Neuro-fuzzy generalized predictive control of boiler steam temperature

Predictive tracking for augmented reality. Unpublished doctoral dissertation

Sch?n, Donald A., Bish Sanyal, and William J. Mitchell, editors, High Technology and Low-Income Communities: Prospects for the Positive Use of Advanced Information Technology. Cambridge MA: MIT Press (1998). High Technology and Low-Income Communities: Prospects for the Positive Use of Advanced Information Technology Not for circulation or quotation. 7 Information Technologies That Change Relationships between Low-Income Communities and the Public and NonProfit Agencies That Serve Them Joseph Ferreira, Jr. What are the prospective benefits for service providers and service recipients of decentralized access to information about populations and their needs, service systems, and operations? Will growing access to such information be, on the whole, enfranchising for community members, or will it subject them to increased centralized control? This chapter examines particular ways in which information technologies (IT) can make land-use planning (and other aspects of metropolitan evolution) more transparent and understandable to individuals and communities. The point is not that such a use of IT is possible; rather, it is to better understand how it might empower or disenfranchise low-income communities, promote efficiency through improved self-governance, or further centralize authority in the hands of government and other large-scale data providers. I begin by focusing on a simple, seemingly straightforward example of the use of IT: to computerize inquiries about land use and ownership of land and property in the city. This "simple" example of decentralized data access becomes complicated, however, as soon as the issues of maintenance and updating are addressed. Moreover, various IT strategies for addressing these issues have significantly different impacts on whether or not data access promotes effective decentralization and citizen empowerment. A careful examination of some of the issues and options involved in simple example improves our ability to draw inferences about how access to information can and should foster improved metropolitan governance and broader public participation in urban and regional planning. The real potential for capitalizing on IT to improve governance is not simply a matter of automating government services, nor is it a question of whether or not to introduce IT. Shaping planning processes, to capitalize on IT, are crucial in improving local governance through reduced bureaucracy and devolution of authority. My reasoning is consistent with recent observations in the management literature by Shoshana Zuboff, Tom Peters, and others about IT-driven restructuring of work in U.S. corporations (Peters 1992; Zuboff 1988).

Building Predictive Models in R Using the caret package

Journal of Statistical Software November2008,Volume28,Issue5.https://www.sodocs.net/doc/2b2026683.html,/ Building Predictive Models in R Using the caret Package Max Kuhn P?zer Global R&D Abstract The caret package,short for classi?cation and regression training,contains numerous tools for developing predictive models using the rich set of models available in R.The package focuses on simplifying model training and tuning across a wide variety of modeling techniques.It also includes methods for pre-processing training data,calculating variable importance,and model visualizations.An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the bene?ts of parallel processing with several types of models. Keywords:model building,tuning parameters,parallel processing,R,NetWorkSpaces. 1.Introduction The use of complex classi?cation and regression models is becoming more and more com-monplace in science,?nance and a myriad of other domains(Ayres2007).The R language (R Development Core Team2008)has a rich set of modeling functions for both classi?cation and regression,so many in fact,that it is becoming increasingly more di?cult to keep track of the syntactical nuances of each function.The caret package,short for classi?cation and regression training,was built with several goals in mind: to eliminate syntactical di?erences between many of the functions for building and predicting models, to develop a set of semi-automated,reasonable approaches for optimizing the values of the tuning parameters for many of these models and create a package that can easily be extended to parallel processing systems.

Model Predictive Control Toolbox——设计和仿真模型预测控制器

——设计和仿真模型预测控制器 Model Predictive Control Toolbox提供了MATLAB函数、图形用户界面和Simulink模块用于设计和仿真模型预测控制器。模型预估计控制器（Model predictive controller）可以帮助工程师优化服从输入输出约束的多输入、多输出控制系统的性能。 为了预测输入量变化对输出的影响，工具箱使用一个内置的对象模型求解控制行为。工程师可以使用System Identification Toolbox从实验数据估算模型，从线性化的Simulink模型获取模型，或是直接指定一个线性时不变对象，比如传递函数或状态空间形式的。对象模型可以包含延迟环节。 使用Model Predictive Control Toolbox提供的 两个模块之一直接在Simulink中设计和仿真控制器 特点 使用图形用户界面和MATLAB命令进行模型预估计控制器的设计和仿真

