Handling-Qualities Optimization and Trade-offs in
Rotorcraft Flight Control Design
Mark B. Tischler
Christina M. Ivler
M. Hosin Mansur
Aeroflightdynamics Directorate (AMRDEC)
US Army Rearch, Development, and Engineering Command
Ames Rearch Center
Moffett Field, CA
USA
Kenny K. Cheung
Tom Berger
Marcos Berrios
三年级下册语文
University Affiliated Rearch Center (UARC)
University of California, Santa Cruz
Moffett Field, CA
USA
Abstract
柠檬的英语怎么说Designing rotorcraft flight control systems to meet the handling qualities and stability margin requirements of Aeronautical Design Standard-33 (ADS-33E-PRF) and MIL-DTL-9490E ensures low-pilot workload, incread mission effectiveness and improved safety of operations in all weather and visibility conditions.
The numerous requirements compete with one another and can result in a highly-constrained design space.
Therefore, achieving a satisfactory design that makes best u of the limited available control power can be
a very time consuming process. This paper demonstrates how multi-objective parametric optimization
(CONDUIT®) can be ud to optimize the many design parameters of a rotorcraft flight control system and meet the competing design requirements. Typical design trade-offs are demonstrated for a simple classical respon-feedback flight control system for the XV-15 tilt-rotor aircraft. A more complex design study bad on the UH-60 demonstrates how optimization methods are ud for a modern multi-mode model-following system. Flight test data for the UH-60 RASCAL in-flight simulator validate the design models and predicted handling-qualities.
1Introduction
The starting point of rotorcraft flight control system design is a detailed definition of the intended missions (mission task elements1), visibility and vision aides (usable cue environment) and operational weather conditions. From here, the handling qualities and stability margin requirements of Aeronautical Design Standard-33 (ADS-33) (Anon 2000) and MIL-DTL-9490E (Anon 2008) specify
the needed respon types (e.g. attitude-command/attitude-hold (ACAH) vs. translational-rate command/position-hold (TRPH) and respon characteristics (e.g., bandwidth) to ensure low-pilot workload, high mission effectiveness and safe operations. Compendiums of lessons learned and best practices in flight control design (AGARD 1987, AGARD 2000) further emphasize that “designing-in” good handling-qualities
1Each key new term or concept is highlighted in italics the first time it is ud.
Prented at the RAeS Rotorcraft Handling-Qualities Conference, University of Liverpool, U.K., 4-6 Nov 2008.小白人的历练
characteristics at the start of the design reduces the time and cost of the development process, and minimizes the potential for costly and sometimes dangerous conquences when problems are uncovered only during flight testing. Achieving a design that meets the many and competing requirements is a challenging task for all air vehicles and per-haps more so for rotorcraft.
Four key challenges of air vehicle flight control system (FCS) design can be summarized bad on an excellent review of recent projects (AGARD 2000). The first challenge is the multi-disciplinary aspect of the flight control problem. The control system designers must have a good understanding
of a wide range of disciplines, including flight dynam-ics, structural dynamics, feedback control theory, simulation modeling, handling-qualities, nsors and actuators, redundancy management, verification and validation. Yet, most engineers are specialists and may have a mastery of only a few of the at best. Long and costly design cycles will ari if there is no common language or integrated design tool. A cond challenge is that the design and evaluation of a flight control system requires checking numer-ous and competing design specifications – of the order of 50-100. This process is repeated for the tens (or even hun-dreds) of design (and off-nominal) points and veral lectable modes that are evaluated for a full flight envelope control system. Finding a satisfactory design by manually “turning knobs” is not a practical approach for any more than a simple exerci. A third challenge is that the control system design engineer must continually update the design, integrating improvements in the mathematical models as hardware test data become available, implementing changes in design requirements, and incorporating pilot feedback from simulation and flight tests. A final challenge is the need for design tools that can facilitate the study of the trade-offs between competing specifications, hardware characteristics, and performance metrics, so that the final design may make the best u of available control authority. The failure to consider such trade-offs can compromi control system performance and handling qualities – espe-cially for a partial authority system.
