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IEEE International Symposium on Intelligent control (ISIC)

作者： C. Cox I. Fletcher A. Adgar Control Systems Centre School of Computing Engineering & Technology University of Sunderland Sunderland UK

Compared to other process industries, the technology employed by the water industry is of a relatively low level. In general, however, methods of process regulation are far from ideal, leading to inefficient plant operation, occurrence of unnecessary costs and in some cases low water quality. Improvements in control and supervision methods have been recognised as one means of achieving higher water quality and efficiency objectives in the potable water industry. Attempts to improve the performance of water treatment works through the application of improved control and measurement have had variable success. The most quoted reason for this is that the individual dynamic operations defining the treatment cycle are complex, highly non-linear and poorly understood. These problems are compounded by the use of faulty or badly maintained sensors. Because of their ability to capture non-linear information very efficiently, artificial neural networks (ANNs) have found great popularity amongst the control community and other disciplines. The paper discusses an application of ANNs at surface water treatment works. The study is used to describe how the introduction of ANNs has resulted in more reliable system measurement and consequently improved coagulation control.

关键词： Industrial control Technological innovation Water resources Wastewater treatment Surface treatment Coagulation control systems Chemical processes Costs Electrical equipment industry

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Automatic generation of digital filters by NN based learning: An application on paper pulp inspection

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6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001

作者： Campoy-Cervera, Pascual Munoz-García, David F. Pena, Daniel Calderón-Martínez, José A. Department of Automatic Control Electrical Engineering and Industrial Computing Universidad Politecnica de Madrid C/ Jose Gutierrez Abascal 2 Madrid28006 Spain Instituto Tecnologico de Aguascalientes SEP CONACYT Mexico

ISBN: (纸本)9783540422372

This paper presents an implementation of a digital filtering inspection system applied on a paper pulp sheet production process. The automation of the inspection phase is particularly critical during this process and its solution is highly complex. The system is based on neural network learning, allowing a compromise between resolution and processing speed. The experimental results demonstrating the use of this algorithm for the visual detection of defects in images obtained from a real factory environment are presented. These results show that the developed learning method generates filters that fulfil the required inspection standard. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Inspection

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Self-tuning position and force control of an underwater hydraulic manipulator

Self-tuning position and force control of an underwater hydr...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： A.C. Clegg M.W. Dunnigan D.M. Lane Advanced Control Technology Club Industrial Systems and Control Limited Glasgow UK Ocean Systems Laboratory Department of Computing and Electrical Engineering Heriot-Watt University Edinburgh UK

ISBN: (纸本)0780365763

Current generation unmanned underwater vehicles, equipped with robotic manipulators, are teleoperated and consequently place a large workload burden on the human operator. A greater degree of automation could improve the efficiency and accuracy with which underwater tasks are carried out. These tasks can involve manipulator motion that is both unconstrained and/or constrained. For unconstrained motion, where a trajectory requires following, a prerequisite is good joint angle control. An adaptive self-tuning pole-placement controller is used for joint angle control. Practical results show the benefits compared to the conventional fixed-gain control. For constrained motion, simultaneous controls of position and force are often required. An adaptive hybrid position/force controller is proposed and compared to a fixed-gain version. Simulation and practical results illustrate the merits and drawbacks of each scheme.

关键词： Force control Automatic control Adaptive control Programmable control Motion control Inspection Mechanical variables control Manipulator dynamics control systems Underwater vehicles

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A GENERAL UPDATING RULE FOR DISCRETE HOPFIELD-TYPE NEURAL NETWORK WITH TIME-DELAY AND THE CORRESPONDING SEARCH ALGORITHM

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International Journal of Computational Intelligence and Applications 2001年第4期1卷 399-412页

作者： DANIEL S. YEUNG SHENSHAN QIU ERIC C. C. TSANG XIZHAO WANG Dept. of Computing The Hong Kong Polytechnic University HungHom Hong Kong China Department of Automatic Control Engineering South China University of Technology Guangzhou 510640 China Hubei University Hubei China

In this paper, the Hopfield neural network with delay (HNND) is studied from the standpoint of regarding it as an optimizing computational model. Two general updating rules for networks with delay (GURD) are given based on Hopfield-type neural networks with delay for optimization problems and characterized by dynamic thresholds. It is proved that in any sequence of updating rule modes, the GURD monotonously converges to a stable state of the network. The diagonal elements of the connection matrix are shown to have an important influence on the convergence process, and they represent the relationship of the local maximum value of the energy function to the stable states of the networks. All the ordinary discrete Hopfield neural network (DHNN) algorithms are instances of the GURD. It can be shown that the convergence conditions of the GURD may be relaxed in the context of applications, for instance, the condition of nonnegative diagonal elements of the connection matrix can be removed from the original convergence theorem. A new updating rule mode and restrictive conditions can guarantee the network to achieve a local maximum of the energy function with a step-by-step algorithm. The convergence rate improves evidently when compared with other methods. For a delay item considered as a noise disturbance item, the step-by-step algorithm demonstrates its efficiency and a high convergence rate. Experimental results support our proposed algorithm.

关键词： Discrete Hopfield neural network convergence conditions stable state convergence theorem

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Self-Tuning Position and Force control of an Underwater Hydraulic Manipulator

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IFAC Proceedings Volumes 2001年第7期34卷 149-154页

作者： A.C. Clegg M.W. Dunnigan D.M. Lane Advanced Control Technology Club Industrial Systems and Control Ltd. 50 George Street Glasgow G1 1QE United Kingdom Ocean Systems Laboratory Department of Computing and Electrical Engineering Heriot-Watt University Riccarton Edinburgh EH14 4AS United Kingdom

Current generation unmanned underwater vehicles, equipped with robotic manipulators, are teleoperated and consequently place a large workload burden on the human operator. A greater degree of automation could improve the efficiency and accuracy with which underwater tasks are carried out. These tasks can involve manipulator motion that is both unconstrained and/or constrained. For unconstrained motion, where a trajectory requires following, a prerequisite is good joint angle control. An adaptive self-tuning pole-placement controller is used for joint angle control. Practical results show the benefits compared to conventional fixed-gain control. For constrained motion, often simultaneous control of position and force is required. An adaptive hybrid position/force controller is proposed and compared to a fixed-gain version. Simulation and practical results illustrate the merits and drawbacks of each scheme.

