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NASA Armstrong continues its legacy of exciting work in the area of Dynamics and Control of advanced vehicle concepts. This presentation describes Armstrongs research in control of flexible structures, peak seeking control and adaptive control in the Spring of 2015.
This presentation will touch topics, including but not limited to, the objectives and challenges of flight dynamics and controls that deal with the pilot and the cockpit's technology, the flight dynamics and controls discipline tasks, and the full envelope of flight dynamics modeling. In addition, the LCTR 7x10-ft wind tunnel test will also be included along with the optimal trajectories for noise abatement and its investigations on handling quality. Furthermore, previous experiments and their complying results will also be discussed.
This paper describes some results of an attitude dynamics and control study for a solar sailing vehicle. This type of vehicle is currently under study and evaluation at JPL and has very high potential for interplanetary missions in and beyond the 1980s. Crucial to the success of such a vehicle would be the performance of its onboard attitude control system. Because of the vehicle's large size and its flexibility, vehicle deformations may have a potential for causing a degradation in vehicle performance. It may therefore be necessary for the control system to take into account the vehicle deformations as well as its rigid-body motions. Distributed parameter system analysis techniques are used in the paper to study certain fundamental aspects of such a control system for the sail vehicle. The techniques can, however, be more generally applicable to other large flexible vehicles.
Engine dynamics and controls data are presented for a ram-jet engine which was operated in a free-jet facility at a Mach number of 2.76 and altitudes from 68,000 to 82,000 feet. The predominant engine dynamic characteristics was dead time, with values ranging from 0.018 to 0.053 sec. The control systems were designed to hold a constant ratio of a diffuser static pressure to an inlet-cone reference static pressure. Response times and percent overshoot to fuel-flow disturbances that would have caused subcritical operation without control are included for a wide range of control settings. For all settings, the control response was fast enough (0.08 to 0.5 sec) to prevent subcritical operation.
The results of a fixed-base simulator study of the effects of variable longitudinal control-system dynamics on pilot opinion are presented and compared with flight-test data. The control-system variables considered in this investigation included stick force per g, time constant, and dead-band, or stabilizer breakout force. In general, the fairly good correlation between flight and simulator results for two pilots demonstrates the validity of fixed-base simulator studies which are designed to complement and supplement flight studies and serve as a guide in control-system preliminary design. However, in the investigation of certain problem areas (e.g., sensitive control-system configurations associated with pilot- induced oscillations in flight), fixed-base simulator results did not predict the occurrence of an instability, although the pilots noted the system was extremely sensitive and unsatisfactory. If it is desired to predict pilot-induced-oscillation tendencies, tests in moving-base simulators may be required. It was found possible to represent the human pilot by a linear pilot analog for the tracking task assumed in the present study. The criterion used to adjust the pilot analog was the root-mean-square tracking error of one of the human pilots on the fixed-base simulator. Matching the tracking error of the pilot analog to that of the human pilot gave an approximation to the variation of human-pilot behavior over a range of control-system dynamics. Results of the pilot-analog study indicated that both for optimized control-system dynamics (for poor airplane dynamics) and for a region of good airplane dynamics, the pilot response characteristics are approximately the same.
Model accounting for adaptation of well-trained human controller to sudden changes in controlled process dynamics of compensatory tracking system
In recent years, NASA has invested in key activities in the areas of flight controls, handling qualities and operations of rotorcraft for civilian applications. More specifically, the flight dynamics and control discipline has focused on analyzing the unique flight control and handling qualities challenges of large rotary wing vehicles anticipated for future passenger service, and examining the effect of control system augmentation on handling qualities for current civilian helicopters in order to improve safety and reduce accident rates. This paper highlights two recent research efforts in these areas. The first is an examination of flight control and handling qualities aspects of large rotorcraft. A series of experiments were performed in the large-motion Vertical Motion Simulator at NASA Ames Research Center to quantify the effects of vehicle size on flight control requirements and piloted handling qualities. These experiments used a large tilt-rotor concept (~100 passengers) to also investigate the control augmentation required to obtain Level 1 handling qualities for a vehicle of this size. The second is an examination of the effect of control system augmentation on handling qualities for current civil rotorcraft, like those currently used for Emergency Medical Service type operations. Many current civilian helicopters have rate response type control systems and little or no control system augmentation, although current technologies allow helicopters to be fitted with stability augmentation systems, either as standard equipment or aftermarket options. A simulation experiment was conducted in the Vertical Motion Simulator to quantify the effects of advanced control modes available with a partial authority stability augmentation system on task performance and handling qualities in both good and degraded visual conditions. In addition to providing an overview of the rotary wing flight dynamics and controls research at NASA, this paper will provide an overview of these two research activities along with key results and conclusions.
