Robust model-based control of open-loop unstable processes

This paper addresses the development of new formulations for estimating mod eling errors or unmeasured disturbances to be used in Model Predictive Cont rol (MPC) algorithms during open-loop prediction. Two different formulation s were developed in this paper. One is used in MPC that directly utilizes l inear models and the other in MPC that utilizes non-linear models. These es timation techniques were utilized to provide robust performance for MPC alg orithms when the plant is open-loop unstable and under the influence of mod eling error and/or unmeasured disturbances. For MPC that utilizes a non-lin ear model, the estimation technique is formulated as a fixed small size on- line optimization problem, while for linear MPC, the unmeasured disturbance s are estimated via a proposed linear disturbance model. The disturbance mo del coefficients are identified on-line from historical estimates of plant- model mismatch. The effectiveness of incorporating these proposed estimatio n techniques into MPC is tested through simulated implementation on non-lin ear unstable exothermic fluidized bed reactor. Closed-loop simulations prov ed the capability of the proposed estimation methods to stabilize and, ther eby, improve the MPC performance in such cases.