Convex Structure-Based Nonlinear State Estimation Using Linear Kalman Filter and Developing an MPC Scheme

In this paper, a novel method for state estimation of nonlinear system is proposed using the linear Kalman Filter (KF) and Takagi-Sugeno (TS) fuzzy modeling technique to extend the estimation potentials of the classic linear KF to the nonlinear dynamics. Incorporating TS fuzzy model in linear KF provides the most accuracy in state estimation compared with Extended Kalman Filter (EKF) which uses a linear approximation of the nonlinear systems. Consequently, the estimation performance is highly improved. Moreover, an MPC-based control strategy based on the proposed estimation algorithm and TS fuzzy modeling is utilized to control the speed of a Electric Vehilcle (EV). Finally, the simulation results are provided to show the superiority of the proposed state estimation method than the conventional ones.