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• W185724507 abstract "Two major tasks that are required to obtain a control system utilizing secondary measurements are measurement selection and inferential control system design. The first involves choosing an appropriate subset of the available measurements and the second involves designing a feedback controller based on the chosen measurements. 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 signifcant ways. These factors include model uncertainty, signal-to-noise ratios, and measurement dynamics. The underlying philosophy is to reduce the number of candidates to a sufficiently low level before going onto detailed analysis by eliminating those candidates for which there does not exist a linear time-invariant controller meeting the required level of robust performance. Based on this philosophy and using the Structured Singular Value theory as a vehicle, a number of numerically efficient screening tools are developed. 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. Two different approaches are considered for inferential control system design: an output estimation based design approach and a state estimation based design approach. The former approach involves independent design of an output estimator and a feedback controller while the latter involves direct one step design although the design can be actually separated into those of a state estimator and of a feedback regulator using the separation principle argument. For the former approach, 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 former case, multi-rate Kalman filter design and μ-Synthesis design are discussed. For the latter case, the estimator design problem is formulated as a regression problem and various regression techniques are evaluated in terms of their suitability to the output estimator design problem. For design of the feedback controller, traditional techniques such as LQG, IMC, and MPC were combined into a control technique that has nice algorithmic properties as well as many operational merits such as straightforward constraint handling and simple, intuitive on-line tuning. A heavy-oil fractionator was used as an example application. 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. The concept of classical IMC was extended to equip the control system with on-line tuning parameters that have direct connections with the speed of the closed-loop responses. Application to a high purity distillation column demonstrates the effectiveness of the control technique in terms of closed-loop performance and operational flexibility." @default.
• W185724507 created "2016-06-24" @default.
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• W185724507 date "1991-01-01" @default.
• W185724507 modified "2023-09-27" @default.
• W185724507 title "Robust Inferential Control: A Methodology for Control Structure Selection and Inferential Control System Design in the Presence of Model/Plant Mismatch" @default.
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