk-sensitive stochastic control problem. An information state approach is used and a new (three-way) separation principle established that leads to a forward dynamic programming equation and a backward dynamic programming inequality equation (both infinite dimensional). A verification theorem is given that establishes the optimal control and optimal stopping time. The risk-neutral optimal stopping stochastic control problem is also discussed.     

Linear state estimators for non-linear stochastic systems with noisy non-linear observations

The design of linear filters is considered for reconstructing the state of a class of discrete-time non-linear stochastic systems using noise-corrupted measurements. It is shown that for systems with mean-square stable dynamics, it is always possible to guarantee stable estimation schemes. This result is used to prove that a mean–square optimal one-step predictor has stable error dynamics and also to generate other stable predictors.     

Multivariable control experiments of non-linear chemical processes using non-linear feedback transformation

The decoupling and linearization control theory based on non-linear feedback transformation for non-linear systems was applied to two non-linear and interactive chemical control processes. The first is a level and temperature control process and the second a level and pH control process. To compensate for the mismatch between the real process and the process model, external PI controllers, which track the outputs from the reference model, were added. The experimental results showed that even if the process is both non-linear and interactive and the process model is not exactly known, a satisfactory control performance can be obtained by applying this theory.     

Adaptive rejection of non-linear disturbances in extended non-linear output feedback systems

This paper deals with asymptotic rejection of disturbances generated from non-linear exosystems for uncertain nonlinear systems in an extended output feedback form, which allows the vector field coupled with the system input to have different nonlinear functions of the system output as its elements. A new internal model design is proposed to deal with nonlinear functions of the system output that are coupled with the input and the unknown disturbance. Adaptive control techniques are then used to deal with the uncertainty in the system. The proposed adaptive disturbance rejection algorithm with the new internal model design ensures the asymptotic rejection of the unknown nonlinear disturbance and the boundedness of all the variables.     

A non-linear system controller

A method is given for finding a feedback controller that forces the response of a given non-linear system to follow the response of a predetermined linear system. A test is provided that gives sufficient conditions for the existence of such a control. Algorithms are given that allow implementation on a digital computer. Possible solutions to some of the difficulties encountered in using this method are also investigated. Both the deterministic and stochastic cases are examined.     

Frequency domain analysis for non-linear Volterra systems with a general non-linear output function

For non-linear Volterra systems which include a non-linear state equation and a general non-linear output function, the system frequency response functions and some related frequency response characteristics are developed and discussed in this study. These new results establish the frequency response functions for this general form of non-linear systems by extending some existing theory, and provide an analytical insight into the relationship between model parameters and frequency response functions, and the relationship between model parameters and the magnitude bound of frequency response functions. Several examples are given to illustrate the new results.     

Adaptive non-linear compensation control based on neural networks for non-linear systems with time delay

This article investigates a new adaptive non-linear compensation controller for a class of time-delay non-linear systems with partly known dynamics. First, a non-linear neural-network(NN)-based identification model that includes a prior knowledge about the plant dynamics is discussed by using the approximation capabilities of NNs. Then, the adaptive non-linear compensation controller is developed to produce the desired tracking performance. The proposed controller based on the NN can reduce the effect of modelling uncertainties and provide the time-delay compensation, while stability of the closed-loop system is guaranteed. The effectiveness of the proposed scheme is demonstrated through the application to the control of a continuous stirred tank reactor.