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Hinge moment and flutter characteristics of flap- type controls - free oscillation tests of changes in control profile and control trailing- edge angle
Pontryagin optimal principle applied to minimum fuel satellite attitude control with two feedback controls
Linear multivariable control system design using modern control theory, and application to coupled core reactor control
Equivalence of optimal control problems and transformation of optimal control problems with compact control regions into Lagrange problems
Control law for double-gimbaled control moment gyros used for space vehicle attitude control
Overall engineering analyses on control computers for Apollo applications spacecraft control moment gyro
The basic principles of the control of TL ongincs are developed on .the basis of a quantitative investigation of the behavior of these behavior under various operating conditions with particular consideration of the simplifications pormissible in each case. Various possible means of control of jet engines are suggested and are illustrated by schematic designs.
There has been considerable interest over the past several years in applying feedback control methods to problems of structural acoustics. One problem of particular interest is the control of sound radiation from aircraft panels excited on one side by a turbulent boundary layer (TBL). TBL excitation appears as many uncorrelated sources acting on the panel, which makes it difficult to find a single reference signal that is coherent with the excitation. Feedback methods have no need for a reference signal, and are thus suited to this problem. Some important considerations for the structural acoustics problem include the fact that the required controller bandwidth can easily extend to several hundred Hertz, so a digital controller would have to operate at a few kilohertz. In addition, aircraft panel structures have a reasonably high modal density over this frequency range. A model based controller must therefore handle the modally dense system, or have some way to reduce the bandwidth of the problem. Further complicating the problem is the fact that the stiffness and dynamic properties of an aircraft panel can vary considerably during flight due to altitude changes resulting in significant resonant frequency shifts. These considerations make the tradeoff between robustness to changes in the system being controlled and controller performance especially important. Recent papers concerning the design and implementation of robust controllers for structural acoustic problems highlight the need to consider both performance and robustness when designing the controller. While robust control methods such as H1 can be used to balance performance and robustness, their implementation is not easy and requires assumptions about the types of uncertainties in the plant being controlled. Achieving a useful controller design may require many tradeoff studies of different types of parametric uncertainties in the system. Another approach to achieving robustness to plant changes is to make the controller adaptive. For example, a mathematical model of the plant could be periodically updated as the plant changes, and the feedback gains recomputed from the updated model. To be practical, this approach requires a simple plant model that can be updated quickly with reasonable computational requirements. A recent paper by the authors discussed one way to simplify a feedback controller, by reducing the number of actuators and sensors needed for good performance. The work was done on a tensioned aircraft-style panel excited on one side by TBL flow in a low speed wind tunnel. Actuation was provided by a piezoelectric (PZT) actuator mounted on the center of the panel. For sensing, the responses of four accelerometers, positioned to approximate the response of the first radiation mode of the panel, were summed and fed back through the controller. This single input-single output topology was found to have nearly the same noise reduction performance as a controller with fifteen accelerometers and three PZT patches. This paper extends the previous results by looking at how constrained layer damping (CLD) on a panel can be used to enhance the performance of the feedback controller thus providing a more robust and efficient hybrid active/passive system. The eventual goal is to use the CLD to reduce sound radiation at high frequencies, then implement a very simple, reduced order, low sample rate adaptive controller to attenuate sound radiation at low frequencies. Additionally this added damping smoothes phase transitions over the bandwidth which promotes robustness to natural frequency shifts. Experiments were conducted in a transmission loss facility on a clamped-clamped aluminum panel driven on one side by a loudspeaker. A generalized predictive control (GPC) algorithm, which is suited to online adaptation of its parameters, was used in single input-single output and multiple input-single output configurations. Because this was a preliminary look at the potential constrained layer damping for adaptive control, static feedback control with no online adaptation was used. Two configurations of CLD in addition to a bare panel configuration were studied. For each CLD configuration, two sensor arrangements for the feedback controller were compared. The first arrangement used fifteen accelerometers on the panel to estimate the responses of the first six radiation modes of the panel. The second sensor arrangement was simpler, using the summed responses of only four accelerometers to approximate the response of the first radiation mode of the panel. In all cases a PZT patch was mounted at the center of the panel for control input. The performance of the controller was quantified using the responses of the fifteen accelerometers on the panel to estimate radiated sound power. The paper begins with a brief discussion of the GPC algorithm and the experimental setup. The experimental results are discussed next, comparing the CLD and sensor configurations, followed by discussion and conclusions.
