检索结果-内蒙古大学图书馆

American control Conference (ACC)

作者： R. Isermann Institute of Automatic Control Laboratory for Control Engineering and Process Automation Darmstadt University of Technology Darmstadt Germany

The synergetic integration of mechanical processes, micro-electronics and information processing opens new possibilities as well to the design of processes as for its automatic control. The solution of tasks within mechatronic systems is performed on the process side and the digital-electronic side. As the interrelations during the design play an import role simultaneous engineering from the very beginning has to take place. Mechatronic systems are developed for mechanical elements, machines, vehicles and precision mechanic devices. The integration of mechatronic systems can be performed by the components (hardware-integration) and by information processing (software-integration). The information processing consists of low-level and high-level feedback control, supervision and diagnosis and general process management. Special signal processing, model based and adaptive methods are applied. With the aid of a knowledge base and inference mechanisms, mechatronic systems with increasing intelligence will be developed. The main goals are to increase systems performance, reliability and economy, and production costs. Some examples for the development of mechatronic systems are given, such as smart actuators, adaptive suspensions, electrical brakes, adaptive cruising control of cars, and hardware-in-the-loop simulation of combustion engines. Experimental results are shown, and the improvements by the mechatronic approaches are pointed out.

关键词： Mechatronics control engineering Information processing Intelligent actuators Programmable control Adaptive control process design automatic control Automotive engineering Design engineering

来源：评论

学校读者我要写书评

暂无评论

Integrated control, diagnosis and reconfiguration of a heat exchanger

Integrated control, diagnosis and reconfiguration of a heat ...

引用

American control Conference (ACC)

作者： P. Balle M. Fischer D. Fussel R. Isermann Institute of Automatic Control Laboratory of Control Engineering and Process Automation Darmstadt University of Technology Darmstadt Germany

Heat exchangers play an important role in chemical and process industries. In order to improve reliability and control performance, intelligent concepts for control, supervision and reconfiguration are necessary. In this paper, an approach is presented which integrates model-based adaptive control and reconfiguration based on fault detection/diagnosis applied to a heat exchanger plant. The adaptive controller and the fault detection scheme are based on a fuzzy model of the process (Takagi-Sugeno type) and the fault diagnosis is performed using a self-organizing fuzzy structure.

关键词： Centralized control Chemical industry Adaptive control Fault detection Fault diagnosis Fuzzy control Chemical processes Intelligent control Programmable control Takagi-Sugeno model

来源：评论

学校读者我要写书评

暂无评论

Identification of Fuzzy Models for Predictive control of Nonlinear Time-Variant processes

引用

IFAC Proceedings Volumes 1997年第25期30卷 403-408页

作者： Oliver Nelles Martin Fischer Rolf Isermann Darmstadt University of Technology institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str 4 D-64283 Darmstadt Germany

来源：评论

学校读者我要写书评

暂无评论

Identification of nonlinear processes and model based fault isolation using local linear models

Identification of nonlinear processes and model based fault ...

引用

American control Conference (ACC)

作者： P. Balle D. Juricic A. Rakar S. Ernst Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University of Darmstadt Darmstadt Germany Department of Computer Automation and Control Jozef Stefan Institute Ljubljana Slovenia

Deals with identification of nonlinear processes and model-based fault detection/isolation (FDI). The applicability of the proposed methods is illustrated on a three-tank laboratory setup. The process identification is based on the local linear model tree (LOLIMOT) algorithm and leads to local linear models. The parameters of the local models are used for generation of structured residual equations, similar to the well-known parity space approach. This enables detection and isolation of five different sensor faults of the three-tank process, continously over all ranges of operation.

关键词： Fault diagnosis Fault detection automatic control Power system modeling Valves Laboratories DC motors automation Neural networks Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Nonlinear predictive control based on the extraction of step-response models from Takagi-Sugeno fuzzy systems

Nonlinear predictive control based on the extraction of step...

引用

American control Conference (ACC)

作者： M. Fischer M. Schmidt K.K. Kavsek-Biasizzo Institute of Automatic Control Laboratory of Control Systems and Process Automation Technical University of Darmstadt Darmstadt Germany Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia

This paper deals with nonlinear predictive control based on higher order Takagi-Sugeno fuzzy systems which can also be interpreted as generalized radial basis function networks. We investigate how the fuzzy models can be linked to a special type of model based predictive control algorithm, namely the dynamic matrix control (DMC). Previously, purely linear step response models were used for long-range prediction. Here, the method is extended to nonlinear processes. Therefore, various step responses for different operating points are extracted from the fuzzy model. For performance evaluation, a heat exchanger is identified by means of the local linear model tree algorithm and controlled by the modified DMC.

关键词： Predictive control Predictive models Takagi-Sugeno model Fuzzy systems automatic control Laboratories Radial basis function networks Fuzzy control control system synthesis Nonlinear control systems

来源：评论

学校读者我要写书评

暂无评论

Detection and Isolation of Sensor Faults for a Heat Exchanger Based on Local Linear Fuzzy Models

引用

IFAC Proceedings Volumes 1997年第25期30卷 525-530页

作者： Peter Ballé Oliver Nelles Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University of Darmstadt Landgraf-Georg-Str. 4 64283 Darmstadt Germany Phone: +49/6151/16-2314 Fax: +49/6151/293445

The development of a reliable fault detection and isolation (FDI) scheme for nonlinear processes is often laborious and difficult to achieve due to the complexity of the system. Neural networks and fuzzy models, able to approximate nonlinear dynamic functions offer a powerful tool to cope with this problem. In this paper, a multi-model approach for FDI of sensor faults on a highly nonlinear real world processes is introduced. The approach is based local linear fuzzy models and provides residuals which are structured in a way that detection of six and isolation of five different sensor faults over all ranges of operation becomes possible. The approach enables on-line supervision of the process. Furthermore, a sensitivity analysis of the residuals to different faults can be made, based on the parameters of the fuzzy models.