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Optimal wavelength allocation and flow assignment in multiwavelength optical networks

Optimal wavelength allocation and flow assignment in multiwa...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： O. Kabranov D. Makrakis C. Charalambous D. Ionescu Broadband Wireless and Internetworking Research Laboratory School of Information Technology and Engineering (SITE) University of Ottawa Ottawa ONT Canada Network Computing and Control Technologies Laboratory School of Information Technology and Engineering (SITE) University of Ottawa Ottawa ONT Canada

Automatically switched optical networks (ASON) require a control strategy that determines the optimal distribution of flows over different wavelengths. Such a strategy will increase the profit, by allowing service providers to quickly and effectively define and deploy new service offers. We introduce a demand elasticity based model for wavelength and flow assignment in multiwavelength optical networks. The model captures the appropriate optical flow for every link and for every wavelength using price/demand elasticity. The model assumes that the physical and logical topology of the optical network, the maintenance cost, and the traffic demands are known parameters. Under these assumptions a mixed integer optimization is used for wavelength allocation and flow assignment of the requested traffic demand and surplus maximization for the transport service supplier, operating the optical network. A case study shows how the bandwidth demand affects the supplier's profit.

关键词： Optical fiber networks Image motion analysis Elasticity Telecommunication traffic Traffic control Optimal control Automatic control Optical control Network topology Costs

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ANN-based Tools Lead to Improved Feedforward Coagulation control Policy

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IFAC Proceedings Volumes 2001年第22期34卷 337-342页

作者： C.S. Cox A. Adgar I. Fletcher T.J. Böhme C.A. Jones Control Systems Centre University of Sunderland School of Computing Engineering & Technology Edinburgh Building Chester Road Sunderland SRI 3SD UK Northumbrian Water Ltd. Leat House Pattinson Rd District 15 Washington NE38 8LB UK.

Attempts to improve the performance of water treatment works through the application of improved control and measurement have had variable success. The most quoted reason for this is that the individual dynamic operations defming the treatment cycle are complex, highly non-linear and poorly understood. These problems are compounded by the use of faulty or badly maintained sensors. The efficient and robust operation of any industrial system is critically dependent on the quality of the measurements made. Also, the structure of the control policy and choice of the individual controller parameters are important decisions to the economic *** of their ability to capture non-linear information very efficiently, artificial neural networks (ANNs) have found great popularity amongst the 'control community' and other disciplines. This paper discusses a recent application of ANNs at surface water treatment works. The study is used to describe how the introduction of ANNs has resulted in more reliable system measurement and consequently improved coagulation control

关键词： Water Treatment Coagulation control Artificial Neural Networks Process control Sensor Failure Detection

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Regularization and validation of neural network models of nonlinear systems

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e & i Elektrotechnik und Informationstechnik 2000年第1期117卷 24-31页

作者： I. Petrović M. Baotić N. Perić Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering in Automation University of Zagreb Zagreb Croatia

A characteristic feature of the neural network models is the large number of parameters. A model offering many parameters usually gives rise to problems, and the variance contribution to the modeling error might be very high. Therefore, it is crucial to find the model with the optimal number of parameters. In this paper two techniques of selection of the optimal number of model parameters are described and compared: explicit and implicit regularization techniques. Model validation forms the final stage of an identification procedure with the aim of assessing objectively whether the identified model agrees sufficiently well with the observed data. In this paper the reliability of the correlation-based validation tests and the χ2-test is analyzed.

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A Knowledge-Base Generating Fuzzy-Neural controller

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IFAC Proceedings Volumes 2000年第28期33卷 207-212页

作者： Ivan Petrović Kristijan Maček Nedjeljko Perić Department of Control and Computer Engineering in Automation Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 10000 Zagreb Croatia

This paper presents a modification to the Kandadai and Tien’s learning algorithm for tuning a fuzzy-neural controller that is able to automatically generate a knowledge base. Tuning is based on reinforcements from a dynamical system, thus giving a pseudosupervised learning scheme using error backpropagation. Originally, a weak reinforcement in the form of a binary failure signal was assumed which proved to be insufficient in terms of steady-state error. Therefore, a continuous reinforcement signal is applied enabling the system to correct the error as well as decreasing the overall control effort in the learning phase.

关键词： expert systems intelligent control fuzzy logic neural networks

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Pole Placement controller Design Based on Fuzzy Process Model

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IFAC Proceedings Volumes 2000年第28期33卷 219-224页

作者： Robert Cupec Nedjeljko Perić Ivan Petrović Department of Control and Computer Engineering in Automation Faculty of Electrical Engineering and Computing university of Zagreb Unska 3 10000 Zagreb Croatia

An explicit self-tuning controller based on the Takagi-Sugeno fuzzy model of the process is proposed. The fuzzy model is represented as a linear regression model whose parameters are functions of some of the process variables. Such a model can be considered as a linear time-varying model whose parameter values are known at every moment. The pole placement design procedure modified for time-varying systems is applied to obtain the polynomial controller parameters that provide the desired closed-loop poles. The proposed algorithm is very simple, and thus suitable for on-line controller design in adaptive control systems.

关键词： fuzzy control fuzzy modeling adaptive control

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