Guaranteed-cost control of the nonlinear benchmark problem using model-based fuzzy systems

We design a state-feedback controller for the nonlinear benchmark problem. Our approach relies on the use of Takagi-Sugeno fuzzy models to approximate the nonlinear system. Once the fuzzy model is obtained, we develop a guaranteed-cost framework to design the controller using linear matrix inequality methods and the recently obtained relaxed stability conditions. We show that our proposed controller will not only stabilize the system, but also has satisfactory disturbance attenuation properties.

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