Robust predictive control of coupled water tank plant

Liquid level control of the liquid tank is a basic control problem in the process industry. The liquid level control system usually has the characteristics of strong coupling, nonlinearity and large lag. Up to now, PID control is the main method of liquid level control in industry, but it is difficult to obtain high precision control performance. The difficulty of model-based liquid level control such as Robust Model Predictive Control (RMPC) is hard to obtain an accurate model of the plant or need accurate steady-state information which is hard to be obtained in practice. This paper provides a systematic modeling and RMPC method to implement a Coupled Water Tank (CWT) plant output-tracking control in the case without whole steady-state information of the system. First, a data-driven modeling technique based on the Radial Basis Function (RBF) net-type coefficients Multi-Input/Multi-Output (MIMO) AutoRegressive model with eXogenous variable (ARX) is applied to build the RBF-ARX model of the CWT plant. Using the model, a locally linearized model and a polytopic uncertain linear parameter varying (LPV) model are constructed to represent the current and future behavior of the nonlinear CWT system. Based on the two models, an RMPC method is designed to implement the system’s output tracking control under the condition without whole steady-state knowledge of the CWT plant. The stability of the RMPC strategy is guaranteed by using the time-varying parameter-dependent Lyapunov function and feasibility of the Linear Matrix Inequalities (LMIs). Real-time control experiments and comparison with other control methods are carried out on the CWT system, and the results show that the presented method is superior.