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Design and implementation of a logic controller using petri nets and ladder logic diagrams

Systems Analysis Modelling Simulation 2002年第1期42卷 135-167页

作者： Tzafestas, S.G. Pantelelis, M.G. Kostis, D.L. Intelligent Robotics and Automation Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Zografou 15773 Athens Greece

Design methods for sequence controllers play an important role in modern industrial automation. The Programmable Logic Controller (PLC) in general meets today's automation requirements. The increasing complexity and varying needs of modern discrete industrial systems have challenged the traditional design methods such as the use of Ladder Logic Diagrams (LLDs) for PLCs. This paper presents a new method for using Petri net models in order to design and implement a sequence controller for a small scale robotic cell which consists of a robotic manipulator, a variety of sensors and electro-pneumatic actuators. The proposed method leads directly to the generation of the associated LLD and it has proved to be effective through the application to sequence control of a small-scale industrial system. In addition, it has reduced the period for developing, debugging and reengineering of the LLD, compared to the traditional method that directly prepares an LLD. Experimental results are included to show the effectiveness of the PN/LLD controller. © 2002 Taylor & Francis Ltd.

关键词： Programmable logic controllers

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NeuroFAST: high accuracy neuro-fuzzy modeling

NeuroFAST: high accuracy neuro-fuzzy modeling

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IEEE International Conference on Artificial Intelligence Systems (ICAIS)

作者： S.G. Tzafestas K.C. Zikidis Intelligent Robotics and Automation Laboratory Electrical and Computer Engineering Department National and Technical University of Athens Athens Greece

Most fuzzy modeling algorithms rely either on simplistic (grid type) or off-line (trial-and-error type) structure identification methods. The proposed neurofuzzy modeling architecture, NeuroFAST, is an on-line, structure and parameter learning algorithm, featuring high function approximation accuracy. It is based on the first order Takagi-Sugeno-Kang (TSK) model (functional reasoning), where the consequence part of each fuzzy rule is a linear equation of the input variables. Fuzzy rules are allocated as learning evolves by a modified Fuzzy ART (Adaptive Resonance Theory) mechanism, assisted by fuzzy rule splitting and adding procedures (structure learning). The well known /spl delta/-rule continuously tunes learning weights on both premise and consequence parts (parameter identification). Tested on the Box-Jenkins gas furnace process modeling and the Mackey-Glass chaotic time series prediction, NeuroFAST yields very good results in terms of approximation accuracy, outperforming all known approaches.

关键词： Function approximation Approximation algorithms Takagi-Sugeno-Kang model Fuzzy reasoning Equations Input variables Subspace constraints Resonance Parameter estimation Testing

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FUZZY COGNITIVE MAPS MODEL FOR LARGE SCALE COMPLEX SYSTEMS

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IFAC Proceedings Volumes 2002年第1期35卷 277-282页

作者： C.D. Stylios Peter P. Groumpos Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Rion 26500 GREECE Tel. +30 61 997293 Fax. +30 61 997309

Most of the technological systems are characterized by complexity, coupled relationships with feedback and a demanding procedure to develop their models. This paper presents the Soft Computing technique of Fuzzy Cognitive Maps that are knowledge-based methodologies suitable to describe and model complex systems and handle with available information from an abstract point of view. Fuzzy cognitive Maps develop behavioral model of the system exploiting the experience and knowledge of experts. Fuzzy Cognitive Maps applicability in modeling complex systems is presented and a general hierarchical structure is proposed to model any complex system. Within the hierarchical structure, Fuzzy Cognitive Map models the coordinator of the system and develops an abstract conceptual model of the complex system.

关键词： Complex Systems Fuzzy Cognitive Map Knowledge based systems

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ACTIVATION HEBBIAN LEARNING RULE FOR FUZZY COGNITIVE MAPS

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IFAC Proceedings Volumes 2002年第1期35卷 319-324页

作者： Elpiniki Papageorgiou Chrysostomos D. Stylios Peter P. Groumpos Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Rion 26500 GREECE Tel. +30 61 997293 Fax. +30 61 997309

Fuzzy Cognitive Maps (FCM) is a soft computing, modeling methodology for complex systems, which is originated from the combination of fuzzy logic and neural networks. Many different learning algorithms have been suggested for the training of neural networks. Only some initial thoughts on learning rules have described for FCMs. A learning law is a mathematical algorithm, which can train the FCM by selecting the appropriate weights and it is very important for a system to have learning and adaptive capabilities. In this paper a new learning algorithm, the Activation Hebbian Learning (AHL) has been proposed for FCMs. The learning rule for a FCM is a procedure where FCM weight matrix is modified in order the FCM to model the behavior of a system. Simulation results proving the strength of the learning rule are provided.

关键词： Hebbian learning Fuzzy Cognitive Maps Modeling Unsupervised learning

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ON–LINE IMPROVEMENT FOR THE DECENTRALIZED PREDICTIVE CONTROL OF THE HEAT DYNAMICS OF A GREENHOUSE

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IFAC Proceedings Volumes 2002年第1期35卷 37-42页

作者： E.J. Kyriannakis K.G. Arvanitis N. Sigrimis National Technical University of Athens Dept. of Electrical and Computer Engineering Intelligent Robotics and Automation Laboratory Zographou 15773 Athens GREECE Agricultural University of Athens Department of Agricultural Engineering Athens 11855 GREECE

A decentralized model-based predictive controller is used for the design of discrete-time control systems aiming at regulating the air temperature and heat supply in greenhouses. Moreover, alternative techniques are proposed for the approximation of the decentralized part of the control and the on-line improvement of the overall control problem. A state space model is used to predict the corresponding local indoor temperature over a long-range time-period and approximate models are used to predict the interactions among the subsystems. The sun radiation and outdoor temperature are treated as external disturbances that affect the overall system dynamics. A series of energy fluxes consist the heating system and the predictive controllers have proved to be powerful in controlling the supply temperature.

