Intelligent Design Environment for Second-Order Positioning Systems

  • Sanda Dale University of Oradea Romania, 410087 Oradea, Universitatii, 1
  • Helga Silaghi University of Oradea Romania, 410087 Oradea, Universitatii, 1
  • Doina Zmaranda University of Oradea Romania, 410087 Oradea, Universitatii, 1 sdale@uoradea.ro, hsilaghi@uoradea.ro *Corresponding author: dzmaranda@uoradea.ro
  • Ulrich Rohde TUniversity of Technology Cottbus Germany, 03048 Cottbus, Lipezker St. 47

Abstract

From the designer perspective, in real-time applications as mechatronic systems - the control algorithm has to be easy to understand and to implement, easy to optimize and last but not least, to ensure the best possible behavior for the controlled system. The paper presents a design environment which provides the possibility to choose between a classical control algorithm (as state feedback stabilization represents) and a new approach (interpolative controllers) with the possibility to modify the designed parameters such as to obtain the optimum behavior for the controlled system. This opportunity is given through the inner structure and operating laws of the algorithm and exploited using genetic algorithm-based techniques. Interpolative-type controller category covers the controllers based on fuzzy, neural and pure interpolative algorithms, due to their common capability to make an approximate reasoning. The first ones, fuzzy and neural algorithms, are already well-known in the control area. A pure interpolative controller presumes to contain at least one block, placed no matter where in his structure, in which interpolation as mathematical operation to have place. The approach presented in the present paper uses this kind of blocks to reproduce and to optimize the behavior of an already existing controller (in this case the state feedback one) [1]. This type of controllers meets all the requirements stated at the beginning: they are easy to implement, easy to understand, they need reduced calculus time and gives notable results in specific cases. The above mentioned kind of controllers will be presented with all the necessary details in the paper. The design environment is presented as a collection of MATLAB functions, SIMULINK configurable schemes and a user interface. A case study ends the presentation with some experimental results obtained through a simulation for a specific second order positioning system.

Author Biography

Sanda Dale, University of Oradea Romania, 410087 Oradea, Universitatii, 1
Department of Mathematics and Computer Science

References

[1] S. Dale, H. Silaghi, D. Zmaranda, U. Rohde (2014), Procedural Aspects Concerning Intelligent Design Environment for Real-Time Applications, Abstracts of ICCCC Papers, 5th International Conference on Computers, Communications & Control, ICCCC2014, Baile Felix, Romania, ISSN 1844-4334, 4:41.

[2] F.G. Filip (2012), A Decision-Making Perspective for Designing and Building Information Systems, International Journal of Computers Communication & Control, ISSN 1841-9836, 7(2):264-272.

[3] S. Dale, G. Gabor, C. Gyorodi, D. Zmaranda (2010), Interpolative Control Algorithm Applied on a 3D-Mechatronic System, Proceedings of 4th International Workshop of Soft Computing Applications, Arad, Romania, 2010, 229-233.
http://dx.doi.org/10.1109/SOFA.2010.5565591

[4] A. Bara, S. Dale, T.Z. Nagy (2009), Comparative Real Time Experimental Study Case for Control Algorithms from the Implementation Point of View, Proceedings of the 8th WSEAS International Conference on System Science and Simulation in Engineering (ICOSSSE09), Genova, Italy, 302-306.

[5] S. Dale, T.L. Dragomir (2009), Interpolative-type Control Solutions, Studies in Computational Intelligence, Berlin, Springer Verlag, 196: 169-203.

[6] S. Dale (2009), Interpolative Control for a d.c. Motor Drive with Genetic Algorithm-based Tuning, Proceedings of EMES09 in Journal of Computer Science and Control Systems, Oradea, 93-96.

[7] A. Bara (2006), An Algorithm for Fuzzy Relation Identification, Proceedings IEEE-TTTC 2006, Tome1, AQTR 2006, Cluj-Napoca, 96-102.

[8] T.L. Dragomir, E. Vladu, S. Dale (2003), Interpolative-type Controller Synthesis Using Genetic Algorithms, Proceedings of CSCS14, Bucuresti, 70-75.

[9] T.L. Dragomir, I. Silea, S. Nanu (2002), Control performances improving by interpolator controllers, Proceedings of The 6th World Multiconference on Systemics, Cybernetics and Informatics, July 14-18 2002 Orlando, Florida, 6: 208-213.

[10] G.F. Kamische, J.P.Brauer (1999), Qualittsmanagement von A bis Z, Hanser.

[11] M.M. Seron, et all (1997), Fundamental limitation in filtering and control, Springer-Verlag, 1997.

[12] D. Drechsel (1996), Regelbasierte Interpolation und Fuzzy Control, Vieweg, 1996.
Published
2014-11-17
How to Cite
DALE, Sanda et al. Intelligent Design Environment for Second-Order Positioning Systems. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 10, n. 1, p. 38-48, nov. 2014. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1562>. Date accessed: 26 sep. 2020. doi: https://doi.org/10.15837/ijccc.2015.1.1562.

Keywords

interpolative control algorithms, intelligent optimization, design environment, mechatronic positioning system