Stability analysis and robustness design of nonlinear systems: An NN-based approach

In this study a neural-network (NN) based approach is developed which combines H^~ control performance with Tagagi-Sugeno (T-S) fuzzy control for the purpose of stabilization and stability analysis of nonlinear systems. A Takagi-Sugeno (T-S) fuzzy model and parallel-distributed compensation (PDC) scheme are first employed to design a nonlinear fuzzy controller for the stabilization of nonlinear systems. The neural-network model is adopted to overcome the modeling error problems found with nonlinear systems. A novel stability condition based on an NN-based controller design is derived to ensure the stability of the nonlinear system. The control problem can now be reformulated as a linear matrix inequality (LMI) problem. A simulation is provided in order to explore the feasibility of the proposed fuzzy controller design method.

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