The need to return high mass payloads is driving the development of a new class of vehicles, Deployable Entry Vehicles (DEV) for which feasible and optimized control architectures have not been developed. The Pterodactyl project, seeks to advance the current state-of-the-art for entry vehicles by developing a design, test, and build capability for DEVs that can be applied to various entry vehicle configurations. This paper details the efforts on the NASA-funded Pterodactyl project to investigate multiple control techniques for the Lifting Nano-ADEPT (LNA) DEV. We design and implement multiple control architectures on the LNA and evaluate their performance in achieving varying guidance commands during entry.First we present an overview of DEVs and the Lifting Nano-ADEPT (LNA), along with the physical LNA configuration that influences the different control designs. Existing state-of-the-art for entry vehicle control is primarily propulsive as reaction control systems (RCS) are widely employed. In this work, we analyze the feasibility of using both propulsive control systems such as RCS to generate moments, and non-propulsive control systems such as aerodynamic control surfaces and internal moving mass actuations to shift the LNA center of gravity and generate moments. For these diverse control systems, we design different multi-input multi-output (MIMO) state-feedback integral controllers based on linear quadratic regulator (LQR) optimal control methods. The control variables calculated by the controllers vary, depending on the control system being utilized and the outputs to track for the controller are either the (i) bank angle or the (ii) angle of attack and sideslip angle as determined by the desired guidance trajectory. The LQR control design technique allows the relative allocation of the control variables through the choice of the weighting matrices in the cost index. Thus, it is easy to (i) specify which and how much of a control variable to use, and (ii) utilize one control design for different control architectures by simply modifying the choice of the weighting matrices.By providing a comparative analysis of multiple control systems, configurations, and performance, this paper and the Pterodactyl project as a whole will help entry vehicle system designers and control systems engineers determine suitable control architectures for integration with DEVs and other entry vehicle types.
The NASA-funded Pterodactyl project seeks to advance the state-of-the-art for varying entry vehicle types by developing unconventional guidance and control technologies for Deployable Entry Vehicles (DEVs) that can be applied to different entry vehicle configurations. Prior work by the authors [1–5] involved developing both traditional and novel integrated guidance and control solutions for a Pterodactyl Baseline Vehicle (PBV), a variant of an asymmetric DEV called the Lifting Nano ADEPT (LNA) [6]. In the prior studies, two different guidance schemes were designed and implemented for the PBV: (i) traditional bank angle guidance developed using the Fully Numerical Predictor-Corrector Entry Guidance (FNPEG) and (ii) novel angle of attack and sideslip (α - β) guidance developed using FNPEG with Uncoupled Range Control [4]. Using Linear Quadratic Regulator (LQR) optimal control methods with state-feedback integral control designs, these guidance trajectories were designed to be tracked using (i) a conventional propulsive entry vehicle control hardware architecture - reaction control systems (RCS) and (ii) novel non-propulsive entry vehicle control systems - aerodynamic flap control system (FCS) and moving mass control system (MMCS) [1]. The novel FCS and MMCS architectures were designed to track α - β guidance commands while the RCS was designed to track bank angle commands. It was discovered that the asymmetric DEV, the PBV, experienced a non-zero induced roll moment due to sideslip that the FCS and MMCS architectures had limited capability to trim out. These two architectures were designed to provide independent angle of attack and sideslip commands with limited consideration for roll moment generation to trim. As a result, for the PBV, the FCS and MMCS configurations as designed, were limited in providing the control authority needed to track an α - β guidance trajectory [1]. These results form the motivation for the work presented in this paper - utilizing an aerodynamic control system to track α - β guidance commands for a symmetric DEV with the expectation that a symmetric entry vehicle will have zero or significantly reduced roll moment due to sideslip that the FCS can handle when tracking an α - β guidance trajectory. To demonstrate the feasibility of a novel guidance and control architecture on a DEV, we utilize a symmetric DEV, the PBV-II, for (i) the novel α - β guidance development using FNPEG with Uncoupled Range Control and (ii) LQR control design using eight aerodynamic control surfaces. This paper demonstrates that the novel uncoupled α - β guidance tracking can be achieved using aerodynamic control surfaces on a symmetric deployable entry vehicle configuration.