关键词： fault detection/isolation fuzzy models parallel/series-parallel model heat exchanger

来源：评论

学校读者我要写书评

暂无评论

Identification with Dynamic Neural Networks - Architectures, Comparisons, Applications

引用

IFAC Proceedings Volumes 1997年第11期30卷 947-972页

作者： Rolf Isermann Susanne Ernst Oliver Nelles Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +49/6151/16-4524 Fax: +49/6151/293445

An overview of presently known neural networks for the identification of nonlinear dynamic systems is given. The suitable networks are classified where two different approaches, the external and internal dynamic networks, have to be distinguished. One alternative is to apply standard static neural networks and incorporate the dynamics by providing the network with information about previous inputs and outputs created by external linear filters. Another possibility is to include dynamic elements within the neural network structure and therefore making the network itself a nonlinear dynamic system. The principles, advantages and drawbacks of both approaches are pointed out. Two special neural network architectures, one with external and one with internal dynamics, are considered in more detail. Their capability to identify complex nonlinear dynamic real-world processes is studied. Experimental results for two multi-variable processes are shown, a combustion engine turbocharger and an industrial scale tubular heat exchanger.

关键词： dynamic neural networks nonlinear systems dynamic systems identification

来源：评论

学校读者我要写书评

暂无评论

Adaptive Predictive control Based on Local Linear Fuzzy Models

引用

IFAC Proceedings Volumes 1997年第11期30卷 819-824页

作者： Martin Fischer Oliver Nelles Rolf Isermann Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +49/6151/16-5109 Fax: +49/6151/293445

In this paper, local linear fuzzy models are used for adaptive predictive control of nonlinear processes. First, an algorithm (LOLIMOT = local linear model tree algorithm) for off-line identification of Takagi-Sugeno type fuzzy models is briefly reviewed. In order to cope with time-variant nonlinear processes and also with the influence of non-measurable disturbances, an on-line adaptation of the model is performed. A recursive least-squares algorithm (RLS) is applied to update the linear parameters in the rule consequents locally. The local estimation results in low computational effort and avoids forgetting in non-active operating regimes. The effectiveness of the approach is demonstrated by simulations and by controlling the output temperature of a cross-flow heat exchanger.

关键词： nonlinear control predictive control adaptation fuzzy models heat exchangers

来源：评论

学校读者我要写书评

暂无评论

Orthonormal Basis Functions for Nonlinear System Identification with Local Linear Model Trees (LOLIMOT)

引用

IFAC Proceedings Volumes 1997年第11期30卷 639-644页

作者： Oliver Nelles Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +49/6151/16-4524 Fax: +49/6151/293445

A new approach for identification of nonlinear dynamic systems is proposed. It is based on a combination of generalized orthonormal basis functions and local linear model trees (LOLIMOT). The main idea is to approximate an unknown function from data by the interpolation of many local linear models. The number of local linear models and their validity regions are determined by a tree construction algorithm that partitions the input space by axisorthogonal cuts. The parameters of the local linear models are estimated by minimizing a equation error criterion (ARX models). These local parametric models are replaced by linear parameterized output error models based on generalized orthonormal basis functions. Thus, stability of the nonlinear dynamic model can be proven and the low bias property of output error models can be exploited. Furthermore, a subset selection technique may be applied for choosing the complexity of each local linear model. Since different basis functions may be constructed for each local linear model, processes with operating condition dependent dynamic behavior can be modeled efficiently.

关键词： orthonormal basis functions nonlinear system identification local linear models

来源：评论

学校读者我要写书评

暂无评论

Knowledge-Based Structures for Fault Diagnosis and Its Applications

引用

IFAC Proceedings Volumes 1997年第27期30卷 1-18页

作者： Rolf Isermann Institute of Automatic Control Laboratory of Control Engineering and Process Automation Darmstadt University of Technology Landgraf-Georg Str. 4 D-64283 Darmstadt Germany Phone: +49 (6151) 16-7412 FAX: +49 (6151)293604

The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper describes structures for advanced methods of fault detection and diagnosis. It begins with a consideration of a knowledge-based procedure which is based on analytical and heuristic information. Then different methods of fault detection are considered, which extract features from measured signals and use process and signal models. These methods are based on parameter estimation, state estimation and parity equations. By comparison with the normal behaviour, analytic symptoms are generated. Human operators may be a further source of information, and support the generation of heuristic symptoms. For fault diagnosis, all symptoms have to be processed in order to determine possible faults. This can be performed by classification methods or approximate reasoning, using probabilistic or possibilistic (fuzzy) approaches based on if-then-rules. The application of these methods is shown for the fault detection and diagnosis of Dieselengines.

关键词： Fault detection supervision monitoring parameter estimation parity equations technical processes actuators sensors fault diagnosis diagnostic reasoning fuzzy reasoning wheel suspension electrical mechanical actuator

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：