EVALUATION OF AIRCRAFT CONTROL-PILOT INTERACTIONS

The HQA and prediction for a fighter aircraft are important in the design and development of a flight-control system [11]. Traditionally, a pilot’s opinion ratings have been on 1- to 10-point scale, called Cooper-Harper scale. Each point gives a qualitative opinion of the pilot’s view of the aircraft’s overall behavior for a specified task/mission. This resembles multivalued description. The ratings/qualitative description is also specified in the three levels: rating 1-3.5 ! Level 1; rating 3.5-6.5 ! Level 2 and rating 6.5-10 ! Level 3. The description of the behavior of the aircraft with a pilot in the loop sounds somewhat like a rule-based logical enunciation of the pilot’s assessment of the aircraft’s performance. Levels 1-3 have a gradation more coarse than the pilot’s rating scale. To quantify this assessment, several HQ criteria have been evolved, which are largely based, in one way or the other, on the fundamental tenets/concepts of the (conventional) control theory, e. g., bandwidth, rise time, settling time, gain and phase margins (GPMs), etc. [12]. It might perhaps be feasible to establish some connectivity between multivalued FL/S, H-infinity concept, and HQ criteria. FL/Ss are useful in representing, in some concrete form, the uncertainty in a system’s model—this uncertainty plays an important role in evaluating the robustness of a flight-control system—the stability and related aspects of which are evaluated using the HQ criteria. A possible systems-oriented synergy of many such aspects presented in this book is highlighted in Figure 1.3 [13]. Further systems-oriented synergy is depicted in Figure 1.4.

Thus, the important features of flight mechanics/dynamics are prediction, mea­surements, and representation, i. e., modeling and analysis of aerodynamic forces, and evaluation of HQ. Related main problems in engineering are [2,6,14,15,17] (1) stability in motion, (2) responses of the vehicle to propulsive and control input changes, (3) responses to atmospheric turbulence and gust, (4) aeroelastic oscilla­tions like flutter, and (5) performance in terms of speed, altitude, range, and fuel assessed from flight maneuvers and testing. Reference [15] is a recent volumetric treatise on flight dynamics. Also, the field of ‘‘soft’’ computing [16] is gaining importance in the aerospace field and hence some aspects are dealt with in this volume (Chapters 2, 8, and 9). There is an immense scope of extending soft computing to flight simulation (ANN—polynomial models for aero data base repre­sentation), human operator modeling, etc.

FIGURE 1.3 System synergy of aircraft control system aided by ANNs, fuzzy system (FS), system identification (SID) and restructuring schemes (AGS: adaptive gain scheduling). (From Raol, J. R., ARA Journal, 2001, 25, 2002. With permission.)