CONSTRAINED STATE ESTIMATOR FOR NONLINEAR DISCRETE-TIME DYNAMICAL SYSTEMS WITH UNCERTAIN PARAMETERS

This paper deals with the problem of state estimation for nonlinear stochastic discrete-time dynamical systems with state constraints and uncertain parameters. The nonlinear system and the output measurements are assumed to be corrupted by zero mean white Gaussian noise. Moreover, the system parameters are assumed to be uncertain and the states of the system are assumed to be subject to nonlinear constraints due to physical or safety considerations. The proposed estimator is based on the extended Kalman filter, the active set method, and the multiple projection approach. The proposed estimator is different from other estimators in which a recursive approach is used to satisfy the violated constraints of nonlinear uncertain systems. To illustrate the effectiveness and simplicity of the proposed estimator, a numerical example and a fed-batch penicillin fermentation process are presented. Simulation results show that the proposed estimator leads to a good state estimator while satisfying the state constraints.