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Systems, methods, and devices of the present invention enable airborne Doppler Wind LIDAR system measurements and INS/GPS measurements to be combined to estimate wind parameters and compensate for instrument misalignment. In a further embodiment, the wind speed and wind direction may be computed based on two orthogonal line-of-sight LIDAR returns.
Optimum design of compensating feedback control system with parameter variations
Soil moisture and vegetation optical depth (VOD; related to vegetation water content) retrieved from SMAP and SMOS satellites are widely used for a range of hydrosphere and biosphere applications. However, while soil moisture has been globally well-validated, VOD validation has been sparse. Furthermore, simultaneously retrieval of these parameters results in uncertainties both individually in soil moisture and VOD retrievals as well as in compensation between the parameters. Here, we show global locations where soil moisture and VOD retrievals will have lower uncertainty, based on complementary brightness temperature information content and signal-to-noise ratio metrics. In these same locations, we show that error still propagates more into VOD. However, using VOD regularization algorithms, this error is greatly reduced, especially at sub-weekly timescales where algorithmic error can be most apparent. Despite these regularization approaches that reduce errors, there are yet vast differences in available global regularized retrievals originating from different algorithmic choices.
A method for synthesizing digital active flutter suppression controllers using the concept of optimal output feedback is presented. A recently developd convergent algorithm is employed to determine constrained control law parameters that minimize an infinite-time discrete quadratic performance index. Low-order compensator dynamics are included in the control law and the compensator parameters are computed along with the output feedback gain as part of the optimization process. An input noise adjustment procedure is used to improve the stability margins of the digital active flutter controller. Results from investigations into sample rate variation, prefilter pole variation, and effects of varying flight condtions are discussed. The study indicates that a digital control law which accommodates computation delay can stabilize the wing with reasonable rms performance and adequate stability margins.
The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.
Precision analog signal conditioning electronics have been developed for wind tunnel model attitude inertial sensors. This application requires low-noise, stable, microvolt-level DC performance and a high-precision buffered output. Capacitive loading of the operational amplifier output stages due to the wind tunnel analog signal distribution facilities caused regenerative oscillation and consequent rectification bias errors. Oscillation suppression techniques commonly used in audio applications were inadequate to maintain the performance requirements for the measurement of attitude for wind tunnel models. Feedback control theory is applied to develop a suppression technique based on a known compensation (snubber) circuit, which provides superior oscillation suppression with high output isolation and preserves the low-noise low-offset performance of the signal conditioning electronics. A practical design technique is developed to select the parameters for the compensation circuit to suppress regenerative oscillation occurring when typical shielded cable loads are driven.
Control system design is considered for attitude control and vibration suppression of flexible space structures. The problem addressed is that of controlling both the zero-frequency rigid-body modes and the elastic modes. Model-based compensators, which employ observers tuned to the plant parameters, are first investigated. Such compensators are shown to generally exhibit high sensitivity to the knowledge of the parameters, especially the elastic mode frequencies. To overcome this problem a class of dynamic dissipative compensators is next proposed, which robustly stabilize the plant in the presence of unmodeled dynamics and parametric uncertainties. An analytical proof of robust stability is given, and a method of implementing the controller as a strictly proper compensator is given. Methods of designing such controllers to obtain optimal performance and robust stability are presented. Numerical and experimental results of application of the methods are presented, which indicate that dynamic dissipative controllers can simultaneously provide excellent performance and robustness.
The approximation of optimal discrete-time linear quadratic Gaussian (LQG) compensators for distributed parameter control systems with boundary input and unbounded measurement is considered. The approach applies to a wide range of problems that can be formulated in a state space on which both the discrete-time input and output operators are continuous. Approximating compensators are obtained via application of the LQG theory and associated approximation results for infinite dimensional discrete-time control systems with bounded input and output. Numerical results for spline and modal based approximation schemes used to compute optimal compensators for a one dimensional heat equation with either Neumann or Dirichlet boundary control and pointwise measurement of temperature are presented and discussed.
The approximation of optimal discrete-time linear quadratic Gaussian (LQG) compensators for distributed parameter control systems with boundary input and unbounded measurement is considered. The approach applies to a wide range of problems that can be formulated in a state space on which both the discrete-time input and output operators are continuous. Approximating compensators are obtained via application of the LQG theory and associated approximation results for infinite dimensional discrete-time control systems with bounded input and output. Numerical results for spline and modal based approximation schemes used to compute optimal compensators for a one-dimensional heat equation with either Neumann or Dirichlet boundary control and pointwise measurement of temperature are presented and discussed.
The development of hardware to support biomedical experimentation and operations in the Skylab vehicle presented unique technical problems. Designs were required to enable the accurate measurement of many varied physiological parameters and to compensate for zero g such that uninhibited equipment operation would be possible. Because of problems that occurred during the orbital workshop launch, special tests were run and new equipment was designed and built for use by the first Skylab crew. Design concepts used in the development of hardware to support cardiovascular, pulmonary, vestibular, body, and specimen mass measuring experiments are discussed. Additionally, major problem areas and the corresponding design solutions, as well as knowledge gained that will be pertinent for future life sciences hardware development, are presented.