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In recent years, NASA has invested in key activities in the areas of flight controls, handling qualities and operations of rotorcraft for civilian applications. More specifically, the flight dynamics and control discipline has focused on analyzing the unique flight control and handling qualities challenges of large rotary wing vehicles anticipated for future passenger service, and examining the effect of control system augmentation on handling qualities for current civilian helicopters in order to improve safety and reduce accident rates. This paper highlights two recent research efforts in these areas. The first is an examination of flight control and handling qualities aspects of large rotorcraft. A series of experiments were performed in the large-motion Vertical Motion Simulator at NASA Ames Research Center to quantify the effects of vehicle size on flight control requirements and piloted handling qualities. These experiments used a large tilt-rotor concept (~100 passengers) to also investigate the control augmentation required to obtain Level 1 handling qualities for a vehicle of this size. The second is an examination of the effect of control system augmentation on handling qualities for current civil rotorcraft, like those currently used for Emergency Medical Service type operations. Many current civilian helicopters have rate response type control systems and little or no control system augmentation, although current technologies allow helicopters to be fitted with stability augmentation systems, either as standard equipment or aftermarket options. A simulation experiment was conducted in the Vertical Motion Simulator to quantify the effects of advanced control modes available with a partial authority stability augmentation system on task performance and handling qualities in both good and degraded visual conditions. In addition to providing an overview of the rotary wing flight dynamics and controls research at NASA, this paper will provide an overview of these two research activities along with key results and conclusions.
An investigation of feel system and flight control system dynamics on lateral flying qualities was conducted using the variable stability USAF NT-33 aircraft. Experimental variations in feel system natural frequency, force-deflection gradient, control system command architecture type, flight control system filter frequency, and control system delay were made. The experiment data include pilot ratings using the Cooper-Harper (1969) rating scale, pilot comments, and tracking performance statistic. Three test pilots served as evaluators. The data indicate that as the feel system natural frequency is reduced lateral flying qualities degrade. At the slowest feel system frequency, the closed-loop response becomes nonlinear with a 'bobweight' effect apparent in the feel system. Feel system influences were essentially independent of the control system architecture. The flying qualities influence due to the feel system was different than when the identical dynamic systenm was used as a flight control system element.
In this paper, we present some ideas regarding the modeling, dynamics and control aspects of granular spacecraft. Granular spacecraft are complex multibody systems composed of a spatially disordered distribution of a large number of elements, for instance a cloud of N grains in orbit, with N greater than 10(exp 3). These grains can be large (Cubesat-size) or small (mm-size), and can be active, i.e., a fully equipped vehicle capable sensing their own position and attitude, and enabled with propulsion means, or entirely passive. The ultimate objective would be to study the behavior of the single grains and of large ensembles of grains in orbit and to identify ways to guide and control the shape of a cloud composed of these grains so that it can perform a useful function in space, for instance, as an element of an optical imaging system for astrophysical applications. This concept, in which the aperture does not need to be continuous and monolithic, would increase the aperture size several times compared to large NASA observatories such as ATLAST, allowing for a true Terrestrial Planet Imager that would be able to resolve exo-planet details and do meaningful spectroscopy on distant world. In the paper, we address the modeling and autonomous operation of a distributed assembly (the cloud) of large numbers of highly miniaturized space-borne elements (the grains). A multi-scale, multi-physics model is proposed of the dynamics of the cloud in orbit, as well as a control law for cloud shape maintenance, and preliminary simulation studies yield an estimate of the computational effort, indicating a scale factor of approximately N(exp 1.4) as a function of the number of grains. A granular spacecraft can be defined as a collection of a large number of space-borne elements (in the 1000s) designed and controlled such that a desirable collective behavior emerges, either from the interactions among neighboring grains, and/or between the grains and the environment. In this paper, each grain is considered to be a highly miniaturized spacecraft which has