Clearly, sophisticated algorithms and associated interactive computational tools are needed to address the many aspects of the flight control design process. Beyond the general challenges of air vehicle flight control development, the literature cites significant flight control challenges on all of the recent upgrade and new-build rotorcraft programs. The flight control design challenges ari from the need for higher modes of augmentation (ACAH, ACVH) for low-speed/hovering flight in the degraded visual environment (DVE) and all-weather operations, combined with the much incread dynamics complexity of the rotorcraft due to: higher-order dynamics and strong inter-axis coupling of the bare-airframe respon, multi-disciplinary nature of the rotor system analysis, very low signal-to-noi, and large respon lags. Taken together the factors all greatly complicate the design problem and limit achievable flight control performance. Crawford (1998) estimated that UH-60 BlackHawk flight control development had accounted for about 37% of the overall flight test time. A comparable percentage was estimated for the RAH-66 Comanche. At a typical flight test cost of about $50k/hr. for developmental flight testing, flight control system modifications/upgrades are considered a high financial risk proposal for most military Program Managers (PMs). A review of this experience emphasizes the need for control law architectures, design methods and analysis tools that offer the maximum trans-parency and insight for problem resolution. Integrated optimization tools can be a great help in achieving a good rotorcraft flight control design solution in an acceptable amount of time and flight testing.
There are three excellent compendiums of lessons learned and best practices in flight control design and development (AGARD 2000, Pratt 2000, Tischler 1996) that provide uful historical perspective and motivation for the rotorcraft flight control development method prented in this paper. The references indicate six key and repeating themes as important “do’s” of flight control:
1) Retain both the required (“1st tier”) and additional alternative (“2nd tier”) specifications in a multi-tier t of
design specifications.
2) Transparency and simplicity of flight control architecture are very important and highly desirable for design (due
to multidisciplinary aspects), failure (and redundancy) management, testing and troubleshooting. The consid-erations give a clear advantage to classical architectures (e.g., respon feedback and model following) as com-
H∞
春节活动图片pared to purely MIMO architectures (e.g., and LQR).
3) Transparency is critical in the flight control system development process. The process must be systematic,
梦见被僵尸追understandable, and well documented.
4) Flight control system designs must allow for future growth. For example, the development of outer-loop (navi-
gation and hold) modes. This provides an additional strength of the classical (nested loop) architecture.
早安怎么回复5) Regardless of the design methods adopted, the system must meet the requirements. Key is the ability to quickly evaluate and tune the lected design architecture to meet the requirements. The multi-objective function para-metric optimization method adopted herein is well suited to this consideration.
6) Actuator rate limiting has been a key contributor to many of the PIOs experienced in flight test programs and must be considered in the development and evaluation of the flight control system.
The importance of adhering to the six “do’s” can be appreciated by considering that 65% of the co
爱图书st of a new flight control system is committed during design pha (AGARD 1987).
Flight control law design for a new aircraft, or a control system upgrade for an existing aircraft, rarely starts from a blank sheet of paper. There is usually a wealth of prior knowledge for a particular aircraft type (e.g., helicopter vs.fixed wing vs. tiltrotor) within the development group or company that establishes a launching point for each new design or upgrade. This launching point includes internal company design methods, rules of thumb, designer intu-ition, lessons learned, together with existing developmental building blocks and computational tools that are all brought to bear on a new project. So, it is quite common to adapt the same control law architecture for a progression of aircraft projects within a particular control system design group.
With the control law architecture or structure lected, the design task is focud on the lection of the design parameters to meet a t of (competing) requirements. Each control law structure has its particular design parameters or “tuning knobs.” For example, in a classical respon-feedback structure, the design parameters are the regulator gains (proportional, integral, rate, or lead-lag compensation gains) and various feed-forward and crossfeed gains. In a model-following structure, the command model parameters (damping and natural frequency) are included as design parameters in addition to the regulator parameters.
