In this paper we present a methodology for decision making in multi agent system comprised of agents driven by repulsive force and attracting force. Navigation functions are expressed as a set of fuzzy rules obtained ...
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ISBN:
(纸本)9781424422241
In this paper we present a methodology for decision making in multi agent system comprised of agents driven by repulsive force and attracting force. Navigation functions are expressed as a set of fuzzy rules obtained by fuzzy Lyapunov stability criteria, thus ensuring stability of the overall system. The goal of the proposed methodology is to create desired formations by moving agents from their initial positions to formation targets, while in the same time avoiding collisions. The destination targets are dynamically permutated as long as the required formation is achieved.
Initially introduced as a model-free control design method, in today practice fuzzy control is dominantly used as yet another nonlinear control technique based either on a linear or nonlinear model of a process. This ...
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Initially introduced as a model-free control design method, in today practice fuzzy control is dominantly used as yet another nonlinear control technique based either on a linear or nonlinear model of a process. This paper addresses the stability assessment of a fuzzy logic control system based only on the partial knowledge of a controlled process. Lyapunov stability conditions are derived and analyzed by using fuzzy numbers and fuzzy arithmetic. The experimental results obtained for a non-stable second-order system confirmed that this approach could be successfully implemented. Some questions, addressed in the paper, remained open for further investigation.
In this paper we describe a procedure that exploits geometric properties of state space in the investigation of the system stability. Although this method is cumbersome, its practical value becomes clear in the situat...
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In this paper we describe a procedure that exploits geometric properties of state space in the investigation of the system stability. Although this method is cumbersome, its practical value becomes clear in the situation when state space is reduced to a phase plane, which is the case in a second-order system. Then phase plane analysis offers well known procedures (especially in case f(.) is linear) for the determination of the system stability. Simulation results, obtained by implementation of the proposed method on the fuzzy controller design, are given at the end of the paper
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