?能够从实验数据或线性的Simulink模型定义一个内置的线性对象模型 ?在Simulink中直接提供Simulink模块用于设计和仿真模型预测控制器 ?利用无扰动控制切换使用多个模型预测控制器控制非线性对象 ?能够处理时变约束和权重、非对角权重及自定义不可测量的干扰模型 ?通过RTW能够生成C代码的应用发布 强大功能 使用Model Predictive Control Toolbox Model Predictive Control Toolbox 使用图形用户界面来组织管理工程师开发的控制器，并把它加入到工程项目中，使工程师可以对项目进行 管理并尝试多种控制器。 使用Control and Estimation Tools Manager 简化了这些工作，导入模型和以 前设计的控制器，定义被控对象的输入输出， 它们的单位及其名义值。该管理器能在界面 中显示控制器的结构，标示设置点的个数、 操作变量、干扰及可测量和不可测量的输出。 使用Control and Estimation Tools Manager 工程师可以： ?定义计算后续控制行为中所用的内置对象模型 ?设计模型预估计控制器 ?仿真线性模型控制器的闭环行为 定义内置对象模型 模型预测控制器的控制行为建立在其内置的过程对象模型之上。这个内置的模型让控制器得以预见将要发生的过程行为并遵从输出约束。具有自我更新能力的内置模型使得模型预估计控制比庞杂的耦合PID

model predictive control toolbox

Using one of the two blocks available in Model Predictive Control Toolbox to design and simulate a controller directly in Simulink. Working with Model Predictive Control Toolbox Model Predictive Control Toolbox uses the Control and Estimation Tools Manager, a GUI that organizes your controller development into projects, enabling you to manage the design and evaluation of multiple controllers. The Control and Estimation Tools Manager simplifies the tasks of importing plant models and previously designed controllers and defining plant inputs and outputs, their units, and their nominal values. It shows your controller structure in one view by indicating the number of set points, manipulated variables, disturbances, and measured and unmeasured outputs. With the Control and Estimation Tools Manager, you can: ?Define internal plant models used in calculating future control actions ?Design a model predictive controller ?Simulate the closed-loop behavior of the controller with linear models Getting Started with Model Predictive Control Toolbox9:59

THE PREDICTIVE INDEX

THE PREDICTIVE INDEX? 预示指数 机构组织调查 人事估计 姓名性别日期 说明：阅读下表所列的词语，你认为哪些词组描述了别人对你的行为表现的期望，请在这些词语后面打个√号。 乐于助人···········受人尊重···········镇静··············· 轻松自在···········担忧···············受欢迎············· 兴奋···············重感情·············有礼貌············· 独断···············喜欢冒险···········充满活力··········· 有耐心·············脾气随和···········性格开朗··········· 诚心诚意···········谦恭···············逃避者············· 世故···············善于交际···········慷慨··············· 坚持不懈···········会处世·············不引人注目········· 诚恳···············随和···············大胆··············· 优秀···············温暖···············能容忍············· 富同情心···········苛求···············和蔼··············· 忠诚···············有慈善心···········强迫他人··········· 自觉···············说服力强···········刚毅··············· 遵循习俗···········谨慎···············安稳··············· 口才好·············知足···············有修养············· 喜嘲讽·············善解人意···········喜支配人··········· 被动···············充满生气···········尊重他人··········· 温文尔雅···········志趣相投···········淡漠··············· 勇敢···············顺从···············易适应环境········· 讨人喜欢···········高兴···············有吸引力··········· 体贴···············固执···············可信赖············· 自信···············令人信服···········热切··············· 稳定···············反映迅速···········怕羞··············· 富竞争性···········亲切···············小题大做··········· 时髦···············自私···············多才多艺··········· 整洁···············缄默···············和蔼可亲··········· 有冒险精神·········严肃···············有外交手腕········· 文雅···············不屈不挠···········唯我··············· 令人害怕···········始终如一··········· 请翻到下一页继续 版权号：PRAENDEX INCORPORATED 拥有1992，1994，1999年版权。 所有版权已注册。未经版权所有者的书面认可，表格的全部内容不可以任何方式复制，也不可以转让或翻译成另外一种机器语言。

