A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Intelligent Control <s> The Pilot's Associate program, a joint effort of the Defense Advanced Research Projects Agency (DARPA) and the US Air Force to build a cooperative, knowledge-based system to help pilots make decisions is described, and the lessons learned are examined. The Pilot's Associate concept developed as a set of cooperating, knowledge-based subsystems: two assessor and two planning subsystems, and a pilot interface. The two assessors, situation assessment and system status, determine the state of the outside world and the aircraft systems, respectively. The two planners, tactics planner and mission planner, react to the dynamic environment by responding to immediate threats and their effects on the prebriefed mission plan. The pilot-vehicle interface subsystem provides the critical connection between the pilot and the rest of the system. The focus is on the air-to-air subsystems. > <s> BIB001 </s> A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Intelligent Control <s> This paper presents an application of the recently developed theory of optimal discrete event supervisory (DES) control that is based on a signed real measure of regular languages. The DES control techniques are validated on an aircraft gas turbine engine simulation test bed. The test bed is implemented on a networked computer system in which two computers operate in the client-server mode. Several DES controllers have been tested for engine performance and reliability. <s> BIB002 </s> A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems <s> Intelligent Control <s> ABSTRACT NASA and the U.S. Department of Defense are conducting programs which support the future vision of “intelligent” aircraft engines for enhancing the affordability, performance, operability, safety, and reliability of aircraft propulsion systems. Intelligent engines will have advanced control and health management capabilities enabling these engines to be self-diagnostic, self-prognostic, and adaptive to optimize performance based upon the current condition of the engine or the current mission of the vehicle. Sensors are a critical technology necessary to enable the intelligent engine vision as they are relied upon to accurately collect the data required for engine control and health management. This paper reviews the anticipated sensor requirements to support the future vision of intelligent engines from a control and health management perspective. Propulsion control and health management technologies are discussed in the broad areas of active component controls, propulsion health management and distributed controls. In each of these three areas individual technologies will be described, input parameters necessary for control feedback or health management will be discussed, and sensor performance specifications for measuring these parameters will be summarized. <s> BIB003
While these traditional control and diagnostic techniques are time-tested and reliable, modern control techniques promise to provide improved control and therefore improved aircraft propulsion system performance. There is little on-board today that could be considered "intelligent." Even with all of the advances in modeling and algorithms, implemented control and health management schemes still maintain safety margins in terms of worst-case stack up of uncertainties; they still consider life usage in terms of idealized flight cycles; and there is essentially no accommodation beyond simple predetermined responses to specific detected faults. The engines of today are reliable and safe, but they are expensive to operate and maintain. With the integration of available models and algorithms for on-board operation and the resultant increase in on-board intelligence, engines of the future will be able to operate even more safely and reliably with reduced life cycle cost. The concept of an intelligent control system that is self-aware and modifies itself in response to changes in itself or its environment is well established. 1,2 The vision of an intelligent engine control that combined control and health management was first conceived for reusable rocket engines, in particular the space shuttle main engine (SSME).  The impetus for this was that the SSME was not as durable as had been expected and that it might benefit from the experience gained in the air breathing arena. Control complexity for air breathing engines had increased over the years, to the point where it had surpassed that of the SSME, yet the SSME was a more complex machine operating in a significantly harsher environment; thus there was a clear justification to increase its level of "intelligence." The framework proposed in reference 3 for the intelligent control consisted of "a hierarchy of various control and diagnostic functionalities. These functionalities include life-extending control, adaptive control, real-time engine diagnostics and prognostics, component condition monitoring, real-time identification, and sensor/actuator fault tolerance. Artificial intelligence techniques [would] be considered for implementing coordination, diagnostics, prognostics, and reconfiguration functionalities." An example of an intelligent control framework for the SSME that was demonstrated in simulation is shown in figure 3 (ref. 4) . Conceptually, both the above description and the figure could apply equally well to an intelligent control system for an air breathing engine. The goal of such an intelligent system is to increase the level of autonomy of the engine enough that it becomes self-diagnostic, self-prognostic, self-optimizing, and mission adaptable. This means that it can diagnose and recover from faults, it can anticipate and avoid or at least minimize the impact of faults, and it can alter either its performance or its goals to increase the likelihood of successfully completing the overall mission even with degraded capability. The current idea of intelligent control and health management of jet engines is essentially the same as the original concept for the SSME; the functionality of the framework is just as valid today as it was then. In general terms, the intelligent control framework consists of a hierarchical structure: the lower levels operate on a shorter time scale performing duties that are algorithmic in nature requiring less intelligence, while the upper levels operate on a longer time scale utilizing broad intelligence.  