Estimating aircraft states
Computer program provides weighted least-squares estimates of aircraft states from measurements recorded during routine flight tests. Program contains standard six-degree-of-freedom kinematic equations.
Wingrove, R. C.↗
SEARCH · Search NASA
Results for “state estimation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Identification of rotating assembly inertial and bearing parameters.
The problem of estimating the state of a turbopump rotating assembly and identifying unknown products of inertia and unknown bearing parameters is considered. A linearized, extended Kalman filtering approach has been used and found to be successful for both state estimation and parameter identification in an inherently nonlinear problem. The input data were simulated Bently test data obtained from a verified 12-dimensional state-space model and were compared with real Bently test data obtained from NASA. The results prove the feasibility of using this model and this type of test data to obtain the hidden parameters of a typical turbopump rotating assembly.
Alspach, D. L.↗
A program to form a multidisciplinary data base and analysis for dynamic systems
Diverse sets of experimental data and analysis programs have been assembled for the purpose of facilitating research in systems identification, parameter estimation and state estimation techniques. The data base analysis programs are organized to make it easy to compare alternative approaches. Additional data and alternative forms of analysis will be included as they become available.
Taylor, L. W.↗
Self-Tuning Adaptive-Controller Using Online Frequency Identification
A real time adaptive controller was designed and tested successfully on a fourth order laboratory dynamic system which features very low structural damping and a noncolocated actuator sensor pair. The controller, implemented in a digital minicomputer, consists of a state estimator, a set of state feedback gains, and a frequency locked loop (FLL) for real time parameter identification. The FLL can detect the closed loop natural frequency of the system being controlled, calculate the mismatch between a plant parameter and its counterpart in the state estimator, and correct the estimator parameter in real time. The adaptation algorithm can correct the controller error and stabilize the system for more than 50% variation in the plant natural frequency, compared with a 10% stability margin in frequency variation for a fixed gain controller having the same performance at the nominal plant condition. After it has locked to the correct plant frequency, the adaptive controller works as well as the fixed gain controller does when there is no parameter mismatch. The very rapid convergence of this adaptive system is demonstrated experimentally, and can also be proven with simple root locus methods.
Chiang, W. W.↗
Kalman filtering, smoothing, and recursive robot arm forward and inverse dynamics
The recursive difference equations of Kalman filtering and Bryson-Frazier fixed time-interval smoothing, arising in the state estimation theory for linear state space systems, are used here to solve problems of serial manipulator inverse and forward dynamics. The configuration analyzed is that of a joint connected N-link serial manipulator attached to an immobile base. The equivalence between the filtering and smoothing techniques from state estimation theory and recursive robot dynamics methods is demonstrated. Several areas for future research are suggested.
Rodriguez, Guillermo↗
Improving Ocean Angular Momentum Estimates Using a Model Constrained by Data
Ocean angular momentum (OAM) calculations using forward model runs without any data constraints have, recently revealed the effects of OAM variability on the Earth's rotation. Here we use an ocean model and its adjoint to estimate OAM values by constraining the model to available oceanic data. The optimization procedure yields substantial changes in OAM, related to adjustments in both motion and mass fields, as well as in the wind stress torques acting on the ocean. Constrained and unconstrained OAM values are discussed in the context of closing the planet's angular momentum budget. The estimation procedure, yields noticeable improvements in the agreement with the observed Earth rotation parameters, particularly at the seasonal timescale. The comparison with Earth rotation measurements provides an independent consistency check on the estimated ocean state and underlines the importance of ocean state estimation for quantitative. studies of the variable large-scale oceanic mass and circulation fields, including studies of OAM.
Ponte, Rui M.↗
An Empirical State Error Covariance Matrix for the Weighted Least Squares Estimation Method
State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the uncertainty in the estimated states. By a reinterpretation of the equations involved in the weighted least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. This proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. Results based on the proposed technique will be presented for a simple, two observer, measurement error only problem.
Joseph Henry Frisbee↗
An Empirical State Error Covariance Matrix for the Weighted Least Squares Estimation Method
State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the un-certainty in the estimated states. By a reinterpretation of the equations involved in the weighted least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. This proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. Results based on the proposed technique will be presented for a simple, two observer, measurement error only problem.
Frisbee, Joseph H., Jr.↗
Optimal post-experiment estimation of poorly modeled dynamic systems
Recently, a novel strategy for post-experiment state estimation of discretely-measured dynamic systems has been developed. The method accounts for errors in the system dynamic model equations in a more general and rigorous manner than do filter-smoother algorithms. The dynamic model error terms do not require the usual process noise assumptions of zero-mean, symmetrically distributed random disturbances. Instead, the model error terms require no prior assumptions other than piecewise continuity. The resulting state estimates are more accurate than filters for applications in which the dynamic model error clearly violates the typical process noise assumptions, and the available measurements are sparse and/or noisy. Estimates of the dynamic model error, in addition to the states, are obtained as part of the solution of a two-point boundary value problem, and may be exploited for numerous reasons. In this paper, the basic technique is explained, and several example applications are given. Included among the examples are both state estimation and exploitation of the model error estimates.
Mook, D. Joseph↗
On-Board Real-Time State and Fault Identification for Rovers
For extended autonomous operation, rovers must identify potential faults to determine whether its execution needs to be halted or not. At the same time, rovers present particular challenges for state estimation techniques: they are subject to environmental influences that affect senior readings during normal and anomalous operation, and the sensors fluctuate rapidly both because of noise and because of the dynamics of the rover's interaction with its environment. This paper presents MAKSI, an on-board method for state estimation and fault diagnosis that is particularly appropriate for rovers. The method is based on a combination of continuous state estimation, wing Kalman filters, and discrete state estimation, wing a Markov-model representation.