Predictive Control in Power Electronics and Drives

Predictive Control in Power Electronics and Drives Patricio Cortés,Member,IEEE,Marian P.Kazmierkowski,Fellow,IEEE,Ralph M.Kennel,Senior Member,IEEE, Daniel E.Quevedo,Member,IEEE,and JoséRodríguez,Senior Member,IEEE Abstract—Predictive control is a very wide class of controllers that have found rather recent application in the control of power converters.Research on this topic has been increased in the last years due to the possibilities of today’s microprocessors used for the control.This paper presents the application of different pre-dictive control methods to power electronics and drives.A simple classi?cation of the most important types of predictive control is introduced,and each one of them is explained including some application examples.Predictive control presents several advan-tages that make it suitable for the control of power converters and drives.The different control schemes and applications presented in this paper illustrate the effectiveness and?exibility of predictive control. Index Terms—Drives,power electronics,predictive control. I.I NTRODUCTION T HE USE of power converters has become very popular in the recent decades for a wide range of applications,in-cluding drives,energy conversion,traction,and distributed gen-eration.The control of power converters has been extensively studied,and new control schemes are presented every year. Several control schemes have been proposed for the control of power converters and drives.Some of them are shown in Fig.1.From these,hysteresis and linear controls with pulse-width modulation(PWM)are the most established in the lit-erature[1]–[3].However,with the development of faster and more powerful microprocessors,the implementation of new and more complex control schemes is possible.Some of these new control schemes for power converters include fuzzy logic, sliding mode control,and predictive control.Fuzzy logic is suitable for applications where the controlled system or some of its parameters are unknown.Sliding mode presents robustness and takes into account the switching nature of the power converters.Other control schemes found in the literature in-clude neural networks,neuro–fuzzy,and other advanced control techniques. Manuscript received August20,2008;revised September18,2008.First published October31,2008;current version published December2,2008. This work was supported in part by the Chilean National Fund of Scienti?c and Technological Development under Grant1080443and in part by the Universidad Técnica Federico Santa María. P.Cortés and J.Rodríguez are with the Electronics Engineering Department, Universidad Técnica Federico Santa María,Valparaíso110-V,Chile(e-mail: patricio.cortes@usm.cl). M.P.Kazmierkowski is with the Institute of Control and Industrial Electro-nics,Warsaw University of Technology,00-661Warsaw,Poland. R.M.Kennel is with the Technical University of Munich,80333Munich, Germany. D.E.Quevedo is with the School of Electrical Engineering and Computer Science,The University of Newcastle,Callaghan,NSW2308,Australia. Color versions of one or more of the?gures in this paper are available online at https://www.sodocs.net/doc/2b2026683.html,. Digital Object Identi?er 10.1109/TIE.2008.2007480 Fig.1.Basic methods of converter control. Predictive control presents several advantages that make it suitable for the control of power converters:Concepts are intuitive and easy to understand,it can be applied to a variety of systems,constraints and nonlinearities can be easily in- cluded,multivariable case can be considered,and the resulting controller is easy to implement.It requires a high amount of calculations,compared to a classic control scheme;however, the fast microprocessors available today make possible the implementation of predictive control.Generally,the quality of the controller depends on the quality of the model. This paper presents a survey of the most important types of predictive control applied in power electronics and drives. A classi?cation of them is proposed in Section II,and each type of predictive control is explained in the following sec- tions,including some application examples.Hysteresis-based predictive control is presented in Section III,trajectory-based predictive control in Section IV,deadbeat control in Section V, model predictive control(MPC)in Section VI,MPC with?nite control set in Section VII,and?nally,some conclusions are presented. II.P REDICTIVE C ONTROL M ETHODS Predictive control is a very wide class of controllers that have found rather recent application in power converters.The classi?cation proposed in this paper for different predictive control methods is shown in Fig.2. The main characteristic of predictive control is the use of the model of the system for the prediction of the future behavior of the controlled variables.This information is used by the controller in order to obtain the optimal actuation,according to a prede?ned optimization criterion. The optimization criterion in the hysteresis-based predictive control is to keep the controlled variable within the boundaries of a hysteresis area,while in the trajectory based,the variables are forced to follow a prede?ned trajectory.In deadbeat control, the optimal actuation is the one that makes the error equal to zero in the next sampling instant.A more?exible criterion is used in MPC,expressed as a cost function to be minimized. The difference between these groups of controllers is that deadbeat control and MPC with continuous control set need a modulator,in order to generate the required voltage.This will result in having a?xed switching frequency.The other 0278-0046/$25.00?2008IEEE

