Fault‐tolerant control for wireless networked control systems with an integrated scheduler

This paper addresses a study of fault‐tolerant control (FTC) for wireless networked control systems (WNCSs) in industrial automatic processes. The WNCSs is composed of many subsystems, which operate with different sampling cycles. In order to meet the real‐time requirements and ensure a deterministic data transmission, the time division multiple access (TDMA) mechanism is adopted in WNCSs. The data in WNCSs are transmitted following a TDMA‐based scheduler. According to the periodicity, WNCSs integrated with the scheduler is first formulated as discrete linear time periodic systems (LTPSs). Afterwards, a fault estimation method for LTPSs is developed under a H_∞ performance specification with a regional pole constraint. With the achieved state observer and fault estimator, an FTC strategy for LTPSs is explored. Finally, the proposed methods are verified on a physical experimental WiNC platform. Copyright © 2016 John Wiley & Sons, Ltd.     

交流配电系统中微网群管控一体化策略

微网群位于多节点配电网系统中,目前的研究大多只针对微网群的控制策略或能量调度问题,没有同时针对这两种问题进行的研究.因此,提出一种上层调度与下层控制联合优化的管控一体化策略.上层调度系统采用基于二阶锥优化的最优潮流算法对多节点系统进行能量调度,并与节点内微网群实时信息交互.下层微网群控制系统中提出一种改进的混合优化控制...     

Extended-state-observer-based dynamic surface control of flexible-joint robot systems with input saturation

This article presents an extended-state-observer-based dynamic surface control approach for flexible-joint robot systems with asymmetric input saturation and large unknown dynamic knowledge. Traditional controllers for flexible-joint robot systems usually use approximation technology to deal with unknown dynamics knowledge. Unlike the traditional control algorithm, this article utilizes an extended state observer to estimate the unknown dynamics. For the closed-loop system, the delay strategy handles the time-scale separation issue, the filtering system overcomes the “explosion of differentiation” caused by the repeated differentiation of auxiliary control signals, and the mean-value-theorem solves the input saturation problem of the actuator. The stability analysis implies that estimation errors of extended state observers (ESOs) and other state variables are semiglobally uniformly ultimately bounded. Compared with fuzzy control algorithms, the novel ESO-based dynamic surface control approach not only omits online learning time but also uses only a few control parameters to obtain satisfactory tracking performance. Finally, a comparison simulation experiment is provided to illustrate the effectiveness of the gained conclusions.     

A dual filtering scheme for nonlinear active noise control

Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered‐x least mean squares (LMS) or filtered‐u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive finite‐dimensional optimal linear filtering of nD smooth Gaussian random fields

This paper deals with the optimal filtering problem of nD sampled, Gaussian random fields. The filtering algorithm is based on a state‐space signal model analytically derived from the assumption that the continuous Gaussian random field can be well approximated, almost everywhere, by a continuously differentiable nD surface. An appealing feature of the proposed optimal filter is that it is not based on nD strip processing schemes. The filtering algorithm has a structure which is recursive both with respect to the point‐to‐point scanning procedure of the sampled field and to the dimensionality of the estimate computed at each point. This greatly reduces the numerical complexity of the filtering scheme. The filtering algorithm requires the knowledge of some statistical parameters of the random field. For a greater generality, a procedure for the adaptive estimation of these parameters is also provided. Numerical results are reported to illustrate the applicability and performance of the proposed filter. Copyright © 2006 John Wiley & Sons, Ltd.     

基于准PR控制的逆变器优化控制

逆变器并网控制过程中往往利用比例积分（PI）控制器追踪信号,然而在正弦电流信号的追踪过程中往往由于冲击性负载的作用,给系统带来稳定性误差和系统扰动等问题,本文提出了一种基于比例谐振（PR）控制技术的准比例谐振（PR）逆变器优化控制策略。首先对PR控制器的原理进行了介绍,通过对PR控制器的改进构成了准PR控制器,重点分析了准PR控制器控制参数对系统性能的影响,从而确定系统最优时控制器的参数,并在准PR控制的基础上添加谐波补偿环,有效消除特定次数谐波。利用MATLAB/Simulink进行仿真实验,对PI控制器与准PR控制器的实验结果进行对比分析,验证了改进的准PR控制能够在逆变器并网系统中实现无静差追踪和稳态误差消除,保证了系统的动态稳定性。     

Optimal slip ratio estimator for traction control system of electric vehicle based on fuzzy inference

In this paper we propose an optimal slip ratio estimation method based on fuzzy inference. One of the major advantages of electric vehicles is the quick and precise torque response of the electric motor, which realizes a novel traction control system. To prevent skidding, optimal slip ratio control has been successfully developed. It maintains the slip ratio at the optimum value that gives the maximum driving force. The remaining problem is how to generate the optimal slip ratio command sent to the controller. First we show that effective estimation of the optimal slip ratio is difficult to perform by the simple gradient method, which is a well‐known optimization method. But various experimentally obtained data can be easily incorporated into fuzzy inference, and therefore its estimation performance can be easily improved by the accumulation of human experience. This is a major advantage in the nonlinear estimation of real road‐tire characteristics. The effectiveness of the proposed estimation and control methods is confirmed by numerical simulation. © 2001 Scripta Technica, Electr Eng Jpn, 135(3): 56–63, 2001     

