Adaptive non-linear control with incomplete state information

Motivated by several recent adaptive non-linear control results which use either full-state or single-output feedback, we present two new adaptive design tools and show how they can be used to construct systematic design procedures for non-linear systems with incomplete state information. The main features of these procedures are illustrated on a simple third-order system. We also provide the geometric conditions which give a co-ordinate-free characterization of one of the partial-state-feedback forms to which these procedures are applicable.     

某200MW机组再热汽温控制系统的自适应控制

为了解决火电厂大惯性、大时滞的再热汽温控制难题,采用了模型参考自适应预估控制方法,在京能热电公司3号机组再热汽温控制系统得到了的成功应用。模型参考自适应预估控制方法通过对模型的在线跟踪来满足Smith预估器模型精度的要求,解决了控制系统中存在的大时滞、特性时变的问题。该方法可以在保留原有的PID控制算法的基础上,通过增加自适应前馈的办法方便地予以实现,可以充分利用原系统的控制组态。为了在HIACS-5000分散系统中实现上述算法,采用了带防火墙的单向控制网络获取过程输入数据,通过DCS的AI模件将自适应前馈输出送入HIACS-5000系统实现控制闭环。经改造再热汽温控制系统成功地实现了自动投运,并取得了良好的控制效果。     

Artificial intelligence in electric power systems: a survey of theJapanese industry

The author studied the Japanese involvement in the field of artificial intelligence (AI) and visited eight Japanese R&D laboratories, where he discussed the AI and expert system related activities in the power industry. A comprehensive look at the combined Japanese effort is provided. The current topics of interest are: AI and its application in power engineering; problems in AI applications development and their solutions; practical system examples; and AI applications to power systems of the future. Out of the 97 papers cited, ten were produced by electric utilities, ten by manufacturers, 17 by universities, and 60 were joint efforts. This shows the level and importance of joint collaborative research among the Japanese researchers. Even though they are working on many theoretical aspects of the AI technology, including automated knowledge acquisition and verification, they still use a significant amount of theoretical work done in the US for successful prototyping of AI based tools. The use of AI tools in the Japanese electric power industry is far more widespread than what is seen in the US or in Europe     

一类非仿射非线性系统的自适应模糊控制

为了讨论一类非仿射非线性系统自适应模糊控制问题,利用有关隐函数定理和泰勒公式,将系统由非仿射型转变为仿射型,基于滑模控制原理,并运用模糊逻辑系统对未知函数进行在线逼近,提出了一种具有监督器的自适应模糊控制方法。该方法在考虑到外界干扰的情况下,通过监督控制器保证闭环系统所有信号有界,通过引入最优逼近误差的自适应补偿项来消除建模误差的影响。通过Lyapunov方法,证明了跟踪误差收敛到零,仿真结果表明了该方法的有效性。     

一类不确定非线性系统的自适应模糊滑模控制

针对一类不确定非线性系统自适应模糊控制中,为了保证系统稳定性而附加监督控制问题,根据滑模控制原理并利用模糊系统的逼近能力,提出了一种Ⅰ型间接自适应模糊滑模控制方法。该方法取消了监督控制,用滑模控制器增加了逼近误差的自适应补偿,李雅普诺夫稳定性理论分析证明,控制系统全局稳定且跟踪误差收敛到零。将这种控制器应用到过程控制的典型对象液位控制中,仿真结果表明了该控制器的有效性和可行性。     

Fuzzy logic and neural networks in power electronics and drives

Artificial intelligence (AI) techniques are finding increased applications in science and engineering. The application of AI techniques to household appliances, power systems, industrial and transport systems, medical equipment, etc., is increasing daily. AI techniques have made inroads to power electronics and drives to give more user-friendly, efficient, and improved dynamic performance, and robust and intelligent products. AI is basically embedding human intelligence into a machine so that it can think like a human being. AI is superior to human intelligence in some aspects. The computer with embedded AI techniques can process problems extremely fast compared to human beings; it can work continuously without tiring, and its problem-solving capability is not-affected by emotions and other human shortcomings. Advanced control based on artificial intelligence techniques is called intelligent control. Unlike classical control, intelligent control strategy may not need the mathematical model of the plant. Intelligent control can be visualized as a type of adaptive control. In this article, the application of fuzzy logic and neural network techniques to power electronics and electrical drives has been briefly described     

