Abstract
We describe in this paper the application of soft computing techniques to controlling non-linear dynamical systems in real-world problems. Soft computing consists of fuzzy logic, neural networks, evolutionary computation, and chaos theory. Controlling real-world non-linear dynamical systems may require the use of several soft computing techniques to achieve the desired performance in practice. For this reason, several hybrid intelligent architectures have been developed. The basic idea of these hybrid architectures is to combine the advantages of each of the techniques involved in the intelligent system. Also, non-linear dynamical systems are difficult to control due to the unstable and even chaotic behaviors that may occur in these systems. The described applications include manufacturing, robotics, adaptive filters, battery charging and reactors.
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© 2002 Springer-Verlag London
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Castillo, O. (2002). Soft Computing for Control of Dynamical Systems. 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_8
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DOI: https://doi.org/10.1007/978-1-4471-0123-9_8
Publisher Name: Springer, London
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