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

In this work, a fuzzy predictive 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 theModel
Predictive Control (MPC) method is proposed by calculating the control signal increment
as a function of the error between a reference state vector and the prediction 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 predicted 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.