Robust model-based control of open-loop unstable processes

Abstract EN

This paper addresses the development of new formulations for estimating modeling errors or unmeasured disturbances to be used in Model Predictive Control (MPC) algorithms during open-loop prediction.

Two different formulations were developed in this paper.

One is used in MPC that directly utilizes linear models and the other in MPC that utilizes non-linear models.

These estimation techniques were utilized to provide robust performance for MPC algorithms when the plant is open-loop unstable and under the influence of modeling error and/or unmeasured disturbances.

For MPC that utilizes a non-linear model, the estimation technique is formulated as a fixed small size on-line optimization problem, while for linear MPC, the unmeasured disturbances are estimated via a proposed linear disturbance model.

The disturbance model coefficients are identified on-line from historical estimates of plant-model mismatch.

The effectiveness of incorporating these proposed estimation techniques into MPC is tested through simulated implementation on non-linear unstable exothermic fluidized bed reactor.

Closed-loop simulations proved the capability of the proposed estimation methods to stabilize and, thereby, improve the MPC performance in such cases.