automotive model predictive control

Chapter3 Mean Value Engine Models Applied to Control System Design and Validation Pierre Olivier Calendini and Stefan Breuer Abstract.The importance of simulation in power train and combustion engine de-velopment is undisputed today,but the search for the most ef?cient use of simulation in the development cycle is still ongoing.In parallel available computing power and the number of tools are increasing.The choice of the right tool has signi?cant im-pact on the development process.In this chapter some insight will be given into a process relying on the mean value model approach for control strategy development and validation regarding the development of a1.6-litres4-cylindre direct injection diesel engine respecting Euro5legislation.To reach the performance and emission targets in a cost effective way,simulation was used early in the project to study dif-ferent concepts of air loop control architecture and then was employed for the con-trol function development of the air loop control.The present study addresses the main components of the mean value model,the air loop,the degree of re?nement in regards to combustion and the actuator dynamics.Two variants of a new control concept have been studied with considerable re?nement.To position the predicted performance they were compared to the conventional Euro4approach.To obtain signi?cant simulation results the work was concentrated on speci?c engine life sit-uations.The comparatively fast execution of mean value models has been exploited to realize statistical computations on the robustness of the controlled system.The main targets of control development are a high degree of precision but also a ro-bust behavior in real life.To asses the performance of the control approaches under study,the main sources of disturbances du to series production were identi?ed and than this knowledge was used to build a mean engine model that allowed to repre-sent their impact on the engine.This allowed distinguishing the control approaches not only on their capacity to perform well on a nominal engine but their ability to cope with the large spectrum of engine behavior to be expected in series production. The obtained results allowed for an intelligent choice of the control strategy,choice which has been con?rmed by experimental work. Pierre Olivier Calendini and Stefan Breuer PSA Peugeot Citro¨e n18,rue des Fauvelles92252La Garenne-Colombes e-mail:pierre-olivier.calendini@https://www.sodocs.net/doc/2b2026683.html,,stefan.breuer@https://www.sodocs.net/doc/2b2026683.html, L.del Re et al.(Eds.):Automotive Model Predictive Control,LNCIS402,pp.37–52. https://www.sodocs.net/doc/2b2026683.html, c Springer-Verlag Berlin Heidelberg2010

