Observer Switching Strategy for Enhanced State Estimation in CSTR Networks

Accurate state estimation is essential for monitoring and controlling nonlinear chemical reactors, such as continuous stirred-tank reactors (CSTRs), where limited sensor coverage and process uncertainties hinder real-time observability. This paper introduces a novel multi-observer switching framework that combines several advanced estimators: Extended Luenberger Observer (ELO), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Quadrature Kalman Filter (QKF), and Particle Filter (PF), operating in parallel. At each sampling instant, a cost function based on the $L_1$ norm and Kullback-Leibler divergence selects the observer, yielding the best agreement with available measurements. The proposed architecture is validated through simulations of both linearized and fully nonlinear CSTR models with up to three reactors in series. Results show that the switching strategy significantly reduces estimation errors compared to single-observer approaches, especially under partial observability and parametric uncertainty. Despite its modular design, the framework remains computationally tractable, making it suitable for real-time industrial applications such as fault detection and model-based predictive control.