Abstract
Classical control tools often encounter a number of limitations on the investigation of smart composite structures due to nonlinearities and/or other uncertainties. Especially in smart structures, which is the case here, a significant degree of uncertainty is involved due to several imperfections and/or errors of both the controller and the structure itself. For example, in structures with multiple layers, several failures may appear, such as delamination, debonding, fatigue, etc. The use of intelligent fuzzy and adaptive control which is based on neuro-fuzzy techniques can be very helpful in this direction. One may also consider using global optimization algorithms for the fine-tuning of the characteristics of the controllers to maximize their applicability, their efficiency, and their robustness. In other words, the controllers can be designed based on intuition and basic engineering principles, and then they can be subjected to optimization, e.g., to training/learning using artificial neural networks, in order to achieve certain properties.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Driankov, D., Hellendoorn, H., Reinfrak, M.: An introduction to fuzzy control, 2nd edn. Springer, Munchen (1996)
Sugeno, M.: An introductory survey of fuzzy control. Inf. Sci. 36, 59–83 (1985)
Precur, R.-E., Hellendoorn, H.: A survey on industrial applications of fuzzy control. Comput. Ind. 62, 213–226 (2011)
Azadegan, A., Porobic, L., Ghazinoory, S., Samouei, P., Kheirkhah, A.-S.: Fuzzy logic in manufacturing: a review of literature and a specialized application. Int. J. Prod. Econ. 132, 258–270 (2011)
Lu, P., Chen, S., Zheng, Y.: Artificial intelligence in civil engineering. Math. Probl. Eng. 2012, Article ID 145974, 22 pp (2012)
Kar, S., Das, S., Ghosh, P.K.: Applications of neuro fuzzy systems: a brief review and future outline. Appl. Soft Comput. 15, 243–259 (2014)
Tairidis, G., Foutsitzi, G., Koutsianitis, P., Stavroulakis, G.E.: Fine tunning of a fuzzy controller for vibration suppression of smart plates using genetic algorithms. Adv. Eng. Softw. 101, 123–135 (2016)
Tairidis, G.K., Foutsitzi, G., Koutsianitis, P., Stavroulakis, G.E.: Fine tuning of fuzzy controllers for vibration suppression of smart plates using particle swarm optimization. In: 8th GRACM International Congress on Computational Mechanics Proceedings, Volos, 12–15 July (2015)
Tairidis, G: Optimal design of smart structures with intelligent control. Ph.D. thesis, Technical University of Crete, Greece.
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man. Mach. Stud. 7, 1–13 (1975)
Marinaki, M., Marinakis, Y., Stavroulakis, G.E.: Fuzzy control optimized by a multi-objective particle swarm optimization algorithm for vibration suppression of smart structures. Struct. Multidisc. Optim. 43, 29–42 (2011)
Marinaki, M., Marinakis, Y., Stavroulakis, G.E.: Fuzzy control optimized by a multi-objective differential evolution algorithm for vibration suppression of smart structures. Comput. Struct. 147, 126–137 (2015)
Mayhan, P., Washington, G.: Fuzzy model reference learning control: a new control paradigm for smart structures. Smart Mater. Struct. 7, 874–884 (1998)
Sharma, M., Singh, S.P., Sachdeva, B.L.: Fuzzy logic based modal space control of a cantilevered beam instrumented with piezoelectric patches. Smart Mater. Struct. 14, 1017–1024 (2005)
Muradova, A.D., Stavroulakis, G.E.: Fuzzy vibration control of a smart plate. Int. J. Comput. Meth. Eng. Sci. Mech. 14, 212–220 (2013)
Ding, J., Sun, X., Zhang, L., Xie, J.: Optimization of fuzzy control for magnetorheological damping structures. Shock. Vib. 2017, Article ID 4341025, 14 pp (2017)
Baygi, S.M.H., Karsaz, A., Elahi, A.: A hybrid optimal PID-fuzzy control design for seismic exited structural system against earthquake: a salp swarm algorithm. In: 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp. 220–225, Kerman (2018)
Singh, J., Singh, N., Sharma J.K.: Fuzzy modeling and control of HVAC systems. A review. J. Sci. Ind. Res. 65, 470–476 (2006)
Dounis, A.I., Caraiscos C.: Advanced control systems engineering for energy and comfort management in a building environment. A review. Renew. Sustain. Energy Rev. 13, 1246–1261 (2009)
Bascetta, L., Rocco, P., Zanchettin, A.M., Magnani, G.: Velocity control of a washing machine: a mechatronic approach. Mechatronics 22, 778–787 (2012)
Shaikh, P.-H., Nor, N.B.M., Nallagownden, P., Elamvazuthi, I., Ibrahim, T.: A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renew. Sustain. Energy Rev. 34, 409–429 (2014)
Suganthi, L., Iniyan, S., Samuel, A.A.: Applications of fuzzy logic in renewable energy systems. A review. Renew. Sustain. Energy Rev. 48, 585–607 (2015)
Haruki, T., Kikuchi, K.: Video camera system using fuzzy logic. IEEE Trans. Consum. Electron. 38, 624–634 (1992)