一类不确定非线性系统的自适应模糊跟踪控制

针对一类不确定非线性连续系统，用模糊系统对未知函数进行逼近的基础上，利用Lyapunov稳定性理论，提出了一种新的自适应模糊跟踪控制算法，此算法的特点是，无论取多少条模糊系统的规则，自适应学习的参数只有一个，便于实现，而且还能确保闭环系统渐近稳定，并使系统的跟踪误差为零，仿真研究表明，所提出的算法是有效的。     

Adaptive control of non‐linear systems with unknown virtual control coefficients

This paper deals with state feedback adaptive control of parametric‐strict‐feedback (triangular) non‐linear systems with unknown virtual control coefficients. A priori knowledge of the signs of the virtual coefficients is not required, and control signals and adaptive laws are smooth. Asymptotic tracking of smooth reference signals is achieved while all the variables remain bounded. The proposed algorithms make use of backstepping and tuning functions, and enlarge the class of non‐linear systems with unknown parameters for which asymptotic output tracking can be achieved. Copyright © 2000 John Wiley & Sons, Ltd.     

Self‐tuning PID control of a brushless DC motor by adaptive interaction

In this paper, a self‐tuning algorithm for proportional integral derivative (PID) control based on the adaptive interaction (AI) approach theory efficiently used in artificial neural networks (ANNs) is proposed. In this approach, a system is decomposed into interconnected subsystems, and adaptation occurs in the interaction weights among these subsystems. The principle behind the adaptation algorithm is mathematically equivalent to a gradient descent algorithm. The same adaptation as the well‐known backpropagation algorithm (BPA) can be achieved without the need of a feedback network, which would propagate the errors, by applying adaptive interaction. Thereby, the ANN controller can be adapted directly without wasting calculation time in order to increase the frequency response of the controller. The velocity control of a brushless DC motor (BLDCM) under slowly and rapidly changing load conditions is simulated to demonstrate the effectiveness of the algorithm. The AI tuning algorithm was used to tune up the PID gains, and the simulation results with PID adaptation process are presented by comparing the obtained results with the adaptive PID controller based on BPNN and a conventional PID controller. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于CPS统计信息自学习机理的AGC自适应控制

提出一种利用控制性能标准(control performance standard,CPS)的统计信息进行自动学习,从而为调度端自动发电控制(automatic generation control,AGC)的比例积分控制参数进行自动调整的自适应控制策略。CPS标准的提出一方面是为提高区域电网对整个电网频率的支援作用,另一方面其统计特性放宽对区域控制偏差控制的要求。该自适应控制策略充分利用CPS的统计特性,对电网结构、参数和运行方式具有良好的适应性,其自学习机制简单实用,易于在现有大多数电网调度AGC系统上实现。以广东电网为对象的实例研究显示,该AGC自适应控制可在保证CPS考核合格率的同时有效减轻发电厂的调节压力。     

人工智能技术在电力系统无功电压控制中的应用

作为电力系统自动化的一个重要组成部分，电力系统无功电压控制具有电力系统控制所固有的复杂性、非线性、不精确性及实时性强等特性，其中有些方面难以用传统的数字模型和常规的控制方法来描述和实现。人工智能技术由于具有传统方法所不具备的智能特性，因而在无功电压控制中得到了广泛的关注。并取得了大量的研究成果。本文综述了近年来专家系统（ES）、人工神经网络（ANN）、模糊理论（FT）、遗传算法（GA）以及多Agent系统（MAS等人工智能技术在电力系统无功电压控制中应用的主要成果和方法，分析比较了它们各自的应用特点，并提出了一些需要解决的问题和值得进一步研究的方向。     