limited size and mass, hence it has limited actuation, limited propulsive capability, limited power, limited sensing, limited communication, limited computational resources, limited range of motion, limited lifetime, and may be expendable. The modeling and dynamics of clouds of vehicles is more challenging than with conventional vehicles because we are faced with a probabilistic vehicle composed of a large number of physically disconnected vehicles. First, different scales of motion occur simultaneously in a cloud: translations and rotations of the cloud as a whole (macro-dynamics), relative rotation and translation of one cloud member with respect to another (meso-dynamics), and individual cloud member dynamics (micro-dynamics). Second, the control design needs to be tolerant of the system complexity, of the system architecture (centralized vs. decentralized large scale system control) as well as robust to un-modeled dynamics and noise sources. Figure 1, top left, shows the kinematic parameters of a 1000 element cloud in orbit. The motion of the system is described with respect to a local vertical-local horizontal (LV-LH) orbiting reference frame (x,y,z)=F(sub ORF) of origin O(sub ORF) which rotates with mean motion omega and orbital semi-major axis R(sub 0). The orbital geometry at the initial time is defined in terms of its six orbital elements, and the orbital dynamics equation for point O(sub ORF) is propagated forward in time under the influence of the gravitational field of the primary and other external perturbations, described below. The origin of this frame coincides with the initial position of the center of mass of the system, and the coordinate axes are z along the local vertical, x toward the flight direction, and y in the orbit normal direction. The assumptions we used to model the dynamics are as follows: 1) The inertial frame is fixed at Earth's center. 2) The orbiting Frame ORF follows Keplerian orbit. 3) the cloud system dynamics is referred to ORF. 4) the attitude of each grain uses the principal body frame as body fixed frame. 5) the atmosphere is assumed to be rigidly rotating with the Earth. Regarding the grains forming the cloud: 1) each grain is modeled as a rigid body; 2) a simple attitude estimator provides attitude estimates, 3) a simple guidance logic commands the position and attitude of each grain, 4) a simple local feedback controller based on PD control of local states is used to stabilize the attitude of the vehicle. Regarding the cloud: 1) the cloud as a whole is modeled as an equivalent rigid body in orbit, and 2) an associated graph establishes agent connectivity and enables coupling between modes of motion at the micro and macro scales; 3) a simple guidance and estimation logic is modeled to estimate and command the attitude of this equivalent rigid body; 4) a cloud shape maintenance controller is based on the dynamics of a stable virtual truss in the orbiting frame. Regarding the environmental perturbations acting on the cloud: 1) a non-spherical gravity field including JO (Earth's spherical field) zonal component, J2 (Earth's oblateness) and J3 zonal components is implemented; 2) atmospheric drag is modeled with an exponential model; 3) solar pressure is modeled assuming the Sun is inertially fixed; and 4) the Earth's magnetic field is model using an equivalent dipole model. The equations of motion are written in a referential system with respect to the origin of the orbiting frame and the state is propagated forward in time using an incremental predictor-corrector scheme. A representative cloud with varying number of grains is simulated to identify the limitations in computation time as the number of grains grows. We derive a control law to track a desired surface in the ORF (equivalently to maintain a reference cloud shape) by defining an error from a desired surface shape, and designing a control law that is exponentially stable and reduces the tracking error to zero. Figure 1 (top right) shows a comparison of various requirements for simulation of single spacecraft vs. granular spacecraft, indicating the high degree of complexity that needs to be taken into consideration. The ORF components of control force required by one of the grains is, for this particular case, in the micro-Newton range. However, no attempt has been made yet to reconfigure (or re-orient) the cloud configuration internally, for which forces in the milli-Newton level are expected, depending on the time required to do the reconfiguration. Figure 1, bottom, shows the computation time as a function of the number of grains, indicating an order N(exp 1.43) scaling on a 8 Gb, 1067 MHz RAM MacOSX computer with a 3.06 GHz Intel Core 2 Duo processor. With this metric, the same simulation for a system of N=1000 grains would take 5.4 hours, and 146 hours (i.e., 6 days) for a system with N=10,000 grains. Therefore, efficient ways to simulate this complex system, where not only the time scales of natural system dynamics, but also the sampling times of the Guidance, Navigation, and Control are included, remain to be explored. Additional details on the cloud modeling, dynamics, and control will be described in the paper.
