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Adaptive Fuzzy Neural Control for Robotic Arm

M. N. Kakatkar

Abstract


Many systems are nonlinear in real life. Nowadays, neural networks are widely used for control of nonlinear control systems. The most important property of Neural Network is the capability to solve the nonlinear functions to the required accuracy. The other advantages are their adaptive nature and parallel processing capability. In control engineering, objects are mostly undefined, so that Fuzzy control technique will be useful. The adaptive neural fuzzy inference system (ANFIS), a proper blend of neural control and fuzzy logic has achieved remarkable success in nonlinear control systems. Proper combination of both methods extracts the advantages of both techniques, i.e., self-learning nature of neural network and adaptability of fuzzy systems can form a better performance than simple neural network or fuzzy logic.

 


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References


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DOI: https://doi.org/10.37628/jvdt.v2i2.1447

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