Multi‐model unfalsified adaptive switching control: Test functionals for stability and performance

The paper studies how to infer behavioral features of a potential loop, consisting of an uncertain plant interconnected in feedback with a candidate controller, on the grounds of experimental data taken from the same plant possibly driven by a different controller. In such a context, convenient tools to work with are test functionals, computed via a virtual experiment, which quantify the discrepancy between the potential loop and the so‐called ‘tuned‐loop’ or ‘reference‐loop’ related to the same candidate controller. Several test functionals are considered and analyzed so as to unveil conditions under which their adoption can accomplish the desired goals. These results are shown to be of practical relevance for on‐line performance inference of feedback control systems and implementation of highly performing adaptive switching control systems. Copyright © 2011 John Wiley & Sons, Ltd.     

Accommodation of unknown actuator faults using output feedback‐based adaptive robust control

In this paper, we solve the problem of output tracking for linear uncertain systems in the presence of unknown actuator failures using discontinuous projection‐based output feedback adaptive robust control (ARC). The faulty actuators are characterized as unknown inputs stuck at unknown values experiencing bounded disturbance and actuators losing effectiveness at unknown instants of time. Many existing techniques to solve this problem use model reference adaptive control (MRAC), which may not be well suited for handling various disturbances and modeling errors inherent to any realistic system model. Robust control‐based fault‐tolerant schemes have guaranteed transient performance and are capable of dealing with modeling errors to certain degrees. But, the steady‐state tracking accuracy of robust controllers, e.g. sliding mode controller, is limited. In comparison, the backstepping‐based output feedback adaptive robust fault‐tolerant control (ARFTC) strategy presented here can effectively deal with such uncertainties and overcome the drawbacks of individual adaptive and robust controls. Comparative simulation studies are performed on a linearized Boeing 747 model, which shows the effectiveness of the proposed scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive‐optimal control under time‐varying stochastic uncertainty using past learning

An adaptive‐optimal control architecture is presented for adaptive control of constrained aerospace systems with matched uncertainties that are subject to dynamic stochastic change. The architecture brings together three key elements, ie, model predictive control–based reference command shaping, Gaussian process (GP)–based Bayesian nonparametric model reference adaptive control (MRAC), and online GP clustering over nonstationary GPs. Model predictive control optimizes reference model and its shaped output is passed into GP–based MRAC, which is used to learn the model in presence of significant time‐varying stochastic uncertainty while maintaining stability. Based on a likelihood ratio test, the changepoints are detected and learned. Lastly, the models are created and clustered by non‐Bayesian clustering algorithm. The key salient feature of our architecture is that not only can it detect changes but also it uses online GP clustering to enable the controller to utilize past learning of similar models to significantly reduce learning transients. Furthermore, persistence of excitation conditions are significantly relaxed due to the use of GP‐MRAC. Stability of the architecture is argued theoretically and performance is validated empirically on different scenarios for wing rock dynamics.     

燃煤电厂协调控制系统故障诊断与容错控制应用研究

基于多模型自适应滤波技术和线性二次型最优控制提出了电厂锅炉汽机协调控制系统的故障诊断和容错控制方案。建立了包含协调控制系统正常和可能发生的各种故障模式的系统模型集，并根据系统模型集以及系统实时输入输出数据进行多模型自适应滤波，从而实现故障诊断；然后根据故障诊断结果以及针对每个模型设计的线性二次型最优基本控制器，实现控制律的在线重组和主动容错控制。最后将上述故障诊断和容错控制方法应用于某300MW燃煤电厂协调控制系统中，仿真结果表明算法的有效性。     

Output feedback adaptive fault-tolerant compensation tracking control for linear systems based on the closed-loop reference model

This paper investigates the problem of output feedback adaptive compensation tracking control for linear systems subject to external disturbances and actuator failures including loss of effectiveness faults and bias faults. The impact of actuator faults on the transient performance of systems can be mitigated predicated on the closed-loop reference model with an additional degrees of design freedom. Using the estimation information provided by the adaptive mechanism, an output feedback adaptive fault-tolerant control strategy is developed to track closed-loop reference model systems. It is shown that all the signals of the resulting closed-loop system are bounded. Finally, simulation results are given to demonstrate the effectiveness of the proposed fault-tolerant tracking control method.     