【9】A Simplified Finite-Control-Set Model-Predictive Control for Power Converters

A Simpli?ed Finite-Control-Set Model-Predictive Control for Power Converters Changliang Xia,Senior Member,IEEE,Tao Liu,Tingna Shi,and Zhanfeng Song Abstract—Finite-control-set model-predictive control(FCS-MPC)requires a large amount of calculation,which is an obstacle for its application.However,compared with the classical linear control algorithm,FCS-MPC requires a shorter control loop cycle time to reach the same control performance.To resolve this contradiction,this paper presents an effective method to simplify the conventional FCS-MPC.With equivalent transformation and specialized sector distribution method,the computation load of FCS-MPC is greatly reduced while the control performance is not affected.The proposed method can be used in various circuit topologies and cases with multiple constraints.Experiments on two-level converter and three-level NPC converter verify the good performance and application value of the proposed method. Index Terms—Finite-control-set model-predictive control (FCS-MPC),power converter,sector distribution method,simpli-?ed algorithm. I.I NTRODUCTION O PTIMAL control of multiple objectives not only realizes the high-quality and high-ef?ciency power conversion but also ensures the high reliability of the power converters.For example,in the transformerless photovoltaic(PV)applications, the vibration suppression of the common-mode(CM)voltage in power converters helps to reduce the current harmonics and power losses and to improve the system safety[1]–[4].For the medium-voltage power conversion system,the multilevel power-converter technique is proposed,and the balancing of the neutral point voltage is considered in order to obtain better control performance and enhance the reliability of the entire system[5],[6]. The?nite-control-set model-predictive control(FCS-MPC) can realize the optimal control of multiple objectives,and it has been attracting growing interest due to many of its advantages, such as fast dynamic response,inherent decoupling,and easy Manuscript received March02,2013;revised July05,2013and August19, 2013;accepted September27,2013.Date of publication October04,2013; date of current version May02,2014.This work was supported in part by the National Key Basic Research Program of China(973project)under Grant 2013CB035602,by the National Natural Science Foundation of China under Grant51107084,and by the Key Technologies Research and Development Pro-gram of Tianjin under Grant13ZCZDGX01100.Paper no.TII-13-0125. C.Xia is with the School of Electrical Engineering and Automation,Tianjin University,Tianjin300072,China,and also with Tianjin Key Laboratory of Ad-vanced Technology of Electrical Engineering and Energy,Tianjin Polytechnic University,Tianjin300387,China(e-mail:motor@https://www.sodocs.net/doc/2b2026683.html,). T.Liu,T.Shi,and Z.Song are with the School of Electrical Engineering and Automation,Tianjin University,Tianjin300072,China(e-mail:taoliu@tju. https://www.sodocs.net/doc/2b2026683.html,;tnshi@https://www.sodocs.net/doc/2b2026683.html,;zfsong@https://www.sodocs.net/doc/2b2026683.html,). Color versions of one or more of the?gures in this paper are available online at https://www.sodocs.net/doc/2b2026683.html,. Digital Object Identi?er10.1109/TII.2013.2284558inclusion of nonlinear constraints[7]–[20].FCS-MPC has de-veloped rapidly in the past few years,and studies on this topic have been spread out across various?elds,for example,renew-able energy,uninterruptible power supply(UPS)systems,and electric motor drivers[21]–[27].However,there exists an ob-vious challenge for the actual application of FCS-MPC. As is known,FCS-MPC undergoes no pulse-width modula-tion(PWM)process,and it outputs only one switching state in each control loop cycle,so its switching frequency is un-?xed.To achieve the same current waveform quality,the con-trol loop cycle time of FCS-MPC should be much shorter than that of the conventional linear control methods[28],[29].As a result,the time allowed to complete the FCS-MPC algorithm becomes so short that it is impractical in reality.For example, in the case of a three-level neutral point clamped(NPC)con-verter,the minimum time needed for completing the FCS-MPC is approximately52s[30];this is still longer than the control loop cycle time required for achieving satisfying control perfor-mances.Moreover,the amount of calculation will also increase with the complexity of circuit topology.The additional multiple constraints,such as balancing of the neutral point voltage and vibration reduction of the common-mode voltage,will also in-crease the calculation amount of FCS-MPC.In summary,time-consuming computation is an obstacle that limits the application of FCS-MPC. In recent years,simpli?cation of the FCS-MPC algorithm has been proposed and discussed[31]–[35].For example,one approach uses sector distribution on a source voltage vector to reduce the number of candidate vectors in the prediction process[31].With this approach,the program running time can be signi?cantly shortened but the control performance may be affected as the proposed simpli?ed algorithm is not an exact equivalent to the original algorithm.Another approach,men-tioned in hybrid hysteresis-SVM algorithms,shed lights also on reducing the computational time of FCS-MPC.However, there is a lack of extensive studies on this issue. In this paper,an effective method for FCS-MPC algorithm simpli?cation is proposed to reduce the running time without affecting the control performance.This method is divided into two steps:the?rst step is to eliminate the calculation for cur-rent prediction;the second step is to reduce the number of cost function calculations.With the above two steps,the calculation time is shortened while the control performance is not affected. The proposed method has some versatility,and it is effective not only in different circuit topologies but also in cases with mul-tiple constraints.Finally,the experiments on a two-level con-verter and a three-level NPC converter prove the effectiveness of the proposed method. 1551-3203?2014IEEE.Personal use is permitted,but republication/redistribution requires IEEE permission. See https://www.sodocs.net/doc/2b2026683.html,/publications_standards/publications/rights/index.html for more information.