The paration between the airframe model and controller structure on the one hand and optimization (tuning) of the design parameters on the other is illustrated in Fig. 1, and forms the basis of control system design using direct para-metric optimization prented herein. The “unconstrained” design problem that is addresd by theoretically-bad methods (e.g., LQR, , eigenstructure assignment, etc.) is thus converted into a “constrained” design problem of optimizing design parameters within a fixed controller structure to meet competing requirements.
As noted by the proponents of the parametric optimization approach at the DFVLR (now DLR) (Grubel and Joos 1997), the focus of the design is correctly placed on the lection of a comprehensive t of design requirements (specifications). In this regard, a key advantage is that the specifications can now be given in physical terms of con-cern to flight vehicle application – for example, predicted handling-qualities, stability margins, or maneuvering capa-bility. This provides a direct connectivity between the control system parameters and the design requirements expresd in terms of physically-meaningful metrics.
This paper reviews the key aspects of the flight control development process using multi-objective function parameter optimization and its benefits for rotorcraft control design. Typical design trade-offs are demonstrated for a simple classical respon-feedback flight control system for the XV-15 tilt-rot
or aircraft (lateral-directional dynamics). A more complex study bad on a higher-order model of the UH-60 demonstrates how optimization methods are ud for a modern multi-mode model-following system. Flight test data for the FBW UH-60 RASCAL validate the design models and predicted handling-qualities.
Fig. 1Separation between the model/controller structure and design parameter tuning.
H
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2Rotorcraft Flight Control Design using Multi-Objective Parametric Optimization
The overall roadmap for the multi-objective parametric optimization design methodology is shown in Fig. 2. In the discussion that follows, each block of the roadmap is referred to in bold.
Fig. 2Roadmap for multi-objective parametric optimization design methodology.
The entry point to rotorcraft flight control design is the Program Requirements and associated rotorcraft configura-tion, operational environment (i.e., weather, visibility), operational mission, vision aids, and definition of key flight conditions. The program requirements flow directly into the flight control design Specifications. The Aeronautical Design Standard for Handling-Qualities Requirements of Military Rotorcraft (ADS-33E-PRF, Anon 2000) contains a comprehensive t of quantitative (frequency-domain and time-domain) requirements for US military rotorcraft to ensure that satisfactory handling-qualities are achieved. Civil rotorcraft are often variants of military programs and may u ADS-33 as a design guide in addition to the (very limited) flight control design requirements t by the FAA. Flight control requirements in SAE-AS94900 (SAE 2007), the update/replacement to MIL-DTL-9490E (Anon 2008), t minimum stability margins for rigid body and structural dynamics and address uncertainty robustness issues and failures. Considering all axes and flight control modes, the design process will typically consider 50-100 specifica-tions for each flight condition.
The System Architecture is defined by the flight control design requirements and provides the needed respon char-acter (e.g., rate-command/attitude-hold vs. translational-command/position-h
old). At the highest level of architecture is the lection of a partial authority (mixed mechanical and electrical) flight control system vs. a full authority “fly-by-wire” architecture. Partial authority systems are well suited to upgrading legacy aircraft that often are already equipped with a stability augmentation system (SAS) that us 10-20% of the available authority. Full authority fly-by-wire systems are the standard for new aircraft. The detailed architecture can range from classical implementations
H∞of respon feedback (PID) and explicit model following, to modern multivariable concepts bad on LQR and . design methods. As stated earlier the choice depends largely on the experience at the company and specific needs of
the program.