带指令滤波的直驱永磁风机自适应反推积分滑模控制

针对直驱式永磁同步风力发电系统最大功率跟踪问题,提出一种带指令滤波的自适应反推积分滑模控制器。构造了直驱式永磁风力发电系统的非线性模型。基于反推法设计控制器,并引入二阶滑模微分器充当指令滤波器,避免传统反推控制中计算膨胀和控制器饱和问题,并针对滤波误差设计补偿信号。考虑系统运行过程中参数的变化和系统未建模部分的影响,引入投影自适应算法提高系统的动态性能,并确保自适应估计值是有界的。同时为提高系统的鲁棒性,引入积分滑模控制,利用李雅普诺夫方法证明了系统的稳定性。仿真结果验证了所设计控制器的有效性。与传统PI控制、指令滤波反推控制器相比,所设计控制器具有更好的响应速度和鲁棒性。     

A decentralized control system for stabilizing multimachine power systems

A decentralized control system is studied for stabilizing multimachine power systems. A longitudinal power system with three areas, each having one machine, is considered in this study. A decentralized control design method is proposed, which is based on the optimal regulator theory. First a centralized control system is designed without any consideration on whether state variables are all available or not. Second a pseudo-decentralized control system is designed by omitting control gains corresponding to state variables which give hardly any effects on the power system stability. It is found that only one variable of phase angle of each machine is absolutely necessary for the pseudo-decentralized control system. This leads to an idea based on power system engineering, that is to say, new variables of tieline power flow are introduced in the decentralized control system design to substitute for the phase angle of each machine. Thus a decentralized control system for power system stability can be designed using the new variables of tieline power flow. It is demonstrated from simulation studies that the decentralized control system improves even longitudinal power system stability as well as the centralized control system.     

Gain conditions and convergence of simple adaptive control

Recent publications have presented successful implementations of simple direct adaptive control techniques in various applications. However, they also expose the fact that the convergence of the adaptive gains has remained uncertain. The gains may not converge to the ideal constant control gains predicted by the underlying linear time‐invariant system considerations. As those prior conditions that were also needed for stability may not hold, this conclusion may raise doubts about the robustness of the adaptive system. This paper intends to show that the adaptive control performs perfect tracking even when the linear time‐invariant solution does not exist. It is shown that the adaptation performs a ‘steepest descent’ minimization of the errors, ultimately ending with the appropriate set of control gains that fit the particular input command and initial conditions. The adaptive gains do asymptotically reach an appropriate set of bounded constant ideal gain values that solve the problem at task. Copyright © 2004 John Wiley & Sons, Ltd.     

Optimal load frequency control through combined state and control gain estimation for noisy measurements

Combined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter. This results in producing controller gains that are capable of controlling the system in response to changes in load demand, system parameter variation and measurement noise.     

基于2阶段家族保护遗传算法的电压稳定霍普夫分岔控制

系统的电压稳定裕度由亚临界霍普夫分岔值表征,如何延迟甚至消除霍普夫分岔,提高稳定裕度,对于防止系统电压失稳具有重要意义。根据一种新的霍普夫分岔指标建立了电压稳定霍普夫分岔控制的最优化模型。该模型考虑了更为切合实际的约束条件,包括系统阻尼限制、PV节点无功出力限制、励磁电压限制以及各节点电压限制等。为有效求解该优化模型,提出了一种2阶段家族保护遗传算法。算法利用混沌变量的遍历性生成初始种群,第1阶段完成家族内部的选择,使得每个家族成员都是优良个体;第2阶段实现家族间的选择,这是一种优–优选择,使算法能以更快的速度收敛到全局最优解。通过对测试函数和WSCC-9节点系统的仿真表明该模型和算法的有效性和可行性。     

Amplitude-constrained adaptive lqg control of first-order systems

This paper considers the problem of adaptive LQG control design when the system control input is subject to an amplitude constraint. A first-order stochastic system is considered mainly because its output can be satisfactorily described by a Gaussian probability density function (PDF) or can be calculated numerically. In this light, two algorithms are proposed. In the first algorithm an approach given by Toivonen has been adopted where the PDF of the output is approximated by the Gaussian density in order to develop a suboptimal adaptive feedback controller. the approach in the second algorithm is based on the numerically calculated PDF of the system output yielding a near-optimal feedback gain. the proposed algorithms are compared with existing ones via simulations.     

Conditions for existence of optimal solution in the synthesis of a multivariable control problem for power systems by using optimization problem

This paper considers the synthesis of a multivariable control problem for power systems by using the optimization method. The power system dynamics based on the usual assumptions can be formulated as a class of nonlinear dynamic systems which contain the sinusoidal functions associated with the power torque-angle curve of the ac generator. Then, an optimal control law, which retains the principal nonlinearity of the power system, is derived by using the optimization techniques in the linear optimal control theory. The feedback gain matrices are obtained by solving matrix equations which appear in the optimization procedure. In this paper, the conditions for the existence of an optimal solution of these matrix equations are derived by using Anderson's theorem. The stability criterion for the control system is discussed from the viewpoint of a Lur'ye-type Lyapunov function. Thus, the optimal control law can be applied to a class of nonlinear dynamical systems represented by the ordinary differential equations divided into linear and nonlinear terms.     