Washington, Richard↗
The estimation error covariance matrix for the ideal state reconstructor with measurement noise
A general expression is derived for the state estimation error covariance matrix for the Ideal State Reconstructor when the input measurements are corrupted by measurement noise. An example is presented which shows that the more measurements used in estimating the state at a given time, the better the estimator.
Polites, Michael E.↗
Simulation Evaluation of a Large-Scale Implementation of Virtual-Phase Link-Based Model Predictive Control
Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly.
large-scale simulation↗
A navigation and control system for an autonomous rescue vehicle in the space station environment
A navigation and control system was designed and implemented for an orbital autonomous rescue vehicle envisioned to retrieve astronauts or equipment in the case that they become disengaged from the space station. The rescue vehicle, termed the Extra-Vehicular Activity Retriever (EVAR), has an on-board inertial measurement unit ahd GPS receivers for self state estimation, a laser range imager (LRI) and cameras for object state estimation, and a data link for reception of space station state information. The states of the retriever and objects (obstacles and the target object) are estimated by inertial state propagation which is corrected via measurements from the GPS, the LRI system, or the camera system. Kalman filters are utilized to perform sensor fusion and estimate the state propagation errors. Control actuation is performed by a Manned Maneuvering Unit (MMU). Phase plane control techniques are used to control the rotational and translational state of the retriever. The translational controller provides station-keeping or motion along either Clohessy-Wiltshire trajectories or straight line trajectories in the LVLH frame of any sufficiently observed object or of the space station. The software was used to successfully control a prototype EVAR on an air bearing floor facility, and a simulated EVAR operating in a simulated orbital environment. The design of the navigation system and the control system are presented. Also discussed are the hardware systems and the overall software architecture.
Merkel, Lawrence↗
Exploration of an Adaptive Routine for Battery Modeling
The purpose of this document is to explore the use of adaptive routines in battery modeling. The adaptive routines consist of real-time state estimators combined with battery parameter model components that are adjusted in real-time as battery data becomes available. Several aspects are explored. It is shown that model parameter identification is possible for simple battery models using available input/output data measurements. The online system identification used is recursive least squares. Model identification may be combined with a state observer such as the extended Kalman filter or the unscented Kalman filter to form an adaptive model combined with state estimation. However, such a combination is found to be problematic due to uncertainty, observability and stability issues. This paper is organized as follows. Section 1 introduces adaptive routines and possible roles they play in battery modeling. In Section 2 real-time parameter identification is described with results based on battery data. Section 3 reviews various state estimators and results using a simple battery model. In Section 4 parameter identification and state estimation are combined to form an adaptive routine. Finally, in Section 5 conclusions are drawn and future work is suggested.
Adaptive↗
A square root formulation for the combined state-parameter estimator with application to the identification of sailplane performance
A square root formulation is presented for the discrete combined state parameter estimation problem with linear plant dynamics, Gaussian random disturbances, and constant but uncertain parameters. The estimator is a combination of the classical Kalman filter and a maximum likelihood algorithm which maximizes the parameter log-likelihood function using a first order search routine.
Froidevaux, M. R.↗
LiAISON-Supplemented Navigation for Geosynchronous and Lunar L1 Orbiter
A high-fidelity simulation is developed to test the theory that relative range and range-rate measurements between a satellite in a geosynchronous orbit and a satellite in orbit about the Earth-Moon L1 point may be used to improve the navigation accuracy of both satellites, compared with ground-only tracking. Recent research has determined that satellite-to-satellite range measurements between these two orbits are su cient to perform absolute state estimation as well as relative state estimation of both satellites - a technology known as LiAISON (Linked Autonomous Interplanetary Satellite Orbit Navigation). This study quanties the improvement in accuracy when LiAISON is used to supplement ground tracking of one or both satellites in a very realistic simulation. Orbit determination techniques show that the inclusion of LiAISON measurements with ground tracking observations can greatly improve absolute position and velocity estimates for both satellites in geosynchronous orbit and about the Earth-Moon L1 point.
satellite-to-satellite tracking (SST)↗
LiAISON-supplemented navigation for geosynchronous and Lunar L1 orbiters
A high-fidelity simulation is developed to test the theory that relative range and range-rate measurements between a satellite in a geosynchronous orbit and a satellite in orbit about the Earth-Moon L₁ point may be used to improve the navigation accuracy of both satellites, compared with ground-only tracking. Recent research has determined that satellite-to-satellite range measurements between these two orbits are sufficient to perform absolute state estimation as well as relative state estimation of both satellites–a technology known as LiAISON(Linked Autonomous Interplanetary Satellite Orbit Navigation). This study quantifies the improvement in accuracy when LiAISON is used to supplement ground tracking of one or both satellites in a very realistic simulation. Orbit determination techniques show that the inclusion of LiAISON measurements with ground tracking observations can greatly improve absolute position and velocity estimates for both satellites in geosynchronous orbit and about the Earth-Moon L₁ point.
Anderson, Rodney L.↗
Application of modal control to wing-flutter suppression
A discrete modal control design approach that is applied to a single control surface, unswept aircraft wing subject to bending torsion flutter is described. The modal approach is a mathematical method to decouple the equations of motion into isolated differential equations. In this paper, a pole-placement approach is then applied to determine stability gains in the discrete plane using only the two complex-conjugate flutter-mode equations. A fixed gain Kalman filter is used to estimate the model amplitudes using three measurements. Results are presented for a full-state estimator (36 states) and two reduced state estimators using two different closed-loop pole locations. The control law is designed for a dynamic pressure that is 50 percent greater than the uncontrolled-flutter dynamic pressure. With constant control-law gains, the closed-loop system remains stable over the dynamic-pressure range from flutter onset to approximately an 80-percent increase in pressure.
Ostroff, A. J.↗