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State estimation for nonlinear systems by using dynamic optimization

The information about the states of the dynamic system is crucial for the successful implementation of model-based dynamic optimization and nonlinear model predictive control strategies. For large-scale nonlinear systems, the moving horizon estimation and globally iterated extended Kalman filtering may not be appropriate for applications with short sampling periods because of the time consuming computation. In addition, the modelling error and the unmeasured disturbances should be considered by the feedback of the real-time measurements. In this paper, a new approach is proposed in which the computation time and the use of real-time measurements are taken into account. The independent variables are reconciled by dynamic optimization, and the state variables are obtained by one-step simulation under the reconciled independent variables. The approach is verified on a pilot heat-integrated distillation column system. The results show that the proposed approach is effective for the real-time state estimation of nonlinear large-scale systems.