Distributed Engine Control Design Considerations - Sanjay Lall

Proceedings of the 46th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit (online and DVD format only). 25–28 July 2010.

Distributed Engine Control Design Considerations Dr. Walter Merrilli, Jong-Han Kimii, Prof. Sanjay Lalliii Steve Majerusiv, Daniel Howev, and Dr. Alireza Behbahanivi

Various distributed engine control architectures are presented. Different elements of this architecute include semi-autonomous (aka ‘smart’) nodes, digital network communications, data concentrators and electronic controllers. To survive the harsh environment of a turbine engine application, a distributed engine control will require high temperature electronics for its implementation. Several high temperature integrated circuits and a semiautonomous node architecture which incorporates the electronics are presented. These high temperature circuits provide sensor measurement and actuator control interfaces, digital communication, and voltage control and distribution. Next, the authors differentiate between distributed and decentralized controllers and develop a decentralized control design approach based on optimal control theory which can be evolved toward an analysis tool for the control performance of various types of decentralized network configurations.

I. Introduction

T

here is continuing interest in and a strong need for development of tools and technologies that will enable implementation of advanced control and diagnostic systems for ever more complex integrated engine and aircraft systems. While the computing power of modern engine controllers continues to increase, the sophistication of the software being implemented in them continues to outpace the computational throughput provided. This is currently being driven by more advanced propulsion health management schemes. In the future algorithm growth will be driven by intelligent engine control modes, such as active compressor and combustor control modes. These modes will require additional sensors, actuation and high frequency, local control. Multiple processor or coprocessing distributed architectures offer opportunities to significantly increase throughput using electronics currently certified for use on gas turbine engine applications. But with these alternative architectures come new challenges in ensuring that the resulting control implementations meet necessary functional and stability robustness requirements. Hardware advances in high temperature electronics, fuel handling, sensors, and high speed buses are enabling these new architectures for distributed engine control (DEC). However, new tools that can support the design and analysis of the control modes and logic to effectively utilize these new architectures are essential. In addition to increasing software demands on the controller, hardware issues are also driving engine control designers to investigate distributed control implementations. Some of these issues1 include: 1. 2.

the ability to eliminate weight by replacing heavy, shielded wire harnesses and connectors with lighter, less “wire intense” networks of smart sensor and actuator nodes connected through a two wire data buss the ability to improve survivability by distributing functions at multiple smart nodes located around the engine rather than in a single, centralized FADEC. In addition to the improvement in survivability inherent in geographical distribution, additional improvement could be achieved by reallocating functions from nodes that have suffered damage to those still functioning by dynamic program allocation.

i

Scientific Monitoring, Inc., Scottsdale, AZ. Department of Aeronautics and Astronautics, Stanford University, CA. iii Department of Electrical Engineering and Department of Aeronautics and Astronautics, Stanford University, CA. iv Scientific Monitoring, Inc., Scottsdale, AZ. v Scientific Monitoring, Inc., Scottsdale, AZ. vi Air Force Research Laboratory / Wright-Patterson Air Force Base, OH. ii

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3.

4. 5. 6. 7. 8.

a distributed engine control system with an open architecture and a standardized buss structure and communications protocols would provide a framework which would enable multiple vendors to provide engine control subsystems. The resultant competition would reduce overall systems procurement costs to the Air Force and would ultimately result in longer lasting, even more reliable subsystem components. the rapid advance of high temperature electronics enabling the creation of smart sensor and actuator nodes and empowering designers to consider more distributed control systems. the use of open architecture concepts to enhance code reusability the use of distributed architecture to preserve modularity in systems over time the requirement for network compatibility with today’s serial buses and those of higher speed for connection to different subsystems of flight, airframe, power distribution and human factors the need for a common input/output (I/O) and a real time operating system (RTOS)

