Distributed multiparametric model predictive control design for a quadruple tank process

Distributed control for plant wide control has received attention lately. In this paper, multiparametric quadratic programming based controllers have been developed for a benchmark quadruple tank problem. A centralized control strategy is developed by partitioning its six dimensional vector space based on constraint satisfaction, stability and optimality. Control design is simplified using its decentralized version after a relative gain array analysis of the benchmark. A cooperative game theory based distributed model predictive controller and decentralized proportional integral (PI) controller are also designed for the same system. A decoupling based cooperative distributed multiparametric model predictive controller (mpMPC) is proposed. The controllers are subjected to reference tracking and disturbance rejection and the performance measures are compared. Also, the robustness of cooperative mpMPC to parameter uncertainties is discussed. Distributed design approach is a natural fit for the vector space partitioning based mpMPC design. Simulations results are analyzed and the performance of the five controllers is discussed.