In the engine application, viewed in the larger context of the vehicle, the lowest level performs direct control, with the basic diagnostic functions above that. The results of the diagnostic system are fed to an Engine Level Coordinator which evaluates the engine's ability to carry out its mission. BIB002 In a multi-engine vehicle, the Engine Level Coordinator communicates with a Propulsion Level Control responsible for distributing the propulsion tasks among the various engines. 7 The Propulsion Level Control in turn communicates with a Mission Level Control that evaluates the feasibility of attempting to complete the mission given the condition of the engines and vehicle, and perhaps replans the mission to increase the probability of success. BIB001 Note that the Mission Level Control might also have beneath it a structure similar to that of the intelligent engine control hierarchy, corresponding to the airframe. The algorithms described in this paper support the levels below the Engine Level Coordinator in figure 3 . Laboratory research has been carried out into the different components of an intelligent control system for jet engines over many years, mostly in simulation. Occasionally the results of the research are incorporated into a product that is used on-wing, but this is rare because of the need for a technology to "buy" its way on board, and a general reluctance to add complexity as it is perceived as reducing reliability. Thus the irony of intelligent control is demonstrated: increased functionality is obtained through the addition of complexity and thus at the expense of reliability-there must be a net gain in reliability brought about by the added complexity, and it is precisely this that has been the impediment to the serious development of an intelligent control system for jet engines. This will be changing, however. Because of new affordability goals for aircraft propulsion systems, many technologies including those related to intelligent control and health management will need to mature to the point where they can be used. The U.S. Federal Government is sponsoring a significant amount of the work in these areas with the majority of the research being driven by several government programs that advance various goals for commercial and military engines. The Versatile Affordable Advanced Turbine Engines (VAATE) program is a joint Department of Defense (DoD), NASA, Department of Energy (DOE), Industry effort focused on a 10× improvement in turbine engine affordability by the year 2017. The VAATE goals are 1) to maximize propulsion system affordability by reducing fuel consumption and improving durability and life, and by exploiting synergies between common military and civil cores; and 2) to apply revolutionary technologies to achieve quantum improvement in turbine engine capability and cost. In parallel with turbine engine capability increases, VAATE places major emphasis on Research and Development efforts to reduce turbine engine development, production, and maintenance costs. 9,  The VAATE plan includes many technology areas related to Controls and Engine Health Management. Utilization of integrated ground-based and on-board control, diagnostic, and prognostic systems will maximize engine time-onwing. This will be achieved while minimizing support costs without compromising performance and survivability. It will provide flexible health management technologies that can be integrated into legacy, pipeline, and future propulsion systems. Additionally, NASA is interested in intelligent technologies to effect substantial reductions in emissions and noise generated by commercial aircraft, to improve the safety of commercial aircraft, and to increase the throughput of our air transportation system. Specifically, NASA goals are to achieve reductions in carbon dioxide emissions by 20 percent, to achieve reductions in nitrogen oxides generated during take-off and landing by 70 percent, and to achieve community noise level reductions of 20 db of effective perceived noise. Safety and mobility goals are similarly aggressive, with metrics concerning: 1) reductions in the fatal accident rate, 2) reductions in the vulnerability of the air transportation system to hostile threats, 3) mitigation of the consequences of accidents and hostile acts, and 4) the enabling of more people and goods to travel faster and farther with fewer delays. These will be met by technology advancements in both airframe and propulsion systems, including advancements in both conventional and "intelligent" technologies. Many of the necessary intelligent control system technologies lend themselves to life cycle cost (LCC) reduction. Primary drivers for LCC are maintenance, reliability, and fuel consumption. Reduction of all three requires a smarter engine, one that will provide real-time, accurate diagnostic and life management status. To accomplish this, the system must be not only aware of but responsive to its condition (i.e., temperatures, efficiency, and performance). Many other technologies will be required as well to meet the goals: Micro-Electro-Mechanical Systems (MEMS), light weight high temperature materials, sensors for harsh environments, etc. Some of these technologies will open up opportunities for additional aspects of control, notably active control of components, BIB003  but it will suffice here simply to mention that fact, since we are focusing on algorithms that have minimal need for the development of new supporting technologies.