Kyriakarakos, G., Dounis, A.I., Arvanitis, K.G., Papadakis, G.: A fuzzy logic energy management system for polygeneration microgrids. Renew. Energy 41, 315–327 (2012)
Kyriakarakos, G., Dounis, A.I., Arvanitis, K.G., Papadakis, G.: Design of a fuzzy cognitive maps variable-load energy management system for autonomous PV-reverse osmosis desalination systems: a simulation survey. Appl. Energy 187, 575–584 (2017)
Sawyer, J.P., Rao, S.S.: Structural damage detection and identification using fuzzy logic. AIAA J. 38, 2328–2335 (2000)
Stavroulakis, G.E.: Inverse and crack identification problem in engineering mechanics. Kluwer Academic Publishers-Springer, Dordrecht, Boston, London (2000)
Jena, P.K., Thatoi, D.N., Parhi, D.R.: Dynamically self-adaptive fuzzy PSO technique for smart diagnosis of transverse crack. Appl. Artif. Intell. 29, 211–232 (2015)
Latha, B., Senthilkumar, V.S.: Fuzzy rule based modeling of drilling parameters for delamination in drilling GFRP composites. J. Reinf. Plast. Compos. 28, 951–964 (2009)
Ivanov, V.: A review of fuzzy methods in automotive engineering applications. Eur. Transp. Res. Rev. 7, 29 (10 pp) (2015)
Chen, C.K., Dao, T.K.: Speed-adaptive roll-angle-tracking control of an unmanned bicycle using fuzzy logic. Veh. Syst. Dyn. 48, 133–147 (2010)
Gupta, S.G., Ghonge, M.M., Jawandhiya, P.M.: Review of unmanned aircraft system (UAS). Int. J. Adv. Res. Comput. Eng. Technol. 2, 1646–1658 (2013)
Kosari, A., Jahanshahi, H., Razavi, S.A.: An optimal fuzzy PID control approach for docking maneuver of two spacecraft: orientational motion. Eng. Sci. Technol. Int. J. 20, 293–309 (2017)
Ivancevic, V.G., Ivancevic, T.T.: Brain and classical neural networks. In: Quantum Neural Computation. Intelligent Systems, Control and Automation: Science and Engineering, vol. 40. Springer, Dordrecht (2010)
Lodish, H., Berk, A., Zipursky, S.L., Matsudaira, P., Baltimore, D., Darnell, J.: Molecular cell biology, 4th edn. W. H. Freeman, New York (2000)
McCulloch, W.S., Pitts, W.H.: A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1942)
Rosenblatt, F.: The perceptron—a perceiving and recognizing automaton. Cornell Aeronautical Laboratory (1957)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 32, 533–536 (1986)
Wang, L.X.: Adaptive fuzzy systems and control: design and stability analysis. Prentice Hall, Upper Saddle River (1994)
Jang, J.-S.R.: Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In: Ninth National Conference on Artificial Intelligence (1991)
Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)
Jang, J.-S.R., Sun, C.-T.: Neuro-fuzzy modeling and control. Proc. IEEE 83, 378–406 (1995)
Widrow, B., Lehr, M.A.: 30 years of adaptive neural networks: perceptron, madline, and backpropagation. Proc. IEEE 7, 1415–1442 (1990)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River (1997)
Jang, J.S.R.: Input selection for ANFIS learning. In: Proceedings of IEEE 5th International Fuzzy Systems, New Orleans, LA, vol. 2, pp. 1493–1499 (1996)
Chiu, S.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2, 267–278 (1994)
Tairidis, G.K., Stavroulakis, G.E., Marinova D.G., Zacharenakis E.C.: Classical and soft robust active control of smart beams. In: Papadrakaikis, M., Charmpis, D.C., Tsompanakis, Y., Lagaros, N.D. (eds.) Computational Structural Dynamics and Earthquake Engineering, pp. 165–177. CRC Press, London (2009)
Tairidis, G.K., Papachristou, I., Katagas, M., Stavroulakis, G.E.: Neuro—fuzzy control of smart structures. In: 10th HSTAM International Congress on Mechanics Proceedings, Chania, 25–27 May (2013)
Foutsitzi, G., Marinova, D., Hadjigeorgiou, E., Stavroulakis, G. E.: Finite element modelling of optimally controlled smart beams. In: 28th Summer School: Applications of Mathematics in Engineering and Economics. Sozopol, Bulgaria (2002)
Stavroulakis, G.E., Foutsitzi, G., Hadjigeorgiou, V., Marinova, D.G., Baniotopoulos, C.C.: Design and robust optimal control of smart beams with application on vibrations suppression. Adv. Eng. Softw. 36, 806–813 (2005)
Stavroulakis, G., Papachristou, I., Salonikidis, S., Papalaios, I., Tairidis G.: Neurofuzzy control for smart structures. In: Tsompanakis, Y., Topping, B.H.V (eds.) Soft Computing Methods for Civil and Structural Engineering, pp. 149–172, Saxe-Coburg, Stirlingshire, UK (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Tairidis, G.K., Stavroulakis, G.E. (2019). Fuzzy and Neuro-fuzzy Control for Smart Structures. In: Blondin, M., Pardalos, P., Sanchis Sáez, J. (eds) Computational Intelligence and Optimization Methods for Control Engineering. Springer Optimization and Its Applications, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-030-25446-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-25446-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-25445-2
Online ISBN: 978-3-030-25446-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)