This paper concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation and reduce the chattering phenomenon introduced by sliding mode control. So first, we conceived a sliding mode controller of the induction motor. A new approach is applied to the cascade structure is presented. For this purpose, a new decoupled and reduced model is first proposed. Then, a set of simple surfaces and associated control laws are synthesized. However, as the magnitude of the piecewise smooth function depends closely on the upper bound of uncertainties, which include parameter variations and external disturbances, we propose a new form of this piecewise smooth control function with a threshold which ensure a significant reduction of the chattering but could not eliminate it. To overcome such a limitation of this control, adaptive neuro fuzzy inference controllers (ANFIS) are designed. Simulation results reveal some very interesting features.
Fuzzy logic, neural network, fuzzy-neural networks play an important role in the linguistic modeling of intelligent control and decision making in complex systems. The Fuzzy-Neural Network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes an Artificial Immune Algorithm (AIA) based optimal learning fuzzy-neural network (IM-FNN). The proposed learning scheme includes the discovery of the fuzzy-neural network structure which can handle linguistic knowledge and the tuning of the membership function of the fuzzy inference system is achieved by AIA. The learning algorithm of the IM-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, immune algorithm is used for tuning the membership functions of the proposed model. This paper also suggests techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine using immune algorithm.