However, there are significant benefits of a modular, open, distributed engine control. These are • Increased Performance
Reduction in engine weight due to digital signaling, lower wire/connector count, reduced cooling need
Increase in thrust-to-weight ratio • Improved Mission Success
System availability improvement due to automated fault isolation, reduced maintenance time, modular LRU
Increase in system availability • Lower Life Cycle Cost
Reduced cycle time for design, manufacture, V&V
Reduced component and maintenance costs via cross-platform commonality, obsolescence mitigation
Flexible upgrade path through open interface standards These benefits were quantified as shown by the Distributed Engine Control Working Group2 (DECWG), a joint DOD/NASA/DOE/Industry/University Effort. Each of these benefits is directly supportive of the USAF PropulsionSafety and Affordable Readiness (PSAR) program goals of 10% reduced maintenance and a two fold increase in time-on-wing. To date, turbine engine controls have been implemented in a Full Authority Digital Engine Control (FADEC), see Error! Reference source not found.. The F100 engine was the first, in 1981, to be controlled by a single channel FADEC (then called DEEC) with a hydromechanical backup control. In 1984 dual channel FADECs were used to control a PW2037 engine without any mechanical backup. A FADEC controls the output of the propulsion unit taking due account of operational limits, so as to achieve optimum performance and economy. It is in full control, and as such must be ultimately reliable (catastrophic failure less than 1 part per billion flight hours), with a mean time between failures (MTBF) that is greater than 20,000 hours. In additional to the basic control modes modern FADECs incorporate onboard models for fault detection and life tracking, comprehensive built in tests and engine diagnostics as well as auxiliary functions such as vectoring nozzle control. For example the advanced military engine may have as many as 8 controlled variables and 10 sensed inputs (not counting redundancy) while an advanced commercial engine may have 7 sensed inputs and 3 controlled variables3. For example, a recent paper4 describes how Figure 1 An example of a small engine FADEC sensors could be used to control the engine in a higher

- Courtesy of Pratt&Whitney

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performance envelope using advanced Propulsion Control and Health Management (PCHM). In this paper we assume a fully distributed architecture that includes distributed, smart digital nodes. In this model, the data acquisition, signal processing, out of bounds criteria and loop closure all occur in the nodes as subprocesses remote from the central control processor. The centralized processor now acts as a data integrator. multithreaded host and hierarchical controller, not a single application. This structure is analogous to a client-server application in information technology terms. This model requires a thinner network wire in terms of overall data, as what is being passed are higher level commands and data, not low-level bit streams. Also, the network permits the FADEC centralized core to be remotely located from the engine, if desired, thus allowing its operation in the much less harsh environment of the avionics bay. This would allow future controllers to capitalize on industry growth in processors, memory, storage and software developments. Additionally, assuming that the centralized FADEC core hardware does not require more than industrial temperature range components, it may be possible to replace the FADEC core as a one board upgrade over time. These developments are then accomplished at lower cost, by taking advantage of COTS components. Also, in this paper the authors assume that certain hardware components will be available to implement distributed controls for turbine engines. Among these components will be smart sensors and actuators which will be able to communicate through a digital network as shown in Error! Reference source not found.. We will describe some recent developments in high temperature electronics that will help enable the use of a digital network. Finally, we will describe an approach which will provide the ability to analyze these control systems which is based on recent developments in decentralized control theory.

II. Distributed Engine Control Implementation The DECWG has developed an initial architecture and some specifications for a distributed control system. The DECWG distributed architecture is shown in Error! Reference source not found.. This figure shows the evolution of the control architecture from a centralized or federated system, to successively more distributed systems. In additional the likely thermal environments for the respective portions of the control are also presented.

Figure 2 FADEC Architectures Evolution

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Initial distributed FADEC configurations will have a core-mounted, uncooled data concentrator with relatively simple communications between smart effectors and the concentrator and higher level communications between the concentrator and the control law processor. In current systems the FADEC is a centralized control box (Federated System) with numerous tubes and cable connectors linking to sensors and actuators. This architecture is used on almost all current aircraft turbine engines and has met the challenge of reliable, cost effective engine control. However, as demands on the control system increase, the use of a distributed architecture to provide additional functionality is likely. For example as more sensors and actuators are added, many feet of cable are added to the system. Moreover, interconnect reliability can be a great concern as the wire bundle may reach a count of 1200 or more wires5. One critical technology that must be matured to enable the implementation of distributed engine controls is high temperature electronics. The authors have developed electronics capable of withstanding the harsh environment of a turbine engine application. A. High Temperature Electronics Reliable integrated circuits capable of operating at elevated temperatures are needed to reduce size and improve performance of turbine engine control systems. In this paper, we present four analog integrated circuit (IC) designs that perform sensing, actuation, and power supply functions essential to closed-loop actuator control. These four ICs along with a digital signal processor and control area network (CAN) devices are incorporated in a semiautonomous node as shown in Error! Reference source not found..