All flight control design methods require an accurate Analysis Model for linear and nonlinear respon simulation. The model captures the dynamics respons of the bare-airframe and the various elements of the flight control system
hardware (actuators, nsors) and software (compensation elements, sampling, filters, memory blocks, and time delays). An accurate prediction of the linearized system frequency respon for the
loop broken at the actuator (bro-ken-loop respon) is especially important in the crossover frequency region (gain is approximately 0 dB), wherein small changes in magnitude and pha can have large effects on the clod-loop behavior. The key nonlinearities are associated with the actuator position limit and rate limit, and internal block diagram (port) limiters. Physics-bad blade-element models such as GENHEL (Howlett 1981) or FLIGHTLAB® (ART 2001) can provide a good starting point especially for flight control design of new aircraft, but are subject to errors in modeling assumptions. Once the prototype test aircraft is available, the most accurate models are obtained using system identification (Tischler and Remple 2006, Ivler 2008). The complete model of the architecture, vehicle dynamics, and flight control system ele-ments is reprented in block diagram form (Simulink® is commonly ud), with as many as 40,000 blocks and 500 dynamic states for a state-of-the-art rotorcraft.
The overall flight control system architecture typically has many tuning parameters. The gains, time constants, cross-feeds, etc. that are available to be adjusted manually or using optimization to meet the specifications are designated as Design Parameters. For two recent multi-mode flight control applications, the numbered some 20-40 parameters in the block diagram. There are many additional parameters that are fixed bad on historical precedence or rules-of-thumb and may not contribute significantly to the specification compliance.
A reasonable Preliminary Design is needed as a starting point for all optimization-bad design methods. Otherwi, there is no guarantee that the arch engine will find a satisfactory solution that meets the many specifications. Pre-liminary design is usually accomplished by applying classical methods (e.g., root locus, loop shaping using Bode plots) to a simplified analysis model (often a reduced-order single-input/single-output reprentation). The MIMO theoretical methods (e.g., LQR) can also provide a uful starting point for key feedback gains. Then, a modern graphical ur interface runs the analys for the various specifications and allows the designer to Check Perfor-mance (as displayed in a horizontal bar chart or performance comb, PCOMB, Fan et al 1991) for the complete sys-tem. Trial and error testing can provide important physical insight into the key connectivities between the design parameters and specifications.
大国担当An automated Optimization procedure (engine) is ud to tune the design parameters of the complete design prob-lem to meet the many competing specifications. The key to automated optimization of the flight control system is the ability to determine a normalized (scaled) numerical grade for each of the metrics. The design parameters are itera-tively updated by solving a multi-pha min-max optimization on the vector of requirements. This ensures that each individual specification is met and expos trade-offs between the requirements.The Updated Design is rechec
ked against the design specifications and associated supporting plots. The optimization process is repeated until all of the requirements are met with a minimum level of feedback and control usage to do the job – thereby minimizing the “cost of feedback.”
Sensitivity Analysis of the converged design provides insight into the quality of convergence, allows a arch of the landscape around the converged solution, evaluates the possibility of local minima, and provides an estimate of the accuracy (Cramer-Rao Bounds) of final design parameters. This ensures that the design problem is “well-pod” and that the solution trade-offs obtained are meaningful. Dependencies of the converged solution on initial design param-eter guess are evaluated to ensure (to the extent possible) that we have avoided local minima. The effects of model-ling uncertainties are also evaluated.
While the complete design may meet all of the minimum requirements, it is usually wi to optimize for incread levels of performance to build in margins for off-nominal flight conditions and uncertainty—in exchange for a reason-able increa in control effort and reduction in stability margin. This is referred to as Design Margin (DM) optimiza-tion, and is achieved by progressively moving the specification boundary locations and re-optimizing the design. The locus of such solutions produces a family of perspective design points – which is a design-off trade curve. Several
of the solutions from the trade-off curve are lected for final off-line and piloted evaluation.
The entire design process is repeated for other flight conditions and aircraft configurations to obtain a Gain schedule of flight control design parameters. Unlike fixed-wing aircraft, the dynamics of the helicopter are not a strong func-tion of center-of-gravity or configuration. Typical look-up tables are limited to gain scheduling bad on airspeed and altitude, and more recent concepts include scheduling bad on external sling-load. Variations with other flight condi-
