Model Predictive Control Tuning Strategy for Non-Square Systems and Range Controlled Variables Based on Multi-Scenarios Approach

Abstract

A novel strategy for model predictive control tuning considering nonsquare systems, with more outputs than inputs, and set points or ranges for controlled variables, is introduced. This methodology is applicable for any predictive control algorithm, since it has adjustable parameters according to the implementation, and is divided in three parts that consists of, initially, the determination of the best attainable closed-loop performance function for the multiscenarios, generated from the complete model, including performance and robustness metrics as additional constraints. Based on the attainable closed-loop function, the second step is the calculation of the optimal scaling for the process and, finally, the controller weights are tuned leading the process to the best operating condition. This constrained strategy was applied in the controller design of the Shell heavy oil fractionator benchmark showing good results for zone region tracking.