Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Aeronautical vehicle
Reexamination Certificate
1999-12-17
2002-01-22
Cuchlinski, Jr., William A. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Aeronautical vehicle
C701S004000, C701S006000, C701S007000, C701S010000, C244S04500R, C244S013000, C244S03500A, C244S07600R, C244S036000, C244S203000, C244S184000
Reexamination Certificate
active
06341247
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to methods for determining and optimizing the performance characteristics of airborne vehicles and, more particularly, to methods for enhancing the performance of airborne vehicles taking into account environmental conditions and the individual characteristics of the particular vehicle.
BACKGROUND OF THE INVENTION
As is known, conventional airborne vehicles such as aircraft and missiles are structurally designed to provide inherent aerodynamic stability and desirable flight and handling characteristics. Generally, such vehicles employ a conventional manually-operated or automated flight control system, which facilitates control and guidance of the vehicle along a desired flight path. The flight control system controls and guides the airborne vehicle using a number of control surfaces or effectors including stabilators, ailerons, flaperons, spoilers, rudders, propulsion devices, and the like. In addition to controlling and guiding the vehicle along its intended flight path, the flight control system desirably optimizes one or more flight performance parameters, such as, for example, cruise range, range factor, specific excess power, fuel consumption, etc.
In order to accurately control and enhance the performance of airborne vehicles, the stability and control effects that each control surface or effector has on the vehicle must be known. Preferably, such effects must be known for all possible combinations of adjustments and/or positioning of all of the control surfaces and effectors. Additionally, the control and performance would be enhanced if such effects were also known for all types of environmental conditions, (such as, for example, altitude, wind speed and direction, atmospheric pressure and density, and humidity) to which the airborne vehicle may be subjected.
One tool to aid the designer of an airborne vehicle in determining the performance of the vehicle is an analytical, mathematical model of the vehicle and its possible environmental operating conditions. Generally, such models are based on the original design of the airborne vehicle and do not adequately take into account such factors as changes in the structure of the aircraft, which naturally occur over time and use. Additionally, conventional models also cannot adequately predict all possible loading configurations of the vehicle including, for example, passengers, fuel, and stores. It is also difficult to accurately predict all of the possible environmental conditions to which the vehicle may be exposed. As a result, many of the forces and moments to which an airborne vehicle is subjected cannot be accurately determined by purely theoretical calculations and, in many instances, the vehicle designer relies on experimental aerodynamics, such as those obtained using a wind tunnel. As is known, a wind tunnel is an engineering tool whereby the reactions of carefully controlled airstreams on scale models of airplanes, missiles or their component parts can be studied. Using a wind tunnel, the vehicle designers can measure various forces and moments on the aircraft or missile model. These measured forces and moments may then be used to determine specific operating conditions, i.e., control effector settings, for various flight conditions, which are then programmed into the air vehicle's flight control system. These operating conditions may be refined using measured flight test data.
These prior art techniques suffer from significant disadvantages in addition to those previously mentioned. For example, these prior art techniques do not take into account the particular environment in which the airborne vehicle is currently operating. Substantial flight test evaluation would be required to collect data sufficient to represent the variety of atmospheric conditions that may effect a given vehicle. The prior methods also require periodic updating to accommodate vehicle changes over time, such as different weapon loading configurations, structural modifications to the vehicle, or structural defects, such as component misalignment or surface warpage. Additionally, using these approaches, the operating conditions would generally be fine-tuned to only a limited number of prototypical vehicles.
One attempt to improve aircraft performance was undertaken during the development of the Mission Adaptable Wing (“MAW”), which was flight tested on an experimental F-111 military aircraft during the 1980s. The MAW program attempted to improve aircraft performance by making small wing camber adjustments to produce incremental changes in range. The MAW used a discrete gradient measurement process, which had the undesirable effect of amplifying sensed data noise. The use of this measurement process also rendered the approach subject to measurement equipment inaccuracies. Unfortunately, the flight test program for the MAW demonstrated that the planned approach was unsuccessful for most airborne applications primarily because of susceptibility to such measurement noise. Moreover, the MAW program did not take into account the interactive influences that multiple control surfaces and effectors have on specific performance indices.
Accordingly, there is a continuing need for an improved system and method for optimizing airborne vehicle performance. Preferably, the improved method would be capable of identifying, measuring, and optimizing vehicle performance in the presence of varying or unknown atmospheric conditions. Desirably, the improved method would not rely on models but would account for the distinct, individual characteristics of each airborne vehicle, and would automatically accommodate changes to the vehicle over time. The improved method would be beneficial if it incorporated the interactive influences of multiple control surfaces and effectors operating at the same time. A preferred method would also be directly transportable across different configurations and airborne vehicle classes and would not require further development for application to new or revised vehicles.
SUMMARY OF THE INVENTION
The present invention is directed to a method to control and optimize the performance of an airborne vehicle. The method is measurement-based and is implemented for use in real-time. The method computes the influence that particular control effectors have on a specific performance objective. The method identifies the interactive effects of multiple control effectors and then uses the results to optimize the specific performance parameter. The method thus provides a coordinated set of control effector settings that maximize a specific performance objective.
A preferred embodiment of the method of the present invention includes the step of generating and applying excitation inputs to at least one control signal associated with at least one selected effector. The excitation inputs modify the airborne vehicle's state variables (i.e., the stability and control of the aircraft) and induce a response in the airborne vehicle reflected by a plurality of response signals. Preferably, each of the excitation inputs comprises a multi-term sinusoidal waveform, each term uniquely associated with a particular system parameter and having a unique frequency. A time domain response of each of the state variables, response signals, and control signals arising from the application of the excitation inputs to the control effectors is then measured. These time domain responses are then transformed into frequency domain models. The effectiveness derivatives are then identified from the frequency domain models of the response and control signals. These effectiveness derivatives represent the contribution that each of the one or more selected effectors has on a particular performance index of the airborne vehicle and are then used to derive a control effector setting for each of the one or more selected effectors. The selected performance index may then be optimized by adjusting the one or more selected effectors to its derived control effector setting. Additionally, vehicle stability and control
Hreha Mark A.
Schkolnik Gerard
Cuchlinski Jr. William A.
Mancho Ronnie
McDonell Douglas Corporation
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