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  • Published by the American Institute of Aeronautics and Astronautics with Permission

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    Nonlinear Control of A High-Performance Aircraft Engine

    P.K. Menon*, G. D. Sweriduk† and S. S. Vaddi‡ Optimal Synthesis Inc., Palo Alto, CA, 94303-4622

    Khary I. Parker§ NASA Glenn Research Center, Cleveland, OH, 93523-0273

    Direct design of nonlinear control systems from the real-time simulation model of an aircraft engine is discussed. The benefits of the design approach are that it permits rapid prototyping, and leverages all the investments made in the engine model development into better control system designs. Nonlinear control system design using a component level simulation model of a high-pressure ratio, dual spool, low bypass, variable cycle, military- type engine is presented. Numerical design methods are used to automatically generate controller C-code. Command tracking and mode blending between low and high-power operations are illustrated. Monte-Carlo simulations are carried out to investigate the robustness of nonlinear engine control systems with respect to parameter variations. Robustness comparisons are given with a previous gain scheduled linear control law.

    Nomenclature a14: Forward variable area bypass injector actuator a16: Aft variable area bypass injector actuator a8: Nozzle throat area actuator p2: Fan inlet pressure p27: High-pressure compressor pressure ps3: High-pressure compressor exit static pressure ps15: Bypass duct static pressure at mixer ps56: Low-pressure turbine exit static pressure stp2: Fan variable inlet stator vane actuator stp27: High-pressure compressor stator vane actuator stp27d: Booster tip stator vane actuator t2: Fan inlet temperature t27: High-pressure compressor temperature t3: High-pressure compressor exit temperature t56: Low-pressure turbine exit temperature t5b: Low-pressure turbine blade temperature w14: Forward duct flow rate w2: Fan exit flow rate wf36: Burner fuel flow actuator wf6: Afterburner fuel flow actuator xnh: High-pressure spool speed xnl: Low-pressure spool speed

    * President and Chief Scientist, 868 San Antonio Road, Associate Fellow AIAA † Senior Research Scientist, 868 San Antonio Road, Senior Member AIAA ‡ Research Scientist, 868 San Antonio Road, Member AIAA § Aerospace Engineer, currently with NASA Robert H. Goddard Space Flight Center, Greenbelt, MD 20771 © Copyright 2006 by Optimal Synthesis Inc. All Rights Reserved.

    PKMenon Text Box 2006 AIAA Guidance, Navigation, and Control Conference, August 21 - 24, Keystone, CO.

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    I. Introduction ircraft turbine engine control system designs have traditionally been based on linear control theory1-5. Although most early designs employed the classical proportional-plus-integral control architecture6, recent research

    initiatives at NASA and the engine manufacturers have introduced the use of modern robust control theory for the design of these control systems1, 3-5, 7. Moreover, the availability of real-time aircraft engine models8, 9 has made it feasible to consider the design of sophisticated model-based control systems10-14 to achieve a better control over the engine dynamics in the presence of uncertainties such as component deterioration due to aging.

    The present research is motivated by the advances in nonlinear control system design methods over the past two decades that exploit the knowledge about the system dynamics to simplify the design process, while delivering better control system designs. Additionally, the availability of computer-aided nonlinear control system design software15 has made it possible to speed up the design process by allowing the direct use of simulation models of the dynamic system. Moreover, the design software can automatically generate C-code for implementing the controllers. Both of these capabilities can help accelerate the control system design process by enabling rapid prototyping. The control system designs presented in this paper are based on the MAPSS9, 16 (Modular Aero-Propulsion System Simulation) engine model developed at the NASA Glenn Research Center.

    Although several nonlinear control systems were designed during the present research, two of them will be discussed in this paper. Theses nonlinear control designs use the MAPSS simulation model to transform the engine dynamics using the feedback linearization methodology17, 18 into a globally linearized form. This transformed model is then used for the design of closed-loop control systems. Well known control system design techniques such as pole placement, Linear-Quadratic Regulator theory19 (LQR) and sliding mode control20 approaches can be employed for the design. Inverse transformation of the feedback linearized controller produces the actuator commands. Alternatively, the nonlinear simulation model can be used directly for the design of predictive controllers. Control system designs presented here will be based on the pole placement and LQR methodology, in conjunction with feedback linearization.

    The overall architecture of a typical full authority digital engine control system (FADEC) is given in Figure 1. The control demand input to the FADEC is typically the Power Lever Angle (PLA) or thrust. The FADEC employs temperature and pressure sensors at several stations on the engine, together with low-pressure and high- pressure rotor speed measurements for feedback. These sensor outputs are processed to derive the engine state variables and output variables of interest. The FADEC includes demand-dependent scheduling of certain doors in the gas path and fan/compressor stators to ensure that the engine is properly configured to deliver the demand.

    The feedback control portion of the FADEC, shown within the dashed box is the design problem of interest in the present research. This nonlinear control system uses the processed feedback sensor data, and the PLA/thrust demand to regulate the engine states. In dual-spool military type engines, the control system is responsible for regulating the speeds of the low pressure and high-pressure stages, and the temperature of the engine hot section. It is also responsible for assuring that adequate stall margins are preserved at all times, and also to ensure that the fan does not reach over-speed conditions.

    An engine performance demand model is used to translate the PLA/thrust demand into a set of output variables that the engine control law must track. Typical tracking variables are thrust, engine pressure ratio at low power demand conditions or the engine temperature ratio at high power demand conditions, corrected fan speed and the liner pressure ratio. The engine control laws are required to impart desired dynamic behavior to the engine through judicious use of feedback, while tracking the output variables and ensuring that the physical constraints are not violated.

    As stated at the beginning of this section, engine control system designs were largely based on linearized engine models, in conjunction with linear control theory1 - 7. Gain scheduling is an integral part of this design process. By exploiting the available knowledge about the system dynamics, the nonlinear control techniques circumvent the need for gain scheduling required in linear control system designs. Moreover, mode transitions required for satisfactory operation of the engine as the inputs and operating conditions change, can be made an integral part of the control system design process.

    Several nonlinear control system design methods have emerged over the past two decades17-20. Nonlinear control system designs are derived by manipulating the system dynamics given in symbolic form. However, in practical aerospace problems, the system dynamics are often given in the form of computer simulations that may contain numerical tables and mode switches. Reference 15 describes computer-aided nonlinear control system design software that can employ a simulation model specified in a standard form to carry out numerical design of nonlinear control systems. This software is used to derive all the results given in this paper.

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    The computer-aided design software discussed in reference 15 incorporates 9 distinct methods for nonlinear control system design. Unlike the linear system designs where the results are in the form of compensator parameters, the outputs of nonlinear design processes are complex algorithms. The design software15 provides the control system design in the form of a software function that can be incorporated in the simulation model to close the feedback loop for further evaluation. Recently, a C-code generation capability has been incorporated in this software package.

    Gas Path Door Schedules

    Gas Path Door Schedules

    Stator Schedules

    Stator Schedules

    Nonlinear Control System

    Nonlinear Control System

    Thrust/PLA Demand

    Sensor Processing Algorithms

    Sensor Processing Algorithms

    Gas Path Door Schedules

    Gas Path Door Schedules

    Stator Schedules

    Stator Schedules

    Nonlinear Control System

    Nonlinear Control System

    Thrust/PLA Demand

    Sensor Processing Algorithms

    Sensor Processing Algorithms

    Figure 1. The Engine Control System Architecture

    A brief description of the MAPSS model used for nonlinear control system designs will be provided in the next section. Two