We propose an approach to describe and contro1 complex systems based on fuzzy cognitive map (FCM). A mathematical model of FCMs and a ealculation method are described as well as a methodology for constructing and deve...
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This paper reviews a number of recent algorithms for mobile robot path planning, navigation and motion control, which employ fuzzy logic and neuro-fuzzy learning and reasoning. Starting with a discussion of the struct...
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This paper reviews a number of recent algorithms for mobile robot path planning, navigation and motion control, which employ fuzzy logic and neuro-fuzzy learning and reasoning. Starting with a discussion of the structure of fuzzy and neuro-fuzzy systems, two fuzzy obstacle avoidance path planning algorithms are presented followed by a 3-level neuro-fuzzy local and global path planning scheme. Then the motion planning and control problem is considered. A fuzzy path tracking strategy is outlined, followed by a fuzzy navigation algorithm among polygonal obstacles and a learning-by-doing neuro-fuzzy motion planning scheme. The paper ends with a hybrid robust motion control technique which combines the minimum interference and sliding mode control principles with fuzzy inference. A representative set of examples are included which illustrate the performance of the algorithms under various realistic conditions.
This paper presents the conceptual framework and simulation results on a general technique for time delay compensation in teleoperation, based on the prediction of the human arm position and force, i.e. effectively th...
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This paper presents the conceptual framework and simulation results on a general technique for time delay compensation in teleoperation, based on the prediction of the human arm position and force, i.e. effectively the master state. This is shown to be significantly less complex and more intuitive than predicting the slave and environment dynamics. Simple polynomial or spline predictors, employing no knowledge of the human arm dynamics, are shown to produce good performance for small time delays when the master force and position are smooth. However, for real life force profiles, better performance is achieved by employing a human arm model and predicting the neural input to it.
In this paper a general methodology for studying and solving fuzzy relation equations based on sup-t composition, where t is any continuous triangular norm, is proposed. To this end the concept of the "solution m...
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We study the behavior of drift-free systems, whose inputs take values in a finite discrete levels set with low bounded switching intervals. In particular, we solve the motion planning problem of drift-free systems und...
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We study the behavior of drift-free systems, whose inputs take values in a finite discrete levels set with low bounded switching intervals. In particular, we solve the motion planning problem of drift-free systems under the above constraints and we prove that the proposed methods provide exact steering when the system is nilpotent. Finally, we explore the details of our methods applying them to the motion planning of a drift-free system.
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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In this paper, some fundamental issues of modern multi-agent robot architectures are discussed. It is argued that the multi-agent approach provides the necessary flexibility and adaptivity for such architectures, and ...
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In this paper, some fundamental issues of modern multi-agent robot architectures are discussed. It is argued that the multi-agent approach provides the necessary flexibility and adaptivity for such architectures, and that the primary issue in designing a multi-agent robot architecture is the selection of the granularity level, i.e., the decision on decomposing the overall desired functionality physically or across tasks. It is explained why at the various system levels different decomposition grains are needed; physical components, tasks or hybrid. This granularity decision is made on the basis of specific criteria of control localization, knowledge decoupling and interaction minimization so as to identify the decision points of the overall functionality. The above criteria lead to a dual composition-decomposition relation, which provides a good basis for system scaling. The paper specializes the discussion to a proposed neuro-fuzzy multi-agent architecture, which is then applied to design the local path planning system of an indoor mobile robot.
This paper proposes an approach for the design of discrete-time decentralized control systems with m-step delay sharing information pattern, employing the modelbased predictive control (MBPC) scheme combined with fuzz...
This paper proposes an approach for the design of discrete-time decentralized control systems with m-step delay sharing information pattern, employing the modelbased 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. For all cases the interconnections and the necessary predictions for them are estimated by an appropriate adaptive fuzzy identifier based on the generation of linguistic IF-THEN rules and the on-line construction of a common fuzzy rule base. Representative computer simulation results are provided and compared for nontrivial example systems.
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.
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.
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