Figure 3 Semi-Autonomous Node

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The four ICs are the HHT104, HHT212, HHT250 and the HHT300. The HHT104 dual-channel sensor interface IC is capable of digitizing up to eight differential sensor inputs with up to 16 bits of variable resolution. This part is typically used to measure LVDTs, thermocouples, RTDs, and pressure sensors. The HHT212 quad precision current driver includes full bridge drivers to control torque motors and other small loads requiring precise current control. The HHT250 quad load driver includes an 8-bit PWM controller and may be used with solenoids, motors, and other inductive loads. The HHT300 quad power supply IC is capable of operating over a wide input supply range and is intended for microprocessor and sensor applications requiring several supply voltages. Additional information about the operation and application of these ICs is available6. 1. Integrated Circuit Process The integrated circuits described in this paper were fabricated in a conventional, low-cost 0.5-micron bulk CMOS process. To achieve high performance over a wide temperature range, proprietary techniques are used to stabilize amplifier gain, operating frequency, and output current7. The low-voltage process used in these designs is extended to high-voltage operation (up to 22V) using devices with thick gate oxides and extended geometries. The designs use circuit topologies that minimize the number of high-voltage nodes required, and over-voltage protection devices are placed on input pins to prevent damage. High-reliability IC layout techniques are used to improve device reliability at elevated temperatures. In particular, the use of redundant vias and wide traces increases initial yield and reduces the rate of failure8. 2. HHT104 Sensor Interface IC The HHT104 is a dual-channel sensor interface IC that can operate at temperatures in excess of 150 degrees Celsius. Each channel consists of a differential-input instrumentation amplifier (INA) and 2nd-order sigma-delta analog-to-digital converter (SD ADC) for single-chip amplification and digitization. The INAs and SD ADCs employ switched-capacitor, Gm-stabilized circuitry, finite gain compensation, and correlated double sampling to provide superior resolution and performance at high temperature. Analog multiplexing extends the number of selectable differential inputs to 8, with common-mode levels up to 12 VDC. The SD ADCs provide a binary bit stream output which should be filtered using an off-chip digital decimation filter to obtain approximately [2.5log2(OSR)-2] bits of effective resolution, to a maximum of 16 bits. In this equation, OSR is the over-sampling ratio, or the ratio of the input signal frequency to the SD ADC clock source of 1.5-MHz. The programmable-gain INA provides a maximum gain of 16X, extending the dynamic range to 110 dB. A 3-wire Serial-Peripheral Interface (SPI) allows for input selection and gain programming. An H-bridge for driving sensors with an arbitrary voltage level separate from VDD is included, and is capable of driving up to 50mA. Other features include a built-in self-test mode, an internal clock oscillator with dual outputs (one at modulator speed, another at microcontroller speed), a band gap voltage reference output and a linear voltage absolute temperature thermometer. A 24-clock-cycle (36-uS) delayed power-on-reset circuit can provide the reset signal for an external microcontroller or digital signal processor (DSP). 3. HHT212 Quad Precision Current Driver The HHT212 is a high-temperature IC that includes four 12-bit current-output digital-to-analog converter s (IDACs) grouped in pairs. Each I-DAC sinks current through a high-voltage switch that may be enabled or disabled via a SPI control register. A complementary high-voltage pull-up switch is controlled via the same SPI operation. As such, an I-DAC pair can be connected across a common load with both enabled to double the load current. Alternatively, an I-DAC pair can be cross-connected to a common load for bipolar drive. A monolithic temperature-stabilized reference current is sunk from the core VDD (DVDD). This reference current is scaled according to a 2-bit gain setting in the SPI control register for simple full-scale range adjustment. An additional 12 bits control the I-DAC resolution within a gain range. The reference current is also scaled to 1-mA and provided to an external pin for biasing of RTDs or for any other application requiring a temperature-stable current source. All I-DACs can be disabled by programming a single SPI bit, and a hardwired shutdown pin provides control in the event of controller failure. Internal overtemperature and overcurrent protection as well as built-in self-test functions increase safety, and the HHT212 can function at temperatures above 150 degrees Celsius. 4. HHT250 PWM Driver IC The HHT250 PWM Driver IC has four pairs of complementary PMOS and NMOS switches that may be operated in several configurations. In the “switch” mode, each transistor is rated for 500mA and may be individually turned on and off. This mode is useful for operating static loads such as relay coils and solenoids. In 5 American Institute of Aeronautics and Astronautics

