Performance-seeking control - Program overview and future directions

Abstract

A flight test evaluation of the performance-seeking control (PSC) algorithm on the NASA F-15 highly integrated digital electronic control research aircraft was conducted for single-engine operation at subsonic and supersonic speeds. The model-based PSC system was developed with three optimization modes: minimum fuel flow at constant thrust, minimum turbine temperature at constant thrust, and maximum thrust at maximum dry and full afterburner throttle settings. Subsonic and supersonic flight testing were conducted at the NASA Dryden Flight Research Facility covering the three PSC optimization modes and over the full throttle range. Flight results show substantial benefits. In the maximum thrust mode, thrust increased up to 15 percent at subsonic and 10 percent at supersonic flight conditions. The minimum fan turbine inlet temperature mode reduced temperatures by more than 100 ° F at high altitudes. The minimum fuel flow mode results decreased fuel consumption up to 2 percent in the subsonic regime and almost 10 percent supersonically. These results demonstrate that PSC technology can benefit the next generation of fighter or transport aircraft. NASA Dryden is developing an adaptive aircraft performance technology system that is measurement based and uses feedback to ensure optimality. This program will address the technical weaknesses identified in the PSC program and will increase performance gains. Nomenclature AAHT area adder high-pressure turbine component deviation parameter, in 2 AdAPT adaptive aircraft performance technology AJ nozzle throat area, in 2 CEM compact engine model CIM compact inlet model CIVV compressor inlet variable guide vane angle, deg COWL cowl deflection, deg CPSM compact propulsion system model DEEC digital electronic engine control DEHPT high-pressure turbine component deviation parameter, percent DELPT low-pressure turbine component deviation parameter, percent DINL inlet drag, lbf DWFAN change in fan airflow component deviation parameter, lb/sec DWHPC change in high-pressure compressor airflow component deviation parameter, lb/sec EPR engine pressure ratio, PT6 / PT2 F steady-state variable model sensitivity matrix FNP net propulsive force, lbf FTIT fan turbine inlet temperature, ° F g acceleration caused by gravity, ft/sec 2 HIDEC highly integrated digital electronic control M Mach number N1 fan rotor speed, rpm N1C2 fan rotor speed, corrected to station 2 temperature, rpm N2 compressor rotor speed, rpm PCTC percent critical inlet mass flow PSC performance-seeking control PSM propulsion system matrix *Aerospace Engineer. Member AIAA.

An Approach to Aircmft Performance Optimization Using Thrust Vectoring

“…(2) The variety of atmospheric conditions affecting aircraft performance can not be generalized into schedule data. (3) Schedules are updated infrequently, if at all, during the aircraft's operational life.…”

Parameters Nonlinear Estimation of the Propulsion System Performance Seeking Control Using Improved PSO

“…Some flight tests on F-15 were conducted successfully since 80-90 20th century, and the researches show that PSC can improve fuel efficiency, increase thrust, or prolong engine life, so PSC becomes an important research direction of the aeroengine control fields [1,2].…”

“…In all of other estimation process, Kalman algorithm is the key technology of estimation though the estimated parameters are different. Obviously Kalman filter needs linear system model [1][2][3], but the process of linearizing the nonlinear model will introduce unavoidable errors.…”

Neuro-fuzzy approach for performance optimisation of variable nozzle turbofan engine

“…An extensive effort in this field has been made jointly by NASA, McDonnell Aircraft Company and Pratt & Whitney between 1985 and 1995. They developed the so-called performance seeking control (PSC) algorithm with the objective of optimising the quasi-steady state performance of the F100 turbofan engine (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) . The algorithm flow diagram ( Fig.…”

“…The global optimal engine operating point requires iterating on the compact model and linear programming optimisation for specified number of times. The F100 PSC algorithm as currently implemented, however, has some disadvantages that may reduce its efficiency and decrease its reliability (8) . These include the vital need of accurate models to predict actual flight hardware performance, the use of linear models in both the Kalman filter and propulsion system matrix modules, and inherent dependency on accurate measurements of the inputs and outputs of the system being optimised.…”