Online Adaptive Fuzzy Neural Identification and Control of Nonlinear Dynamic Systems

  • Meng Joo Er
  • Yang Gao
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 116)


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.


Membership Function Fuzzy Rule Inverse Dynamic Gaussian Membership Function Fuzzy Control Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Meng Joo Er
    • 1
  • Yang Gao
    • 1
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversityNanyang AvenueSingapore

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