the PWM mode, two individual PWM controllers may also be configured to operate in full-bridge (four switches) or half bridge (two switches) operation. Unipolar and bipolar motors can be operated in this mode, and programmable dead-band delay allows designers to trade power efficiency for increased holding torque in their application. 5. HHT300 Quad Switched-Mode Power Supply IC The HHT300 Quad Switched-Mode Power Supply (SMPS) IC is intended for modules that need several supply voltages, require low power dissipation, and have a wide input voltage range. The internal FET switches are rated for 500 mA per supply to meet the typical power requirements of a closed-loop actuator node. The converters operate from 6-22V DC and can regulate their outputs down to 1.25V. A novel feedback divider mechanism enables the output voltage to be pin-programmed to one of three common supply voltages (5V, 3.3V, or 1.8V) with no additional components. A fourth “bypass” option allows other voltages to be set using a resistor divider. This feedback mechanism uses no external loop compensation components that are typically required in switching power supplies. Each SMPS supply has an “enable” input and a “ready” output to facilitate power supply sequencing. This feature reduces node startup current and is required by many ICs to prevent latch-up. The “ready” signal is asserted when the output exceeds 85% of the steady-state value. B. Networked Communications Another key technology is the network that will be used to enable sensors, actuators and controllers to communicate. In discussions9 with experts, it is clearly a preference to utilize a distributed architecture for large engines. For example, estimates of one-fourth (1/4) to one-half (1/2) of the computing power of the FADEC is used in low level activities, such as data conversion, sampling, filtering, Fast Fourier Transform (FFT) and other processes that are related to data acquisition and signal conditioning (DAQ). As more sensors and actuators are added to the engine, this low level overhead will require more processing or CPU cycles. There is no inherent reason or value to performing these calculations in the central FADEC. In fact distributing the DAQ function to local nodes can have substantial cost and weight savings. Various network topologies (CAN 2.0b, Ethernet, 1553, 422, Flex-Ray, Firewire) are being used throughout the airframe with good success. A similar approach may be used as appropriate in engine controls with DAQ being done in a module near the sensors, or at a data concentrator in central area near the sensors and actuators. The modes and logic portion of the FADEC could then be located off the engine itself, and mounted in an avionics bay or it could be a smaller, engine mounted device requiring less cooling. In any case, distributing the processing throughout nodes within the engine should provide simpler overall engine control software, as each node will have fewer loops and branches in code, and the code per node can be much simpler. Centralized FADEC controls have been successfully analyzed and designed using the existing body of classical and modern control design tools. However, as the demand for distributed engine controls grows, new tools that can predict performance and stability of systems with decentralized control functions will be needed. In addition to the author’s work described herein, others are looking into distributed control design techniques10. In this paper the authors describe design and analysis tools created from existing, general, distributed controls design theory and tools created from new theory to provide a means of effective and efficient distributed engine controls design and analysis.