Multiple characteristic model‐based golden‐section adaptive control: Stability and optimization

The characteristic model‐based golden‐section adaptive control (CM‐GSAC) law has been developed for over 20 years in China with a broad range of applications in various fields. However, quite a few theoretical problems remain open despite its satisfying performance in practice. This paper revisits the stability of the CM‐GSAC from its very beginning and explores the underlying implications of the so‐called golden‐section parameter l₂≈0.618. The closed‐loop system, which consists of the CM and the GSAC, is a discrete time‐varying system, and its stability is discussed from three perspectives. First, attentions have been paid to select the optimal controller coefficients such that the closed‐loop system exhibits the best transient performance in the worst case. Second, efforts are made to improve the robustness in the presence of parameter estimation errors, which provide another choice when designing the adaptive controller. Finally, by measuring the slowly time‐varying nature in an explicit inequality form, a bridge is built between the instantaneous stability and the time‐varying stability. In order to relax the constraints on the parameter bounds of the CM, the GSAC is further extended to multiple CMs, which shows more satisfying tracking performance than that of the traditional multiple model adaptive control method. Copyright © 2014 John Wiley & Sons, Ltd.     

Coordinated voltage control of wind-penetrated power systems via state feedback control

This paper presents an optimal coordinated voltage control scheme for preserving long-term voltage stability of power systems. Linear quadratic integral (LQI) controller is employed to construct this scheme. Also, this paper considers the detailed dynamic model of doubly-fed induction generator (DFIG) wind turbine, synchronous generators, over excitation limiter (OEL) and under-load tap changer (ULTC) system, which are important elements influencing voltage stability of power systems. The proposed approach at each time instance involves following two major steps: First, the power system nonlinear equations are linearized and optimal controllers are obtained by LQI technique. Then, in the second step the system dynamic behavior is investigated via time-domain simulations by applying the attained optimal control signals at the first step. The impact of the proposed coordinated voltage control scheme is evaluated by time domain simulations on a well-known test system, under variable wind speed and fault conditions.     

多功能柔性功率调节器三闭环控制方式的研究

多功能柔性功率调节器(FPC)利用交流励磁的变速恒频双馈电机带动飞轮运转进行储能,可用于电力调峰或系统稳定控制、提高电能质量以及无功补偿等各种领域。本文对FPC转子侧变换器的矢量控制方式进行研究,提出对转子侧q轴电流采用速度外环、功率中环、电流内环的三闭环控制方式,以满足装置对于速度和功率同时控制的要求。     

改进的变尺度法在自动电压控制(AVC)中的应用

采用分层分区、集中和分散控制相结合的思想,实现了电力系统无功电压的实时在线控制。控制中心层利用改进的变尺度法(DFP)在线计算满足约束条件的各节点的最优无功配置,通过SCADA/EMS系统实时传送到节点层;节点层根据最优的无功配置,利用模糊控制理论确定有载调压变压器、电容器等电压无功调节设备的调节策略。本文重点论述了中心控制层中改进的DFP的基本算法、实时控制的优势,并对实时控制过程中的相关问题提供了解决方案,实现了实时控制的各种要求。通过对IEEE6节点、IEEE30节点和安徽电网等的计算分析,结果充分     

Adaptive control of non-minimum phase systems

Adaptive control schemes based on the recursive least squares (RLS) estimation technique are analysed in this paper. First, under the assumption that the asymptotically estimated system does not present pole-zero cancellations, a general stability result is proven. Such a result does not require the system to be minimum phase. Then it is shown that on the grounds of the stability property the control system attains asymptotic optimality. The results of the present paper rely on previous work by Rootzen and Sternby concerning the consistency of RLS estimates and are intended as a first step in the extension of Kumar's work in adaptive control to non-minimum phase systems.     

基于多变量校正的MMC快速有限集模型预测控制策略

有限集模型预测控制方法(finite control set model predictive control, FCS-MPC)因其能够实现多目标的控制,在模块化多电平变换器(modular multilevel converter, MMC)中得到广泛应用。随着子模块数量增加,模型预测控制方法计算量呈指数增长,面临计算复杂度高、权重因子难以整定等问题。为了解决上述问题,提出了一种基于多变量校正控制集的MMC模型预测控制策略(multi-variate adjusting set predictive control, MAS-MPC)。该策略基于输出电流与桥臂电压差对子模块投入控制集进行快速校正,通过评估两个成本函数得到最优开关矢量。此外,提出了一种基于分化中项的电容电压平衡方案,可以有效降低排序算法的复杂度。为了验证所提策略的有效性,使用Matlab/Simulink软件平台搭建了10电平的三相MMC系统,并与传统方案进行比较。所提方案在降低输出电流与环流的谐波含量的同时,大幅减少了系统的计算量,使得系统具有更快速的动态响应速度。