A large-angle, flexible, multi-body, dynamic modelling capability has been developed to help validate numerical simulations of the dynamic motion and control forces which occur during berthing of Space Station Freedom to the Shuttle Orbiter in the early assembly flights. This paper outlines the dynamics and control of the Station, the attached Shuttle Remote Manipulator System, and the Orbiter. The simulation tool developed for the analysis is described and the results of two simulations are presented. The first is a simulated maneuver from a gravity-gradient attitude to a torque equilibrium attitude using the Station reaction control jets. The second simulation is the berthing of the Station to the Orbiter with the Station control moment gyros actively maintaining an estimated torque equilibrium attitude. The influence of the elastic dynamic behavior of the Station and of the Remote Manipulator System on the attitude control of the Station/Orbiter system during each maneuver was investigated. The flexibility of the Station and the arm were found to have only a minor influence on the attitude control of the system during the maneuvers.
Dynamic characteristics of airframe structures are expected to vary as aircraft flight conditions change. Accurate knowledge of the changing dynamic characteristics is crucial to enhancing the performance of the active noise control system using feedback control. This research investigates the development of an adaptive, on-line state estimator using a neural network concept to conduct active noise control. In this research, an algorithm has been developed that can be used to estimate displacement and velocity responses at any locations on the structure from a limited number of acceleration measurements and input force information. The algorithm employs band-pass filters to extract from the measurement signal the frequency contents corresponding to a desired mode. The filtered signal is then used to train a neural network which consists of a linear neuron with three weights. The structure of the neural network is designed as simple as possible to increase the sampling frequency as much as possible. The weights obtained through neural network training are then used to construct the transfer function of a mode in z-domain and to identify modal properties of each mode. By using the identified transfer function and interpolating the mode shape obtained at sensor locations, the displacement and velocity responses are estimated with reasonable accuracy at any locations on the structure. The accuracy of the response estimates depends on the number of modes incorporated in the estimates and the number of sensors employed to conduct mode shape interpolation. Computer simulation demonstrates that the algorithm is capable of adapting to the varying dynamic characteristics of structural properties. Experimental implementation of the algorithm on a DSP (digital signal processing) board for a plate structure is underway. The algorithm is expected to reach the sampling frequency range of about 10 kHz to 20 kHz which needs to be maintained for a typical active noise control application.
Flight dynamics and control analysis of Centaur vehicle
An investigation of the dynamics and control problems specifically related to the Satellite Power System (SPS), to assess performance of selected control concepts, and to identify and initiate development of advanced control technology that would enhance feasibility and performance of the SPS system was made. The initial stages of the study are reported.
The recent Tethered Satellite System-1 (TSS-1) mission has provided a wealth of data concerning the dynamics of tethered systems in space and has demonstrated the effectiveness of operational techniques designed to control these dynamics. In this paper, we review control techniques developed for managing tether dynamics, and discuss the results of using these techniques for the Tethered Satellite System's maiden flight on STS-46. In particular, the flight results of controlling libration dynamics, string dynamics, and slack tether are presented. These results show that tether dynamics can be safely managed. The overall stability of the system was found to be surprisingly good even at relatively short tether lengths. In fact, the system operated in passive mode at a tether length of 256 meters for over 9 hours. Only monitoring of the system was required during this time. Although flight anomalies prevented the planned deployment to 20 km, the extended operations at shorter tether lengths have proven the viability of using tethers in space. These results should prove invaluable in preparing for future missions with tethered objects in space.
Work related to the dynamics of tether connected orbiting systems and their control during deployment, operational, and retrieval phases is briefly reviewed. In particular, attention is given to the modeling of tether dynamics and control, end bodies tethered at point masses, deployment dynamics and tension control, and thruster and offset control. Directions of future efforts aimed at gaining a better understanding of the performance of tethered systems are outlined.
In addition to cooperative dynamic control, the system incorporates real time vision feedback, a novel programming technique, and a graphical high level user interface. By focusing on the vertical integration problem, not only these subsystems are examined, but also their interfaces and interactions. The control system implements a multi-level hierarchical structure; the techniques developed for operator input, strategic command, and cooperative dynamic control are presented. At the highest level, a mouse-based graphical user interface allows an operator to direct the activities of the system. Strategic command is provided by a table-driven finite state machine; this methodology provides a powerful yet flexible technique for managing the concurrent system interactions. The dynamic controller implements object impedance control; an extension of Nevill Hogan's impedance control concept to cooperative arm manipulation of a single object. Experimental results are presented, showing the system locating and identifying a moving object catching it, and performing a simple cooperative assembly. Results from dynamic control experiments are also presented, showing the controller's excellent dynamic trajectory tracking performance, while also permitting control of environmental contact force.