III. Optimal Decentralized Control Once a fully distributed architecture is in place, it will be possible to geographically decentralize the control logic into the various smart nodes. Here a distinction is made between distributed and decentralized controls. In a distributed control the hardware elements of the control, the sensors, actuators and the controller are geographically distributed throughout the engine. From this perspective every control is distributed. However, we add the further distinction that the control elements communicate with one another over a digital communication network rather than through point-to-point analog means. A decentralized control refers to the geographical distribution of various portions of the control logic throughout the hardware elements of a distributed control. Decentralization assumes the presence of distributed smart nodes and a fully distributed network for implementation. Decentralization would have several advantages including improved survivability, code redundancy and more efficient usage of computing resources. Successful decentralization requires adequate design and analysis tools that can provide control designs

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which will be compatible with various distributed architectures while meeting stability and performance requirements. In this section of the paper the authors first provide a definition of the decentralized control problem and then suggest a design method for an approximate solution to such problems, based on the recently introduced quadratic invariance11 concept. Since the design obtained by the quadratic invariance method is optimal for the approximated plant, but not necessarily for the original plant, we next suggest a coordinate-wise descent method which improves the design by straightforward iteration. Finally, a numerical example based on a linearized model of the GE F404 turbine engine at rated thrust and at 35,000 ft altitude is presented to illustrate the approach. A. Problem Definition The optimal decentralized control problem can be defined by the information constraint added to the optimal centralized control problem. minimize subject to

P11 + P12 K ( I − GK ) −1 P21 K stabilizes P K satisfies information constraint

P11 , P12 , and P21 describe the input-output interconnection of the models. G represents the plant model and K is the controller to be designed. The problem description without the last information constraint is exactly the classical optimal centralized control design problem, where the change of variables according to Q = K ( I − GK ) −1 transforms the problem to a convex one. The optimal control K * is generally full, which represents the centralized control authority. The last constraint describes the decentralized control architectures. For example, independence of the control u i on the measurement

K ij being zero implies the

y j . For example,

0  k ( s ) K ( s ) =  11 k 22 ( s )  0 represents the fully decentralized controllers, where the first control is decided by the first measurement only, and the second control is decided by the second measurement only. Another example with an upper triangular controller

k ( s ) k12 ( s )  K ( s ) =  11 k 22 ( s )  0 represents the partially decentralized controllers, where the first control depends on both of the measurements, and the second control depends on the second measurement only. The change of variables, which helped the centralized problem, is of no use in this case since it transforms the simple information constraints to complex nonconvex constraints. In general, finding the optimal control for such a decentralized setup is very hard, and no algorithm is known to efficiently solve the problem in polynomial time. B. Quadratic Invariance Method The quadratic invariance characterizes a simple algebraic condition of the plant model and the controller model, under which the decentralized optimal control problem reduces to a convex problem and hence can be easily solved. We define a general representation of decentralization constraints. A subspace S whose elements are the controllers with the same size and the same sparsity pattern is called an information constraint. We consider finding optimal linear controllers, and define the quadratically invariant information constraints as follows. •

S is quadratically invariant under G if KGK ∈ S for all K ∈ S . 7 American Institute of Aeronautics and Astronautics

If •

S is quadratically invariant information constraint under G , the following can be shown. K ∈ S if and only if K ( I − GK ) −1 ∈ S

The above tells that the quadratic invariance guarantees the convexity of the information constraint set under the transformation according to Q = K ( I − GK ) minimize subject to

−1

. This gives the equivalent problem.

P11 + P12 QP21

Q ∈ RH ∞ Q∈S

This is now a convex optimization problem, and may be solved by standard methods. This implies that if a system and a controller jointly satisfy some simple algebraic condition, then the decentralized optimal control problem is easily solved. Notice that quadratic invariance is an algebraic condition. It therefore holds for continuous-time and discrete time systems on both a finite and infinite time horizon. Also it can be used with many convex criteria problems such as H 2 or H ∞ problems, directly handling the information constraint. For an illustrated example, consider the following three plants structures

G1 , G2 , and G3 with three information

S1 , S 2 , and S 3 . * * * G1 = * * * * * *

* 0 0 G2 = * * 0 * * *

* 0 0  G3 = 0 * 0 0 0 *

* 0 0 S1 = * * 0 * * *

* 0 0 S 2 = * 0 0 * * *

* 0 0  S 3 = 0 * 0 0 0 *

where * indicates the nonzero elements (transfer functions). We can easily check that S1 and S 2 are quadratically invariant under both

G2 and G3 . S 3 is quadratically invariant under G3 . None of the above information constraints is quadratically invariant under G1 . Note that the set of full controllers are the only quadratically invariant subset of controllers under full plants ( G1 ), and the diagonal plants ( G3 ) are the only class under which the diagonal controllers are quadratically invariant.

