Abstract
This chapter presents a robust Adaptive Fuzzy Neural Controller (AFN C) suitable for identification and control of uncertain Multi-Input-Multi-Output (MIMO) nonlinear systems. The proposed controller has the following salient features: (1) Self-organizing fuzzy neural structure, i.e. fuzzy control rules can be generated or deleted automatically; (2) Online learning ability of uncertain MIMO nonlinear systems; (3) Fast learning speed; (4) Fast convergence of tracking errors; (5) Adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; (6) Robust control, where global stability of the system is established using the Lyapunov approach. Two simulation examples are used to demonstrate excellent performance of the proposed controller.
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Er, M.J., Gao, Y. (2003). Online Adaptive Fuzzy Neural Identification and Control of Nonlinear Dynamic Systems. In: Zhou, C., Maravall, D., Ruan, D. (eds) Autonomous Robotic Systems. Studies in Fuzziness and Soft Computing, vol 116. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1767-6_14
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DOI: https://doi.org/10.1007/978-3-7908-1767-6_14
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2523-7
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