Abstract
We describe in this paper, different hybrid approaches for controlling dynamical systems in electrochemical applications. The hybrid approaches combine soft computing techniques and mathematical models to achieve the goal of controlling the electrochemical process to follow a desired production plan. We have developed several hybrid architectures that combine fuzzy logic, neural networks, and genetic algorithms, to compare the performance of each of these combinations and decide on the best one for our purpose. Electrochemical processes, like the ones used in battery charging, are very complex and for this reason very difficult to control. We have achieved very good results using fuzzy logic for control, neural networks for model ling the process, and genetic algorithms for tuning the hybrid intelligent system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bode, H., Brodd, R.J. & Kordesch, K.V. (1977). Lead-Acid Batteries, John Wiley & Sons.
Castillo, O. & Melin, P. (1998). “A New Fuzzy-Fractal-Genetic Method for Automated Mathematical Modelling and Simulation of Robotic Dynamic Systems, Proceedings of IEEE WCCI’98 Congress, Vol. 2 pp. 1182–1187.
Castillo, O. & Melin, P. (1999). “A New Fuzzy Inference System for Reasoning with Multiple Differential Equations for Modelling Complex Dynamical Systems”, CIMCA”99, IOS Press, Vienna Austria, pp.224–229.
Castillo, O. & Melin, P. (1999). “Intelligent Model-Based Adaptive Control of Robotic Dynamic Systems with a New Neuro-Fuzzy-Genetic Approach”, Proceedings of Robotics and Applications, Acta Press, USA. pp. 270–275.
Hehner, N. & Orsino, J.A. (1985). Storage Battery Manufacturing Manual III, Independent Battery Manufacturers Association.
Jang, J.R., Sun, C.T. & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing, Prentice Hall.
Melin, P. & Castillo, O. (1998). “An Adaptive Model-Based Neuro-Fuzzy-Fractal Controller for Biochemical Reactors in the Food Industry”, Proceedings of IJCNN’98, IEEE Press, Anchorage Alaska, USA, Vol. 1, pp. 106–111.
Melin, P. and Castillo, O. (1998). A New Method for Adaptive Model-Based Neuro-Fuzzy-Fractal Control of Non-Linear Dynamic Plants: The case of Biochemical Reactors, Proceedings of IPMU’98, EDK Publishers, France, Vol. 1, pp. 475–482.
Melin, P. & Castillo, O. (1999). A New Method for Adaptive Model-Based Neuro-Fuzzy-Fractal Control of Non-Linear Dynamical Systems, Proceedings of the International Conference of Non-Linear Problems in Aviation and Aerospace’98, European Conference Publications, Daytona Beach, USA, pp. 499–506
Melin, P. & Castillo, O. (1999). “A New Neuro-Fuzzy-Fractal Approach for Adaptive Model-Based Control of Non-Linear Dynamic Plants”, Proceedings of Intelligent Systems and Control, Santa Barbara, USA. pp. 397–401.
Sepulveda, R., Castillo, O., Montiel, O. & Lopez, M. (1998). “Analysis of Fuzzy Control System for Process of Forming Batteries”, ISRA’98, Mexico, pp. 203–210
Zadeh, L. A. (1975). “The Concept of a Linguistic Variable and its Application to Approximate Reasoning”, Information Sciences, 8, pp. 43–80.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag London
About this chapter
Cite this chapter
Sepulveda, R., Melin, P. (2002). Intelligent Control of a Battery Charging Process with a Hybrid Approach. In: Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds) Soft Computing and Industry. Springer, London. https://doi.org/10.1007/978-1-4471-0123-9_12
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0123-9_12
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1101-6
Online ISBN: 978-1-4471-0123-9
eBook Packages: Springer Book Archive