The notion of quadratic invariance is powerful for plants with some sparsity patterns. However it is not appropriate for application to general full models because of the aforementioned reasons, i.e., the engine model we consider here is full, thus the only quadratically invariant class of controllers are full (centralized) controllers. In approximately computing the optimal decentralized controller for the given engine system, we suggest a heuristic solution process using the quadratic invariance concept. The key idea is simple. Consider the designerspecified control structure requirements (e.g., diagonal, triangular, or some other sparsity patterns that meets the system design requirements). Among those information structures, some allow approximate plant models under which the given patterns are quadratically invariant, and then the optimal decentralized control for the approximate plant is easily computed via off-the-shelf convex optimization tools. We simply truncate some elements of the original transfer function matrix to build the approximation model. Provided that the approximate sparse model dominates the full dynamics, like weakly couples systems, the computed solution is expected to show acceptable control performance on the original full model. The suggested process is summarized in the following box. 8 American Institute of Aeronautics and Astronautics

Quadratic invariance method:

S is specified, on the given full plant G . Say, * * * * S= G=   0 * * * Note that S is not quadratically invariant under G . 1.

Desired decentralized controller structure

2.

Find a sparse approximation G of the given model, such that the specified control structure quadratically invariant under the approximate model.

~

S is

~ * * G=  0 * Now 3.

~ S is quadratically invariant under G and the optimal control is can be efficiently computed. Find the optimal control for the approximate model.

* * K* =  ∈S 0 *   4.

~ * * G=  * *

Apply the conputed control to the original model.

* * K* =   0 *

* * G=  * *

C. Local Optimization: Coordinate-wise Descent Method The design obtained by the quadratic invariance method is optimal for the approximated plant, but not necessarily for the original plant. Thus it can be further improved by local optimization methods such as a simple iterative algorithm presented below. Consider a plant P to be controlled and the controller K .

 x& = Ax + Bw w + Bu  P  z = C z x + Dzw w + Dz u  y = Cx + Dw w 

 q& = AK q + BK y K u = C K q + DK y

The optimal control design problem in the previous section can be equivalently written using this representation. If we constrain the optimization over a set of fixed order controllers, the decentralized H 2 control problem is equivalently written in the bilinear matrix inequality (BMI) representation as follows. minimize subject to

Y  A X + XA X B   0  >0 C Y 

trace

T

D =0

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 AK C  K where

BK  satisfies information constraint DK 

A , B , C , and D describe the closed loop dynamics.  A + BDK C BC K  A= AK   BK C C = [C z + D z DK C D z C K ]

 B + BDK Dw  B = w   B K Dw  D = Dzw + D z DK Dw

The information constraints in the state-space representation can also be written as sparsity pattern constraints of AK , BK , C K , and DK . Some of them are illustrated below. Note that the first matrix inequality is bilinear in

AK , BK , C K , DK quadruple, and X . However for fixed

AK , BK , C K , DK , it is linear in X and the problem reduces to a semidefinite programming (SDP). Similarly the problem reduces to an SDP for fixed X . Based on these observations, we can apply a simple heuristic approach to improve the QI design by solving the coordinate-wise LMIs iteratively. Note that this process guarantees to monotonically reduce the objective value from the initial design. Since the convergence of such a local coordinate-wise descent scheme is sensitive to the choice of the initial controller, the QI design as the initial point, which is near-optimum, can result in a very useful design in practice, although it may not be globally optimal.

Coordinate descent method: 1. 2.

Initialize a controller with the design obtained from the QI method. Fix AK , BK , C K , DK . It reduces the problem to an SDP with LMIs in

4.

Go to 2. Iterate the process until the progress reaches the termination criteria.

