A neuro fuzzy sliding mode controller for load frequency problem in deregulated environment for multi area power system

2016 ◽  
Author(s):  
A. Ganga Dinesh Kumar ◽  
N.V. Ramana
Keyword(s):  
Power System ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Load Frequency ◽  
Neuro Fuzzy ◽  
Fuzzy Sliding Mode

Adaptive Neuro-Fuzzy Sliding Mode Controller

2018 ◽  
Vol 7 (2) ◽  
pp. 34-54 ◽  
Author(s):  
Sana Bouzaida ◽  
Anis Sakly
Keyword(s):  
Sliding Mode ◽  
Benchmark Problems ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Adaptive Sliding Mode Controller ◽  
Neuro Fuzzy ◽  
Fuzzy Sliding Mode ◽  
Fuzzy Network ◽  
Online Tuning

A novel adaptive sliding mode controller using neuro-fuzzy network based on adaptive cooperative particle sub-swarm optimization (ACPSSO) is presented in this article for nonlinear systems control. The proposed scheme combines the advantages of adaptive control, neuro-fuzzy control, and sliding mode control (SMC) strategies without system model information. An adaptive training algorithm based on cooperative particle sub-swarm optimization is used for the online tuning of the controller parameters to deal with system uncertainties and disturbances. The algorithm was derived in the sense of Lyapunov stability analysis in order to guarantee the high quality of the controlled system. The performance of the proposed algorithm is evaluated against two well-known benchmark problems and simulation results that illustrate the effectiveness of the proposed controller.

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