Aircraft control systems are usually very reliable because of redundancy and multiple control surfaces. However, there are rare occasions when potentially disastrous flight control system failures do occur. At such times, the use of appropriate modulation of engine thrust to stabilize the aircraft may be the only chance of survival for the people aboard. In several cases where complete loss of control systems has occurred in multi-engine aircraft, pilots used the propulsion system to regain limited control of the aircraft with various degrees of success. In order to evaluate the feasibility of using only engine thrust modulation for emergency backup flight control, the NASA Dryden Flight Research Center has been conducting a series of analytical studies and flight tests on several different types of aircraft in a propulsion controlled aircraft (PCA) program. Simulation studies have included B-720, B-727, MD-11, C-402, C-17, F-18, and F-15, and flight tests have included B-747, B-777, MD-11, T-39, Lear 24, F-18, F-15, T-38, and PA-30. One objective was to determine the degree of control available with manual manipulation (open-loop) of the engine throttles. Flight tests and simulations soon showed that a closed loop controller could improve the chances of making a safe runway landing. The major work to date has concentrated on three aircraft (F-15, F-18, and the MD-11). Successful landings using PCA controllers were performed on the F-15 and MD-11 without the use of control surfaces. During the course of the research, some unique challenges have been identified. Compared to the conventional flight control surfaces, the engines are slow and have limited control effectiveness. Hence the ability of the system to promptly respond to aerodynamic changes is limited. Consequently, many nonlinear effects, which are easily accommodated by a conventional flight control system, become significant issues in the design of an effective controller when the engines are used as the only control effectors. A number of nonlinear behaviors observed during PCA flight tests, which are not meant to be exhaustive, are reviewed in the next section.
As a portion of the Benchmark Models Program at NASA Langley, a simple generic model was developed for active controls research and was called BACT for Benchmark Active Controls Technology model. This model was based on the previously-tested Benchmark Models rectangular wing with the NACA 0012 airfoil section that was mounted on the Pitch and Plunge Apparatus (PAPA) for flutter testing. The BACT model had an upper surface spoiler, a lower surface spoiler, and a trailing edge control surface for use in flutter suppression and dynamic response excitation. Previous experience with flutter suppression indicated a need for measured control surface aerodynamics for accurate control law design. Three different types of flutter instability boundaries had also been determined for the NACA 0012/PAPA model, a classical flutter boundary, a transonic stall flutter boundary at angle of attack, and a plunge instability near M = 0.9. Therefore an extensive set of steady and control surface oscillation data was generated spanning the range of the three types of instabilities. This information was subsequently used to design control laws to suppress each flutter instability. There have been three tests of the BACT model. The objective of the first test, TDT Test 485, was to generate a data set of steady and unsteady control surface effectiveness data, and to determine the open loop dynamic characteristics of the control systems including the actuators. Unsteady pressures, loads, and transfer functions were measured. The other two tests, TDT Test 502 and TDT Test 5 18, were primarily oriented towards active controls research, but some data supplementary to the first test were obtained. Dynamic response of the flexible system to control surface excitation and open loop flutter characteristics were determined during Test 502. Loads were not measured during the last two tests. During these tests, a database of over 3000 data sets was obtained. A reasonably extensive subset of the data sets from the first two tests have been chosen for Test Cases for computational comparisons concentrating on static conditions and cases with harmonically oscillating control surfaces. Several flutter Test Cases from both tests have also been included. Some aerodynamic comparisons with the BACT data have been made using computational fluid dynamics codes at the Navier-Stokes level (and in the accompanying chapter SC). Some mechanical and active control studies have been presented. In this report several Test Cases are selected to illustrate trends for a variety of different conditions with emphasis on transonic flow effects. Cases for static angles of attack, static trailing-edge and upper-surface spoiler deflections are included for a range of conditions near those for the oscillation cases. Cases for trailing-edge control and upper-surface spoiler oscillations for a range of Mach numbers, angle of attack, and static control deflections are included. Cases for all three types of flutter instability are selected. In addition some cases are included for dynamic response measurements during forced oscillations of the controls on the flexible mount. An overview of the model and tests is given, and the standard formulary for these data is listed. Some sample data and sample results of calculations are presented. Only the static pressures and the first harmonic real and imaginary parts of the pressures are included in the data for the Test Cases, but digitized time histories have been archived. The data for the Test Cases are also available as separate electronic files.
The simulation developments for use in dynamics and control analysis during boost from liftoff to orbit insertion are reported. Also included are wind response studies of the NR-GD 161B/B9T delta wing booster/delta wing orbiter configuration, the MSC 036B/280 inch solid rocket motor configuration, the MSC 040A/L0X-propane liquid injection TVC configuration, the MSC 040C/dual solid rocket motor configuration, and the MSC 049/solid rocket motor configuration. All of the latest math models (rigid and flexible body) developed for the MSC/GD Space Shuttle Functional Simulator, are included.