X . Solve it. 3. Fix X . It reduces the problem to an SDP with LMIs in AK , BK , C K , DK . Solve it.

D. Numerical Example A linearized model of the GE F404 turbine engine at the rated thrust condition at 35,000 ft altitude was taken for an example, and then scaled for design convenience.

3.388 0   − 1.46  0.184 0.4578    x& p = Ap x p + B p u p = 0.2219 − 2.23 0  x p +  0.163 0.0015 u p  1.467 − 4.8375 − 0.4 1.5325 − 0.0978 0 0 1  0 0  y p = C p x p + D pu p =  xp +   u p 1 0 0 0 0  Note that the above model has no pure integrator, and needs to be augmented with additional integrators at the input terminal in order to achieve zero steady state error. A target feedback loop in the classical LQG/LTR approach T

for the augmented system is shown below. The new variables are defined by x = [ u p

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T

T

x p ] and u = u p .

− (C p Ap −1 B p ) −1  0  u p   I  & + u +  w Ap   x p  0 p  C p T (C p C p T ) −1 

0 x& = Ax + Bu + Lw =  Bp

[

y = Cx + µ v = D p

u p  Cp   + µv x p 

]

w and v can be interpreted as the zero mean process and the measurement noise with the identity T T covariance. The design problem of z = [ (Cx) ρ u T ] is considered with µ = 0.05 and ρ = 10 −6 . where

Since the augmented model has pure integrators at both input channels, the system is not open-loop stable, thus the optimal control design problem should begin with finding any stabilizing controller. It turns out that small static diagonal feedback (or with a fast servo for a strictly proper controller) stabilizes the system. Even with the simple model, the controllers resulting from the QI method can be fairly high order systems because of the vectorization process in handling sparse models. Fortunately, it turns out that relatively small (2 to 5 in this example, see Figure 4.) number of modes dominate the resulting control system, thus allowing reduced order controllers with good approximation and easy implementation. A centralized H 2 control (LQG solution) as well as two decentralized H 2 controls (an upper triangular controller and a diagonal controller) were designed using the QI method and compared for commanded step changes in turbine temperature. The simulation results are shown in Figure 6. Slight degradation in results between the full and the sparse case is observed. The lack of disturbance rejection is seen as well. Both of the QI designs can be further improved by the coordinate descent method, though only the diagonal controller is demonstrated here. The convergence history and the response to turbine temperature command are shown in Figure 5 and Figure 7. Achieved H 2 norms are summarized in Table 2.

xp

yp

up

Variable

Description

Controller

Objective value

N2

Fan speed

Full (LQG)

0.543

N 2.5

High pressure compressor speed

Diagonal (QI)

0.619

Tt 4.5

Turbine total temperature

Diagonal (QI+CD)

0.592

Tt 4.5

Turbine total temperature

N2

Fan speed

Wf

Fuel flow rate

Aj

Nozzle area

Table 1: State variable, measurements, and control inputs for the engine model. All the variables are scaled.

Table 2: Achieved objective values (squared norms)

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H2

Figure 4 Hankel singular values of the upper triangular QI controller. This indicates each controller allows a reduced model of order less than 5.

Figure 6. Response to turbine temperature command. An upper triangular controller and a diagonal controller, both designed by the QI method, are compared with the centralized LQG control.

Figure 5. Performance improvement coordinate-wise descent method.

by

the

Figure 7. Response to turbine temperature command. Diagonal controllers (QI and QI + coordinate descent).

