This paper proposes an approach for the design of discrete-time decentralized control systems covering not only the case of m-step delay sharing information pattern, but also any general nonclassical information patte...
This paper proposes an approach for the design of discrete-time decentralized control systems covering not only the case of m-step delay sharing information pattern, but also any general nonclassical information pattern where the non-local information is not spread among the subsystems. It employs the model-based predictive control (MBPC) scheme combined with fuzzy prediction for the interconnections among the subsystems. A state space model is used at each control station to predict the corresponding subsystem output over a long-range time period. The interaction trajectories are considered to be non-linear functions of the states of the subsystems. In all cases, the interconnections and the necessary predictions for them are estimated by an appropriate neuro-fuzzy identifier trained on-line using the back-propagation training algorithm. Representative computer simulation results are provided and compared for nontrivial example systems.
Ultrasonic range sensors are used to obtain the information required for collision-free navigation of a mobile robot in a semi-structured or unstructured environment. A set of range readings from a ring of sonars are ...
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This paper investigates the implementation of a hybrid methodology, which combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for the modeling of the supervisor of Large Scale Systems. The description...
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This paper investigates the implementation of a hybrid methodology, which combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for the modeling of the supervisor of Large Scale Systems. The description and the construction of Fuzzy Cognitive Map will be extensively examined and it will be proposed a model for the supervisor. There is an oncoming need for more autonomous and intelligent systems, especially in Large Scale Systems and the application of Fuzzy Cognitive Map for the modeling of the Supervisor may contribute in the development of more autonomous systems.
Intelligent Manufacturing Systems is the major challenge that must be met by scientific community in order to develop the next generation sophisticated manufacturing systems. This paper presents an overview of new pro...
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Intelligent Manufacturing Systems is the major challenge that must be met by scientific community in order to develop the next generation sophisticated manufacturing systems. This paper presents an overview of new promising domains, such as fuzzy logic, neural networks and hybrid intelligent control systems, that could be utilised to achieve human intention for more advanced manufacturing systems. The modelling of the supervisor of manufacturing systems using fuzzy cognitive map is proposed and system functionalization architecture is presented. Finally, conclusions of an international symposium on issues and challenges of Manufacturing and Control Education for the 21 st Century are presented.
Advanced powerful tools are sought for in order to conduct power system studies, especially for large scale systems. In this paper, an efficient PC-based environment for power system studies is presented. It is based ...
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Advanced powerful tools are sought for in order to conduct power system studies, especially for large scale systems. In this paper, an efficient PC-based environment for power system studies is presented. It is based on both analytical and artificial intelligence techniques. To demonstrate its effectiveness, this environment has been used for restoration studies following a recent blackout in the Hellenic power system, with very promising results.
In this paper the application of Fuzzy Cognitive Map (FCM) in controlling a process problem and its use in modelling Supervisory Manufacturing Systems is presented. The description, construction and the mathematical m...
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In this paper the application of Fuzzy Cognitive Map (FCM) in controlling a process problem and its use in modelling Supervisory Manufacturing Systems is presented. The description, construction and the mathematical model of Fuzzy Cognitive Map are examined. Then, a chemical process is modelled with FCM and its behaviour is simulated and the application of Fuzzy Cognitive Maps in the mode ling of the Supervisor of Manufacturing Systems is discussed.
Extends the kinematic manipulability concept commonly used for serial manipulators to general constrained rigid multibody systems. Examples of such systems include multiple cooperating manipulators, multiple fingers h...
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Extends the kinematic manipulability concept commonly used for serial manipulators to general constrained rigid multibody systems. Examples of such systems include multiple cooperating manipulators, multiple fingers holding a payload, multi-leg walking robots, and variable geometry trusses. Explicit formulas for velocity and force manipulability ellipsoids are derived and their duality explained. The concept of unstable grasp and manipulable grasp are also extended and illustrated with examples.
We study control problems for a specific mechanical system consisting of a rigid base body with an unactuated internal degree of freedom. The key assumptions are that the translational and rotational motion of the bas...
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We study control problems for a specific mechanical system consisting of a rigid base body with an unactuated internal degree of freedom. The key assumptions are that the translational and rotational motion of the base body can be completely controlled by external forces and moments, while the internal degree of freedom is unactuated. This specific example is representative of a class of underactuated mechanical control systems that are not linearly controllable. This class of control problems presents major theoretical and practical difficulties, and such models do arise in important terrestrial and spacecraft applications. Our focus in this paper is on the simplest mechanical example in this class; it illustrates important features and difficulties associated with the class of such problems.
In this paper, we present a method for high-level control of robots whose low-level software is based on dynamically reconfigurable, reusable real-time software modules. Our approach is to use an embedded interpreter ...
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In this paper, we present a method for high-level control of robots whose low-level software is based on dynamically reconfigurable, reusable real-time software modules. Our approach is to use an embedded interpreter for a general-purpose programming language to direct the operation of the low-level modules toward meeting the task-level goals of the robot. To this end, we present RSK, a virtual-machine kernel implementing a scheme interpreter capable of hard real-time operation, and employing a method of code execution we call "message-based evaluation" (MBE). MBE is a novel combination of a traditional code execution model and a message-passing architecture, which simplifies the process of writing code for managing the robot's reconfigurable subsystem.
Models of human control strategy (HCS), which accurately emulate dynamic human behavior, have far reaching potential in areas ranging from robotics to virtual reality to the intelligent vehicle highway project. A numb...
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Models of human control strategy (HCS), which accurately emulate dynamic human behavior, have far reaching potential in areas ranging from robotics to virtual reality to the intelligent vehicle highway project. A number of learning algorithms, including fuzzy logic, neural networks, and locally weighted regression exist for modeling continuous human control strategies. These algorithms, however, may not be well suited for modeling discontinuous human control strategies. Therefore, we propose a new stochastic discontinuous modeling framework, for abstracting human control strategies, based on hidden Markov models. In this paper, we first describe the real-time driving simulator which we have developed for investigating human control strategies. Next, we demonstrate the shortcomings of a typical continuous modeling approach in modeling a discontinuous human control strategy. We then propose an HMM-based method of modeling discontinuous human control strategies, and show that the proposed controller overcomes these shortcomings and demonstrates greater fidelity to the human training data. We conclude the paper with further comparisons between the two competing modeling approaches.
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