Dual-mode distributed Model Predictive Control of a quadruple-tank system

Abstract

In this paper, a dual-mode distributed Model Predictive Control (MPC) approach is proposed in order to reduce the on-line computational complexity of the distributed optimal control of nonlinear interconnected systems. It consists in using a nonlinear distributed MPC approach when the state variables of the overall system are far from the origin and applying a linear distributed MPC method in a neighborhood of the origin. The nonlinear distributed approach is based on first-principles (nonlinear) models of the interconnected systems dynamics. It includes a sequential linearization of these models and finding distributedly a suboptimal solution of the resulting quadratic programming problem. In order to apply the linear distributed MPC method, it is necessary first to obtain a linearized model of the overall nonlinear system in a neighborhood of the origin. The benefit of the suggested dual-mode distributed MPC approach is the reduced complexity of the on-line computations in comparison to the entirely nonlinear approach when the current overall system state is in a neighborhood of the origin. The proposed method is illustrated with simulations on the model of a quadruple-tank system