A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Performance Seeking Control <s> The communication throughput and data-processing capacities of integrated flight/propulsion control systems allow engine operating schedules to be adjusted in-flight, on the basis of adaptive optimization algorithms which identify engine component performance variations due to manufacturing, wear, and damage. A quantification is presently made of the performance benefits accruing to adaptive in-flight optimization, via comparisons of fuel consumption and turbine temperature data for variable geometry and component match optimized cases with conventional cases. A low-bypass mixed-flow turbofan and a high-bypass nonmixed turbofan are thus treated. 6 refs. <s> BIB001 </s> A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Performance Seeking Control <s> A model-based control system for an advanced gas turbine engine is described, and some preliminary performance benefits are derived based on simulation testing. The approach to plant model development and a method for adapting a nominal engine model to reflect the performance of a deteriorated engine is given. A general process for model-based control law design and validation, with emphasis on linear and nonlinear analysis, is then presented. Performance and Operability modes that implement closed loop control of model-derived parameters are demonstrated via simulation testing. Future plans include testing of the model-based control described in this paper on an IHPTET Phase II advanced technology engine. NOMENCLATURE <s> BIB002 </s> A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Performance Seeking Control <s> Abstract : A key to producing reliable engine diagnostics and prognostics resides in the fusion of different processing techniques. Fusion of techniques has been shown to improve diagnostic performance while simultaneously reducing false alarms. Presented here is an approach that fuses a physical model called STORM (Self Tuning Onboard, Real-time engine Model) developed by Pratt & Whitney, with an empirical neural net model to provide a unique hybrid model called enhanced STORM (eSTORM) for engine diagnostics. STORM is a piecewise linear approximation of the engine cycle deck. Though STORM provides significant improvement over existing real-time engine model methods, there are several effects that impact engine performance that STORM does not capture. Integrating an empirical model with STORM accommodates the modeling errors. This paper describes the development of eSTORM for a Pratt & Whitney high bypass turbofan engine. Results of using STORM and eSTORM on simulated engine data are presented and compared. eSTORM is shown to work extremely well in reducing STORM modeling errors and biases for the conditions considered. <s> BIB003
Aircraft engine performance seeking control technology has been advanced under a number of DoD and NASA led programs. BIB001   BIB002 The goal of performance seeking control is to operate the engine in a manner to achieve optimal performance based upon the current condition of the engine and the current mission of the aircraft. Optimal performance is typically defined in terms of fuel burn, thrust, engine life, or a combination of these objectives. A comparison between a conventional aircraft engine control architecture and a performance seeking control architecture is shown in figure 7 . In both cases the engine control system is responsible for providing the desired level of thrust while maintaining the necessary operability margins at steady-state and transient operating conditions throughout the engine operating envelope. Since engine parameters such as thrust and stall margin are not directly measurable, the conventional control design approach is to infer these parameters through other direct sensor measurements such as rotor speeds and pressure ratios. Complicating this process is the fact that slight performance variations always exist between engines due to engine-to-engine manufacturing variations. Furthermore, each individual engine will naturally undergo degradation over its lifetime of use. To account for these variations the conventional control system must be designed to ensure robust operation for a range of engine conditions from fully healthy to fully degraded. However, this robustness is obtained in exchange for performance. Performance seeking control aims to address some of the shortcomings of conventional control logic by directly controlling the parameters of interest, and optimizing engine operation based upon the current condition of the engine. This is achieved by using a real-time on-board aerothermodynamic engine model incorporated into the engine control architecture as shown in figure 7 . An associated on-line parameter estimation algorithm, or tracking filter, adjusts model tuner parameters to match the performance of the physical engine. Linear estimation techniques, such as Kalman filters, are often used to implement the tracking filters. Once the on-board model is accurately tuned it provides accurate estimates of sensed engine outputs as well as estimates of unmeasurable engine parameters such as stall margin and thrust which can be used for direct feedback control purposes. By adapting to account for engine variations and controlling directly on the parameters of interest, the engine control can be optimized to provide enhanced performance while still providing the necessary degree of robustness. In the past, performance seeking control technology has been evaluated in engine ground-based tests and flight test environments demonstrating the benefits of the technology. It should be noted that any model-based performance seeking control technology is only as accurate as its corresponding on-board model. Therefore any advances in on-board modeling or tracking filter technology will directly enhance the capabilities of such modelbased control approaches. BIB003 Additional advances are required in terms of control logic verification, validation and certification. Any adaptive control technology must adequately address these issues to ultimately transition into service. The on-board model estimation and tracking technology is not unlike the estimation techniques used for ground-based performance diagnostics. In addition to the control functionality that it provides, an added benefit of having a real-time on-board performance model is that it supplies real-time continuous trending of engine component health and can be invaluable for fault detection and isolation purposes.