Leveraging Prior Knowledge for Performance Improvement in Control, Estimation, and Identification

摘要

In many practical engineering applications, a significant portion of the available information is excluded from the design process due to a lack of obvious mechanisms for its incorporation. In this dissertation, several methods are presented for leveraging such underutilized prior knowledge in application-oriented settings. Three cases, motivated by real-world examples, are considered, addressing controller, estimator, and identification design respectively. In each case, a methodology is presented capturing the key features of the prior knowledge in a characterization which can be readily incorporated into standard solution.;Firstly, a flexible modeling framework is presented for characterizing time-advanced forecast data associated with an exogenous disturbance. The model is incorporated into a disturbance-attenuating feedforward controller which can be synthesized with standard H2 or Hinfinity methods. The closed-loop performance calculation provides a comparative metric to juxtapose multiple designs and address economic questions, such as sensor placement. A practical example is provided for a wind turbine and lidar sensor with tunable focus range.;Secondly, a modeling framework is presented for characterizing logic-valued measurements that provide timely indication of an associated disturbance event. An estimator is constructed using the fast logic-valued measurement, and known disturbance statistics, to rapidly adjust the disturbance estimate, resulting in improved performance. The framework is applied to a gas turbine (GT) system with transient load disturbance associated with a fast electrical breaker switching measurement. The method is generalized to incorporate multiple disturbance load and breaker pairs.;Finally, a high-fidelity GT (HFGT) model is used to construct a linear GT engine model for control design. The HFGT model generates closed-loop transient simulation data for system identification and the structure of its internal subsystems is leveraged to reduce the complexity of the identification process by excluding unnecessary subsystems. The partition of subsystems is enabled by access to signals in the high-fidelity model which are otherwise unavailable during physical engine testing. The resulting linear engine model can be modularly reconfigured with different fuel subsystem and rotor subsystem models. The linear GT model is validated in closed-loop transient simulations.