Fuzzy logic-based formulation of the organizer of intelligent robotic systems

Stellakis, Haris M.; Valavanis, Kimon P.

doi:10.1007/bf00452099

Cited by 17 publications

(3 citation statements)

References 20 publications

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“…In brief, first an incentive leader-follower method [25] is presented to study such problems in the elastic traffic, which is to be implemented within the lower layer of the supervisory task-oriented controller [5], [lo], [48] the upper layer of which is an organizing discrete-event (e.g. a fuzzy-Petri-net) supervisor [ 1 I].…”

Section: N On the Background Researchmentioning

confidence: 99%

“…It appeared there exist several alternative sub-classes of the control architectures, consistently employing different formalisms at different levels [ 7 ] , [91, [461, [491, [571, [591, worth to be investigated with respect to their control potential and structural properties. One of these architectures [4], [lo], [48] employs fuzzy-logic control algorithms at its upper, 'qualitative' or 'non-analytical', level of control, and conventional linear and/or affine non-liner controls at its lower levels, the later implying 'quantitative' or 'analytical' algorithms. The other system architecture is an entirely intelligent two-level system for integrated control and supervision as reported in with machine intelligence employed at both supervisory (upper) and regulatory (local) levels.…”

Section: Outline Of the Two-level Intelligent Control And Supervisionmentioning

confidence: 99%

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Hybrid leader-follower and fuzzy-Petri-net traffic rate control and supervision in network systems

Dimirovski

Jing²,

Zhang³

5th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service. TELSIKS 2001. Proceedin

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TELSIKS 2001 PROCEEDINGS (2 volume)This paper addresses networking and traffic control problems in network systems along with the potential for introducing soft-computing applications at supervisory control level. The incentive Stackelberg strategy control concept was introduced to the networking model that comprises subsidiary systems of users and of network. A linear strategy is proposed for the elastic traffic problem, and results are demonstrated and illustrated via Kelly's (1997) example. The presented method is extended to the non-elastic traffic problem and results are also illustrated via a slightly modified Kelly example. Ways of employing combined analytical and soft-computing approaches and techniques in a hybrid framework have been investigated. A fuzzy-Petri-net supervisor is proposed to implement the strategic decision and control layer. Insofar obtained results are rather encouraging, and this research continues further towards possible implementation as well as some open theoretical issuesFac Electr Engn; Ei Holding Co; Radio-Televis Serbia; IEEE; MTTs; APS; ComSoc; IEEE Broadcast Technol Soc; IEEE Region 8 Tech; Serbian Acad Sci & Art; Serbian Minist Sci Technol & Dev; Serbian Minist Traff & Communicat; Yugoslav Minist Transportat & Telecommunicat; Yugoslav IEEE Sect; Soc Telecommunicat, Yugoslav IEEE Communicat Chapter; Soc Microwave Theory & Tech, Yugoslav IEEE MTT Chapter; Yugoslav ETRAN So

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Section: N On the Background Researchmentioning

confidence: 99%

Section: Outline Of the Two-level Intelligent Control And Supervisionmentioning

confidence: 99%

Hybrid leader-follower and fuzzy-Petri-net traffic rate control and supervision in network systems

Dimirovski

Jing²,

Zhang³

5th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service. TELSIKS 2001. Proceedin

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“…The controlling factor in a Markov chain is the transition probability, which is a conditional probability for the system to go to a particular next state given the current state of the system. In the present study we have explored a possible application of a twolevel decision and control architecture, a supervisory controller that implements a fuzzy-Petri-net reformulation of Saridis' organizing intelligent controller [2,3] following his principle of increasing intelligence with decreasing precision [4]. The supervisory controller has been constructed as a fuzzy-Petri-net production rule system [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-petri-net reasoning supervisory controller and estimating states of markov chain models

Dimirovski

IEEE 3rd International Conference on Computational Cybernetics, 2005. ICCC 2005.

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Abstract. Markov chain models are efficient tools for representing stochastic discrete event processes with wide applications in decision and control. A novel approach to fuzzy-Petri-net reasoning generated solution to initial or another state in Markov-chain models is proposed. Reasoning is performed by a fuzzy-Petri-net supervisory controller employing a fuzzy-rule production system design and a fuzzyPetri-net reasoning algorithm, which has been developed and implemented in C++. The reasoning algorithm implements calculation of the degrees of fulfilment for all the rules and their appropriate assignment to places of Petri net representation structure. The reasoning process involves firing active transitions and calculating degrees of fulfilment for the output places, which represent propositions in the knowledge base, and determining of fuzzy-distributions for output variables as well as their defuzzified values. Finally, these values are transferred to assign the state of Markov-chain decision model in terms of transition probabilities.

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Sequencing via explanation-based learning

Tianfield

2001

Int. J. Intell. Syst.

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Fuzzy logic-based formulation of the organizer of intelligent robotic systems

Cited by 17 publications

References 20 publications

Hybrid leader-follower and fuzzy-Petri-net traffic rate control and supervision in network systems

Hybrid leader-follower and fuzzy-Petri-net traffic rate control and supervision in network systems

Fuzzy-petri-net reasoning supervisory controller and estimating states of markov chain models

Sequencing via explanation-based learning

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