Adaptive Covariance Kalman Filtering and Nonlinear Decoupling Control via Feedback Linearization for a Three-Tank Process

Abstract

Hydraulic three-tank systems are widely used in water treatment and liquid storage applications, where accurate level regulation is essential for safe and efficient operation. This paper investigates linear and nonlinear control strategies for reference tracking in a three-tank process. A linear state-feedback controller with integral action is first designed based on a linearized model, followed by a nonlinear decoupling controller using feedback linearization. In addition, an adaptive covariance Kalman filter (AKF) is employed for state estimation by dynamically updating the process-noise covariance matrix. Numerical simulations demonstrate that both control approaches achieve satisfactory reference tracking, while the proposed AKF provides accurate state estimation and effectively captures the nonlinear system behavior. The results highlight the effectiveness of combining nonlinear control and adaptive state estimation for hydraulic process systems.