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Incremental state model in pbkp_redictive control: A new fuzzy control proposal for nonlinear systems

Abstract:

In this work, a fuzzy pbkp_redictive optimal control for multivariable nonlinear systems with pure time delays is presented. Therefore, dynamic local linear state models are used at each point of the state space obtained by fuzzy Takagi-Sugeno (T-S) modelling. The modelling error is considered as white noise, and the state is observed using the Kalman Filter (KF). This method does not take into account possible input constraints. Therefore, it applies to systems where there are no saturation problems. In this work, a new approach in the Model Pbkp_redictive Control (MPC) method is proposed by calculating the control signal increment as a function of the error between a reference state vector and the pbkp_rediction at N-steps of the state vector instead of using the traditional MPC approach which is based on calculating the error between a reference and the pbkp_redicted output. The proposed method has a satisfactory tracking performance. The main feature of the proposed method is that it attains computational savings compared to other methods that have used incremental state models, which making it more appropriate for real-time applications.