SVIBOR - Project code: 2-08-187

MINISTRY OF SCIENCE AND TECHNOLOGY

Strossmayerov trg 4, HR - 10000 ZAGREB
tel.: +385 1 459 44 44, fax: +385 1 459 44 69
E-mail: ured@znanost.hr

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Project code: 2-08-187


NEURAL NETWORKS FOR MODELLING AND CONTROL SYSTEMS


Main researcher: KECMAN, VOJISLAV (20623)


Assistants
Type of research: applied
Duration from: 01/01/91. to 12/31/96.
Papers on project (total): 32
Papers on project quoted in Current Contents: 3
Institution name: Fakultet strojarsva i brodogradnje, Zagreb (120)
Department/Institute: Automation and Measurement Department
Address: Ivana Lučića 5. P.O.B. 194, HR - 1000 Zagreb, CROATIA
City: 10000 - Zagreb, Croatia
Communication
Phone: 385 (0) 1 6111-944
Fax: 385 (0) 1-514-535
E-mail: Vojislav.Kecman@x400.srce.hr
Summary: In recent years the importance of better understanding, modeling and control has increased greatly in all fields of human endevours e.g. in biology, physiology, medicine, economics, ecology and certainly in the field of engineering, or more precisely in control, automatization and flexible manufacturing. In technical applications the creation of models (theoretically or by experiments) and control of the processes and systems is an essential part of man's intelectual activity. Throughout the past years we have been witnessing a fast development and recognition of these fields which is related to requirements from practice stimulated by the need for: - satisfying of quality, productivity and enviromental demands, - responding to global undustrial competition -responding to global industrial competition, - more information and better understanding of processe and systems. My personal contributions, as well as the very contributions of my younger coworkers Mr. J. Petric, Mr. D. Majetic, Mr. M. Siroki (published in many papers, monographs and books) are particularly strong in the field of mathematical modeling of system dynamics, in original approach in treating the dynamics of different processes in a unified and consistent way and in theory and application of modern computing paradigms such as Artificial Neural Networks (ANN) and Fuzzy Logic System (FLS). The newest field of our research (and main part of this proposal) is connected with recent advances in ANN and FLS. This originated from problems when it is not possible (or at least it is very hard) to adequately represent system characteristics such as nonlinearity, time delays, saturation or time-varying paremeters. For such, very common, situations neural networks can be of great interest to the dynamics and control engineers because they have potential to treat many problems that cannot be handled by traditional analytic approaches. I am sure that ANN and FLS with their massive parallelism, approximation, generalization and learning capabilities can provide better solutions to (at least some) old and new control problems. This capability of approximation, generalization, learning and embeding of the existing human knowledge stands behind the word intelligent in the title of this proposal. As it is well known to all working in the computer time consumption field of neural networks, a lot of simulation time has been spent in my laboratory too , trying to train hundreds of network configurations for the sake of dynamics and control of real physical systems. It has been proved that out of many types of neural networks particularly the multilayerd feedforward ANN with error back-propagation algorithm can be used for simulation of dynamical systems. It is interesting to note that for back-propagation network common sigmoidal transfer function gives much worse results than sinusoidal transfer function. The last one has also better properties than tangens-hyperboloid transfer function but this is not so firm claim at this moment. Thus, for detailed experimental training of three-therm controller (PID controller) the sinusoidal transfer function was used. The series of simulation runs of different systems proved that the choice of training procedure and ANN structure is of great importance and right now a series of simulation experiments is trying to establish the connections between the number of hidden layers and processing elements in them with the type of dynamical systems. The world's new research is now taking place in the field of aplication of RBF Networks as well as in connecting RBF with FLS. Finally, our newest and the most intriguing investigations are now in the field of design of inverse-dynamics controller using the neural network approach. This concept of an inverse-dynamics controller is an old one but the realization of such controller is not an easy task, particularly when the system dynamics is unknown and with varying and uncertain parameters (gains, poles, zeros). Also, very strong research will take place in the field of solution of some vision problems as well as in control of flexible robots. We plan to do research also in the field of more deeper understanding of foundations of ANN and FLS paradigms.

Keywords: Identification and Control of Nonlinear Systems, Artificial Neural Networks, Fuzzy Logic Systems

Research goals: Final goal of proposed research is to merge Artificial Neural Networks and Fuzzy Logic Systems computing paradigms. Recently, we proved a very strong result about equivalence of RBF Network and FLS. This is a world new and powerful result and because of its importance we wil try to track this line of research, too. The goals will be also the development of theories, methodologies, algorithms and software for these computing paradigms.

COOPERATION - PROJECTS


  1. Name of project: Neural Networks in Control Systems
    Name of institution: Institut fur Automatisierungstechnik, Universitat Bremen
    City: Bremen, Njemačka

  2. Name of project: 2-08-171 ALGORITMI VOĐENJA ROBOTA I FLEKSIBILNIH PROIZVODNIH SISTEMA
    Name of institution: Fakultet strojarstva i brodogradnje
    City: 10000 - Zagreb, Croatia

  3. Name of project: 2-07-176 FLEKSIBILNA AUTOMATIZACIJA PROIZVODNJE I INDUSTRIJSKI ROBOTI
    Name of institution: Fakultet strojarstva i brodogradnje
    City: 10000 - Zagreb, Croatia
Other information about the project.
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