An investigation of the properties important for the design of stochastic adaptive controllers for the higher harmonic control of helicopter vibration is presented. Three different model types are considered for the transfer relationship between the helicopter higher harmonic control input and the vibration output: (1) nonlinear; (2) linear with slow time varying coefficients; and (3) linear with constant coefficients. The stochastic controller formulations and solutions are presented for a dual, cautious, and deterministic controller for both linear and nonlinear transfer models. Extensive simulations are performed with the various models and controllers. It is shown that the cautious adaptive controller can sometimes result in unacceptable vibration control. A new second order dual controller is developed which is shown to modify the cautious adaptive controller by adding numerator and denominator correction terms to the cautious control algorithm. The new dual controller is simulated on a simple single-control vibration example and is found to achieve excellent vibration reduction and significantly improves upon the cautious controller.
Traditional control theory is well-developed mainly for linear control situations. In non-linear cases there is no general method of generating a good control, so we have to rely on the ability of the experts (operators) to control them. If we want to automate their control, we must acquire their knowledge and translate it into a precise control strategy. The experts' knowledge is usually represented in non-numeric terms, namely, in terms of uncertain statements of the type 'if the obstacle is straight ahead, the distance to it is small, and the velocity of the car is medium, press the brakes hard'. Fuzzy control is a methodology that translates such statements into precise formulas for control. The necessary first step of this strategy consists of assigning membership functions to all the terms that the expert uses in his rules (in our sample phrase these words are 'small', 'medium', and 'hard'). The appropriate choice of a membership function can drastically improve the quality of a fuzzy control. In the simplest cases, we can take the functions whose domains have equally spaced endpoints. Because of that, many software packages for fuzzy control are based on this choice of membership functions. This choice is not very efficient in more complicated cases. Therefore, methods have been developed that use neural networks or generic algorithms to 'tune' membership functions. But this tuning takes lots of time (for example, several thousands iterations are typical for neural networks). In some cases there are evident physical reasons why equally space domains do not work: e.g., if the control variable u is always positive (i.e., if we control temperature in a reactor), then negative values (that are generated by equal spacing) simply make no sense. In this case it sounds reasonable to choose another scale u' = f(u) to represent u, so that equal spacing will work fine for u'. In the present paper we formulate the problem of finding the best rescaling function, solve this problem, and show (on a real-life example) that after an optimal rescaling, the un-tuned fuzzy control can be as good as the best state-of-art traditional non-linear controls.
This viewgraph presentation describes the general methodology used to apply Honywell's Multi-Application Control (MACH) and the specific application to the F-18 High Angle-of-Attack Research Vehicle (HARV) including piloted simulation handling qualities evaluation. The general steps include insertion of modeling data for geometry and mass properties, aerodynamics, propulsion data and assumptions, requirements and specifications, e.g. definition of control variables, handling qualities, stability margins and statements for bandwidth, control power, priorities, position and rate limits. The specific steps include choice of independent variables for least squares fits to aerodynamic and propulsion data, modifications to the management of the controls with regard to integrator windup and actuation limiting and priorities, e.g. pitch priority over roll, and command limiting to prevent departures and/or undesirable inertial coupling or inability to recover to a stable trim condition. The HARV control problem is characterized by significant nonlinearities and multivariable interactions in the low speed, high angle-of-attack, high angular rate flight regime. Systematic approaches to the control of vehicle motions modeled with coupled nonlinear equations of motion have been developed. This paper will discuss the dynamic inversion approach which explicity accounts for nonlinearities in the control design. Multiple control effectors (including aerodynamic control surfaces and thrust vectoring control) and sensors are used to control the motions of the vehicles in several degrees-of-freedom. Several maneuvers will be used to illustrate performance of MACH in the high angle-of-attack flight regime. Analytical methods for assessing the robust performance of the multivariable control system in the presence of math modeling uncertainty, disturbances, and commands have reached a high level of maturity. The structured singular value (mu) frequency response methodology is presented as a method for analyzing robust performance and the mu-synthesis method will be presented as a method for synthesizing a robust control system. The paper concludes with the author's expectations regarding future applications of robust nonlinear multivariable controls.