A theory and the associated numerical technique are developed for an iterative design improvement of the compensation for linear, time-invariant control systems with multiple inputs and multiple outputs. A strict constraint algorithm is used in obtaining a solution of the specified constraints of the control design. The result of the research effort is the multiple input, multiple output Compensator Improvement Program (CIP). The objective of the Compensator Improvement Program is to modify in an iterative manner the free parameters of the dynamic compensation matrix so that the system satisfies frequency domain specifications. In this exposition, the underlying principles of the multivariable CIP algorithm are presented and the practical utility of the program is illustrated with space vehicle related examples.
The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described.
As part of JPL's ocean data assimilation effort to study ocean circulation and seasonal-interannual climate variability, sea level anomaly observed by TOPEX altimeter, together with sea surface temperature and wind stress data, are assimilated into a simple coupled ocean atmosphere model of the tropical Pacific. Model-data consistency is examined. Impact of the assimilation (as initialization) on El Nino Southern Oscillation (ENSO) forecasts is evaluated. The coupled model consists of a shallow water component with two baroclinic modes, an Ekman shear layer, a simplified mixed-layer temperature equation, and a statistical atmosphere based on dominant correlations between historical surface temperature and wind stress anomaly data. The adjoins method is used to fit the coupled model to the data over various six-month periods from late 1996 to early 1998 by optimally adjusting the initial state, model parameters, and basis functions of the statistical atmosphere. On average, the coupled model can be fitted to the data to approximately within the data and representation errors (5 cm, 0.5 C, and 10 sq m/sq m for sea level, surface temperature, and pseudo wind stress anomalies, respectively). The estimated fields resemble observed spatio-temporal structure reasonably well. Hindcasts/forecasts of the 1997/1998 El Nino initialized from forced estimated ocean states and parameters are much more realistic than those simply initialized from ocean states (see figure below). In particular, the ability of the model to produce significant warming beyond the initial state is dramatically improved. Parameter estimation, which compensates for some model errors, is found to be important to obtaining better fits of the model to data and to improving forecasts.
The beam divergence of a 30-centimeter diameter thruster with dished grids was calculated from current densities measured with a probe rake containing seventeen planar molybdenum probes. The measured data were analyzed as a function of a number of parameters. The most sensitive parameters were the amount of compensation of the accelerator grid and the ratio of net to total accelerating voltage. The thrust losses were reduced by over 5 percent with the use of compensated grids alone, and by variation of other parameters the overall thrust losses due to beam divergence were reduced to less than 2 percent.
The beam divergence of a 30-centimeter diameter thrustor with dished grids was calculated from current densities measured with a probe rake containing seventeen planar molybdenum probes. The measured data were analyzed as a function of a number of parameters. The most sensitive parameters were the amount of compensation of the accelerator grid and the ratio of net to total accelerating voltage. The thrust losses were reduced by over 5 percent with the use of compensated grids alone, and by variation of other parameters the overall thrust losses due to beam divergence were reduced to less than 2 percent.
For flexible structures with collocated rate and attitude sensors/actuators, we characterize compensator transfer functions which guarantee modal stability even when stiffness/inertia parameters are uncertain. While the compensators are finite-dimensional, the structure models are allowed to be infinite-dimensional (continuum models), with attendant complexity of the notion of stability; thus exponential stability is not possible and the best we can obtain is strong stability. Robustness is interpreted essentially as maintaining stability in the worst case. The conditions require that the compensator transfer functions be positive real and use is made of the Kalman-Yakubovic lemma to characterize them further. The concept of positive realness is shown to be equivalent to dissipativity in infinite dimensions. In particular we show that for a subclass of compensators it is possible to make the system strongly stable as well as dissipative in an appropriate energy norm.
Moessbauer spectra were obtained of single crystal and powdered samples of rutile (TiO2) doped to about one percent by weight in isotopically enriched iron. It is shown that the oxidation state may be reversibly altered in situ. After reduction, the oxygen neighbors of the dopant ion are apparently shifted to accomodate the larger ferrous ion. The agreement of calculated quadrupole splittings with experimental results suggests that impurities and oxygen vacancies are uniformly distributed in powdered samples, giving the dopant ions an 'ideal lattice' local environment. The differences between the single crystal and powder sample hyperfine parameters are attributed to variations in stoichiometry, charge compensation mechanisms, or other diffusion related parameters.
The paper presents several improved methods for the measurement of magnetic fields on cool stars which take into account simple radiative transfer effects and the exact Zeeman patterns. Using these methods, high-resolution, low-noise data can be fitted with theoretical line profiles to determine the mean magnetic field strength in stellar active regions and a model-dependent fraction of the stellar surface (filling factor) covered by these regions. Random errors in the derived field strength and filling factor are parameterized in terms of signal-to-noise ratio, wavelength, spectral resolution, stellar rotation rate, and the magnetic parameters themselves. Weak line blends, if left uncorrected, can have significant systematic effects on the derived magnetic parameters, and thus several methods are developed to compensate partially for them. The magnetic parameters determined by previous methods likely have systematic errors because of such line blends and because of line saturation effects. Other sources of systematic error are explored in detail. These sources of error currently make it difficult to determine the magnetic parameters of individual stars to better than about + or - 20 percent.