IV. Summary While the computing power of modern engine controllers continues to increase, the sophistication of the software being implemented in them continues to outpace the computational throughput provided. This is currently being driven by more advanced propulsion health management schemes. In the future algorithm growth will be driven by intelligent engine control modes, such as active compressor and combustor control modes. These modes will require additional sensors, actuation and high frequency, local control. Multiple processor or co-processing distributed architectures offer opportunities to significantly increase throughput using electronics currently certified for use on gas turbine engine applications. But with these alternative architectures come new challenges in ensuring that the resulting control implementations meet necessary functional and stability robustness requirements. Hardware advances in high temperature electronics, fuel handling, sensors, and high speed buses are enabling these new architectures for distributed engine control (DEC). However, new tools that can support the design and analysis of the control modes and logic to effectively utilize these new architectures are essential. 12 American Institute of Aeronautics and Astronautics

In this paper an evolution of distributed engine control architectures has been presented. Several high temperature integrated circuits and a semi-autonomous node architecture which incorporates the electronics have been presented. These high temperature circuits provide sensor measurement and actuator control interfaces, digital communication, and voltage control and distribution. Under the assumptions of a fully distributed system and the availability of high temperature capable implementation hardware, the authors next develop a decentralized control design approach based on optimal control theory. The proposed approach systematically finds an approximate solution to the optimal decentralized control problem applying the recently introduced quadratic invariance concept followed by a coordinate-wise descent method which improves the design by straightforward iteration. A numerical example based on a linearized model of the GE F404 turbine engine was presented, and the proposed method was demonstrated to find the approximate solution efficiently. The approach can be evolved toward an analysis tool for the control performance of various types of decentralized network configurations, such as fully decentralized networks, partially decentralized networks, or decentralized networks with delayed communication. Future work is required both on the hardware and software sides. On the software side work should include the further development of the design and analysis tools for computational efficiency and more robustness in the design process. Also a graphical user interface for the tools is need to create an efficient and effective work environment for the designer. Finally, the tools need to be validated using more realistic models that incorporate non-linear and limit effects typical of aircraft engine operation. On the hardware side additional development is needed to develop and demonstrate high temperature electronics that can provide the necessary reliability for successful engine application. Also, a network architecture and common set of communication protocols is needed to allow for multiple suppliers to provide components for a distributed system. The network architecture will need to be supported by its own set of electronics, connectors and wiring that can survive reliably in an engine’s operating environment.

References 1 Behbahani, A. R., “Achieving AFRL Universal FADEC Vision with Open Architecture Addressing Capability and Obsolescence for Military and Commercial Applications,” Air Force Research Laboratory/ Wright-Patterson Air Force Base, Nov. 2006. 2 Behbahani, A. R., Culley, D., et. al, “Status, Vision and Challenges of an Intelligent Distributed Engine Control Architecture,” AIAA 2007-01-3859. 3 Jaw, L. and Mattingly, J., Aircraft Engine Controls – Design, System Analysis, and Health Monitoring, ISBN 978-1-60086-7057, AIAA Education Series, 2009 4 Simon, D. L., Garg, S., Hunter, G. W., Guo, T.-H., Semega, K. J., “Sensor Needs for Control and Health Management of Intelligent Aircraft Engines,” Proceedings of ASME Turbo Expo 2004, Power for Land, Sea, Air, Vienna, Austria, June 2004. 5 E-mail conversations with Goodrich ECS. 6 Majerus, S., Howe, D., Garverick, S., Hiscock, D., Merrill, W., “High Temperature, bulk-CMOS Integrated Circuits for a Distributed FADEC System,” International Conference on High Temperature Electronics (HiTEC 2010), May 11-13, 2010, Albuquerque, New Mexico. 7 Yu, X., Garverick, S.L., “A 300°C, 110-dB Sigma-Delta Modulator with Programmable Gain in Bulk CMOS,” Custom Integrated Circuits Conference, 2006. CICC '06. IEEE , vol., no., pp.225-228, 10-13 Sept. 2006. 8 Allan, G.A., Walton, A.J., Holwill, R.J., “A yield improvement technique for IC layout using local design rules,” ComputerAided Design of Integrated Circuits and Systems, IEEE Transactions on , vol.11, no.11, pp.1355-1362, Nov 1992. 9 Goodrich ECS and Hamilton Sundstrand, private communications. 10 Pakmehr, M., Mounier, M., et.al., “Distributed Control of Turbofan Engines,” 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, 2-5 Aug 2009, Denver, Colorado. 11 Rotkowitz, M., Lall, S., “A Characterization of convex problems in decentralized control,” IEEE Transactions on Automatic Control, 51(2):274-286, Feb. 2006.

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