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

Overview & Synopsis

In this work, a fuzzy predictive optimal control for multivariable nonlinear systems withpure time delays is presented. Therefore, dynamic local linear state models are used at eachpoint of the state space obtained by fuzzy Takagi-Sugeno (T-S) modelling. The modellingerror 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 tosystems where there are no saturation problems. In this work, a new approach in theModelPredictive Control (MPC) method is proposed by calculating the control signal incrementas a function of the error between a reference state vector and the prediction at N-stepsof the state vector instead of using the traditional MPC approach which is based on calculatingthe error between a reference and the predicted output. The proposed method hasa satisfactory tracking performance. The main feature of the proposed method is that itattains computational savings compared to other methods that have used incremental statemodels, which making it more appropriate for real-time applications.