Current state of neural networks applications in power system monitoring and control

For over two decades Neural Network (NN) has been applied to power system monitoring and control. Conventional controllers suffer from certain limitations which NN as an Artificial Intelligence (AI) technique is able to overcome. Therefore, many researchers prefer to use NN technique in the monitoring and control of power systems. This paper reviews published recently schemes for control and monitoring based on NN. The performance of various NN controllers is compared with one another as well as to the performance of other types of controllers. This review further reveals that the design of a proper NN control can maintain first-swing stability, damp oscillation, ensure voltage stability and the reliable supply of electric power.     

柔性交流输电系统稳定控制综述

从柔性交流输电系统元件的数学模型、装设地点及控制策略三方面评述了稳定控制的研究工作和现状。文中从作用机理出发介绍了柔性交流系统元件主回路的代数模型;着重分析了目前研究柔性交流输电系统元件最佳装设地点的各种方法,提出了解决这一问题的两种新的方向,即二阶典型分析法和以EEAC理论为基础的参数灵敏度分析法;同时阐述了非线性控制和智能控制在柔性交流输电系统稳定控制策略中的应用情况,指出了柔性交流输电系统元件综合智能控制器设计的关键在于电力系统稳定分析的定量分析技术。     

现代控制理论在有源电力滤波器中的应用

随着电力电子技术和控制技术的飞速度发展,现代控制理论在有源电力滤波器中得到了广泛的应用。对现有的研究成果进行了综述,以便充分了解研究现状和发展趋势。概括了有源电力滤波器的基本工作原理、技术特点,详细论述了自适应控制、滑动模控制、反馈线性化解耦控制、无源性控制和智能控制在有源电力滤波器中的应用,给出了各种控制算法的特点及现有研究的不足。有源电力滤波器现有控制算法研究的缺点以及系统中不确定性较多的特点,需要应用鲁棒性较强,对模型依赖性不强的控制算法。如滑模控制、鲁棒控制、无源性控制和智能控制等,才能达到更好的控制效果。     

Application of a system with distributed architecture for information acquisition and processing in tasks of active–adaptive voltage control in distribution electric grids

An active–adaptive control system for power grids with distributed architecture of data acquisition and processing is considered. The advantages of the proposed control principle are compared with commonly used methods. A domestic apparatus—a programmable recording bay controller (PRBC)—is described. This hardware was designed to measure the basic electrical parameters and create distributed systems for data acquisition and processing. The procedure is considered of supervisory control and data acquisition (SCADA) by the SONATA system based on a multicore distributed architecture having high reliability and supporting a rigid real-time mode. A full-function full-scale model of active–adaptive voltage control system (AAVC) was set up using proposed software and hardware. The AAVC makes it possible to use the results of calculating–measuring procedures to select a corresponding on-load tap-changer (OLTC) on the actual voltage levels in the nodes of distribution grid. This approach to centralized voltage control in distribution grids makes it possible to ensure the required level of voltage in the greatest possible number of power consumers owing to prediction of voltage change. The effect of emergencies (random failure of control equipment) during the operation of an active–adaptive voltage control system is analyzed. A qualitative evaluation of the effect of faults on regulation quality and choice of OLTC connection is carried out. It is shown that, for undisturbed operation of an active–adaptive voltage control system, additional diagnostic tools, backup, and data loss compensation are needed.