The U.S. Army and NASA are currently developing the Rotorcraft Aircrew Systems Concepts Airborne Laboratory (RASCAL) at the Ames Research Center. RASCAL, shown in Figure 1, is a UH-60, which is being modified in a phased development program to have a research fly-by-wire flight control system, and an advanced navigation research platform. An important part of the flight controls and handling qualities research on RASCAL will be an FCS design for the aircraft to achieve high bandwidth control responses and disturbance rejection characteristics. Initially, body states will be used as feedbacks, but research into the use of rotor states will also be considered in later stages to maximize agility and maneuverability. In addition to supporting flight controls research, this FCS design will serve as the inflight simulation control law to support basic handling qualities, guidance, and displays research. Research in high bandwidth controls laws is motivated by the desire to improve the handling qualities in aggressive maneuvering and in severely degraded weather conditions. Naturally, these advantages will also improve the quality of the model following, thereby improving the inflight simulation capabilities of the research vehicle. High bandwidth in the control laws provides tighter tracking allowing for higher response bandwidths which can meet handling qualities requirements for aggressive maneuvering. System sensitivity is also reduced preventing variations in the response from the vehicle due to changing flight conditions. In addition, improved gust rejection will result from this reduced sensitivity. The gust rejection coupled with a highly stable system will make more precise maneuvering and pointing possible in severely degraded weather conditions. The difficulty in achieving higher bandwidths from the control laws in the feedback and in the responses arises from the complexity of the models that are needed to produce a satisfactory design. In this case, high quality models that include rotor dynamics in a physically meaningful context must be available. A non-physical accounting of the rotor, such as lumping the effect as a time delay, is not likely to produce the desired results. High order simulation models based on first principals are satisfactory for the initial design phase in order to work out the control law design concept and get an initial set of gains. These models, however, have known deficiencies, which must be resolved in the final control law design. The error in the pitch-roll cross coupling is one notable deficiency that even sophisticated rotorcraft models including complex wake aerodynamics have yet to capture successfully. This error must be accounted for to achieve the desired decoupling. The approach to design the proposed inflight simulation control law is based on using a combination of simulation and identified models. The linear and nonlinear higher order models were used to develop an explicit model following control structure. This structure was developed to accommodate the design of control laws compliant to many of the quantitative requirements in ADS-33C. Furthermore, it also allows for control law research using rotor-state feedback and other design methodologies such as Quantitative Feedback and H-Infinity. Final gain selection will be based on higher order identified models which include rotor degrees of freedom.
During the assembly flights of the Space Station Freedom (SSF), the Orbiter will either dock with the SSF and retract to the final berthed position, or will grapple the SSF using the Shuttle Remote Manipulator System (SRMS) and maneuver the SRMS coupled vehicles to their final berthed position. The SRMS method is expected to take approximately one to one and a half hours to complete and require periodic attitude corrections by either the Orbiter or the SSF reaction control system (RCS) or continuous control by a control moment gyro (CMG) system with RCS desaturation as required. Free drift of the attached vehicles is not currently thought to be acceptable because the desired system attitude will quickly deteriorate due to unbalanced gravity gradient and aerodynamic torques resulting in power generation problems, thermodynamic control problems, and communications problems. This paper deals with the simulation and control of the SRMS during trunnion/latch interaction dynamics and during RCS maneuvers. The SRMS servo drive joints have highly non-linear elastic characteristics which tend to degrade sensitive control strategies. In addition the system natural frequencies are extremely low and depend on the drive joint deflections and SRMS geometric position. The lowest mean period of oscillation for the Orbiter/SRMS/SSF(MB6) system in brakes hold mode positioned near the final berthed position is approximately 120 seconds. A detailed finite element model of the SRMS has been developed and used in a newly developed SRMS systems dynamics simulation to investigate the non-linear transient response dynamics of the Orbiter/SRMS/SSF systems. The present SRMS control strategy of brakes only recommended by the Charles Draper Labs is contrasted with a robust controller developed by the authors. The robust controller uses an optimal inear quadratic regulator (LQR) to optimally place the closed-loop poles of a multivariable continuous-time system within the common region of an open sector with the sector angle plus or minus 45 degrees from the negative real axis, and the left-hand side of a parallel to the imaginary axis in the complex s-plane. This guarantees that the critical damping ratio for the desired control modes is equal to or in excess of 0.707. The matrix sign function is used for solving the Riccati equations which appear in the controller design procedure. Fast and stable algorithms have recently been developed for the computation of the matrix sign function. Simulation results are given which demonstrate the potential CSI involvement for the current SRMS control system and the proposed control system.