关键词： Decentralized control predictive control greenhouse heat dynamics

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Aging agents

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Artificial Life and robotics 2001年第1期5卷 46-57页

作者： Elpida S. Tzafestas Intelligent Robotics and Automation Laboratory Electrical and Computer Engineering Department National Technical University of Athens Zographou Greece

We are adopting Brooks and Wiley's view of evolution as an irreversible process capable of producing increasingly greater complexity at higher organizational levels. We start from the assumption that the evolutionary force is intrinsic in the living system, and is in reality a continuous senescence function leading gradually and unavoidably to death. We are therefore seeking a senescence function that favors social rather than solitary agents in terms of longevity, without prespecifying in detail the agent's life span. We show that a senescence function relyling on negative (destructive) feedback links from metabolism to genetic program conforms with these specifications. We also show that senescence should affect all the regulation parameters of the agent, and that the system remains nonmanipulable and unpredictable as far as its life span is concerned. This senescence function favors the more “cognitive” agent models (the ones having additional regulation loops), and thus the emergence of organizations of a higher order that have more elaborate social relations.

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Fuzzy Cognitive Maps Multi-Model for Complex Manufacturing Systems

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IFAC Proceedings Volumes 2001年第8期34卷 61-66页

作者： Chrysostomos D. Stylios Peter P. Groumpos Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Rion 26500 GREECE Tel. +30 61 997293 Fax. +30 61 997309

This paper examines the usefulness of Fuzzy Cognitive Maps in modeling complex systems and specifically their use in modeling supervisory manufacturing systems and handling with information from an abstract point of view. The use of Fuzzy Cognitive Maps for developing behavioral model for a complex system is discussed. Fuzzy Cognitive Maps applicability in modeling complex systems is presented and a hierarchical structure is proposed to model and control a complex system. Complex system is decomposed in subsystems and for each one a FCM model is developed. A hierarchical structure is proposed where Fuzzy Cognitive Map models the supervisor of the system and develops an abstract conceptual model of the complex system.

关键词： Fuzzy Cognitive Map Modeling Supervision Complex Systems Manufacturing Systems

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Decentralized Predictive Control ofthe Heat Dynamics of a Greenhouse

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IFAC Proceedings Volumes 2001年第11期34卷 152-157页

作者： S.G. Tzafestast E.J. Kyriannal K.G. Arvanitis N. Sigrimis National Technical University of Athens. Dept. of Electrical and Computer Engineering Intelligent Robotics and Automation Laboratory Zographou 15773 Athens GREECE. Agricultural University of Athens Department of Agricultural Engineering Athens 11855 GREECE.

This paper proposes a decentralized model - based predictive controller approach for the design of discrete-time control systems for the regulation of the air temperature and heat supply in greenhouses. A state space model is used at the greenhouse to predict the corresponding indoor temperature over a long-range time period. The sun radiation and outdoor temperature are treated as external disturbances tliat affect the overall system dynamics. A series of energy fluxes consist the heating system and the predictive controllers have proved to be powerful in controlling the supply temperature.

关键词： Decentralized control Predictive control Greenhouse heat dynamics control

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A simulator to analyze creeping locomotion of a snake-like robot

A simulator to analyze creeping locomotion of a snake-like r...

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IEEE International Conference on robotics and automation (ICRA)

作者： Shugen Ma W.J. Li Yuechao Wang Robotics Laboratory CAS Shenyang Institute of Automation Shenyang China Department of Systems Engineeringineering Faculty of Engineering Ibaraki University Hitachi Ibaraki Japan Department of Automation/Computer-Aided Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

ISBN: (纸本)0780365763

Snakes perform many kinds of movement that are adaptable to the environment. Utilizing the snake (its forms and motion) as a model to develop a snake-like robot that emulates a snakes' function is important for generating a new type of locomotor and expanding the possible use of robots. We developed a simulator to simulate the creeping locomotion of a snake-like robot, in which the robot dynamics is modeled and its interaction with the environment is considered through Coulomb friction. This simulator makes it possible to analyze the creeping locomotion with the normal-direction slip coupled to gliding along the tangential direction. Through the developed simulator, we investigated the snake-like robot creeping locomotion which is generated only by swinging each of the joints from side to side, and discussed the optimal creeping locomotion of the snake-like robot that is adaptable to a given environment.

关键词： Analytical models Robots robotics and automation Friction Computational modeling Systems engineering and theory Content addressable storage Tracking Ice National electric code

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Fuzzy logic control of a robot manipulator based on visual servoing

Fuzzy logic control of a robot manipulator based on visual s...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： C.S. Kim W.H. Seo S.H. Han O. Khatib Department of Mechanical Design Graduate School of Kyungnam University Masan South Korea Division of Mechanical Engineering and Automation Kyungnam University Masan South Korea Computer Science and Robotics Laboratory Department University of Stanford Stanford CA USA

ISBN: (纸本)0780370902

In this paper, a solution to a manipulation control problem (target identification and grasping) using a real platform in combination with a vision system is proposed. The task for the end-effector is to approach a randomly placed spherical object of known size. The control law is approximated by using an approach based on fuzzy logic. The controller determines the parameters of the target (a spherical object acquired from the vision system mounted on the manipulator) using a vision algorithm. The fuzzy rules are built in a supervised way, after studying the behavior of the system. Experimental results obtained using an industrial manipulator (AM1 with 6 DOF model in Samsung Electronics Co., Ltd., Korea) are presented.

关键词： Fuzzy logic Robot control Manipulators Visual servoing Control systems Machine vision Fuzzy systems Electrical equipment industry Electronics industry Industrial electronics

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