Robust inferential control: A methodology for control structure selection and inferential control system design in the presence of model/plant mismatch.

摘要

Two major tasks that are required to obtain a control system utilizing secondary measurements are measurement selection and inferential control system design. The important issues to be addressed are not only the theoretical performance of the closed-loop system, but also the effects arising from the factors prevalent in practical environments such as model/plant mismatch, constraints, and failures of actuators and sensors.; General measurement selection methodology is developed accounting for all the factors that can affect the measurement selection in significant ways. These factors include model uncertainty, signal-to-noise ratios, and measurement dynamics. Conditions are derived under which some of the new criteria reduce to previously published measurement selection criteria. The proposed tools are applied to the measurement selection problems in a multi-component distillation column and a high-purity distillation column.; Design of the output estimator was examined for two different cases: the case where a full dynamic model is available and the case where only the time records of the primary and secondary measurements are available either from simulations or from process measurements.; For the latter approach, general state estimation techniques (e.g., multi-rate Kalman filtering) used in LQG and finite receding horizon control used in traditional MPC were integrated into a control technique that can incorporate general disturbances and multi-rate sampled measurements and has desirable operational characteristics. (Abstract shortened with permission of author.)