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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.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

A stopping theoretic approach to minimal time detection of system parameter change

The problem of minimal time detection of abrupt parameter changes in linear stochastic systems considered. The problem is posed as an optimal stopping problem for the detection in change of the induced probability measure. Under the assumption of a prior distribution for the time of change (or disruption) a stopping rule is given which minimizes the average detection delay when there is knowledge of the new measure after the change. When the new induced measure is unknown, a stopping rule is given, based only on the noisy observations and is shown to be better than the a priori knowlege of the disruption time.

Mazumdar, Ravi R.↗
NASA NTRS1994

Designing robust control laws using genetic algorithms

The purpose of this research is to create a method of finding practical, robust control laws. The robustness of a controller is judged by Stochastic Robustness metrics and the level of robustness is optimized by searching for design parameters that minimize a robustness cost function.

Marrison, Chris↗
NASA NTRS2020

Designing Trajectories Resilient to Missed Thrust Events Using Expected Thrust Fraction

With the adoption of efficient low-thrust propulsion methods, the probability of a missed thrust event occurring has become a significant concern for short and long- duration missions. If the missed thrust events take place during a critical portion of the trajectory, the mission can be compromised. Therefore, it is essential to de- velop trajectories that are resilient to missed thrust events. This paper investigates the use of expected thrust fraction, which embeds the stochastic nature of missed thrust events into a deterministic optimal control problem. The performance of trajectories designed using expected thrust fraction is compared with traditionally designed trajectories to measure changes in resiliency to missed thrust events. In this investigation, trajectories designed using expected thrust fraction arrive with a median lateness half that of traditionally designed trajectories. Using expected thrust fraction can help astrodynamicists mitigate risks posed by the use of low- thrust propulsion.

Laipert, Frank E.↗
NASA NTRS1976

Optimal ride control for the Twin Otter, STOL aircraft

An aircraft with low wing loading, such as the deHavilland, Twin Otter, exhibits a relatively large acceleration sensitivity to wind gusts. These undesirable aircraft motions can be reduced using an automatic ride control system. In this paper, the techniques of quadratic optimal control theory are utilized to investigate the capabilities of such a ride control system. The effects of the wind gusts on the aircraft can be modeled by a vector stochastic process containing seven states. The impact of this disturbance model on the optimal control system structure is assessed and comparison is made to a system without gust feedback.

Holley, W. E.↗
NASA NTRS1989

A disturbance based control/structure design algorithm

Some authors take a classical approach to the simultaneous structure/control optimization by attempting to simultaneously minimize the weighted sum of the total mass and a quadratic form, subject to all of the structural and control constraints. Here, the optimization will be based on the dynamic response of a structure to an external unknown stochastic disturbance environment. Such a response to excitation approach is common to both the structural and control design phases, and hence represents a more natural control/structure optimization strategy than relying on artificial and vague control penalties. The design objective is to find the structure and controller of minimum mass such that all the prescribed constraints are satisfied. Two alternative solution algorithms are presented which have been applied to this problem. Each algorithm handles the optimization strategy and the imposition of the nonlinear constraints in a different manner. Two controller methodologies, and their effect on the solution algorithm, will be considered. These are full state feedback and direct output feedback, although the problem formulation is not restricted solely to these forms of controller. In fact, although full state feedback is a popular choice among researchers in this field (for reasons that will become apparent), its practical application is severely limited. The controller/structure interaction is inserted by the imposition of appropriate closed-loop constraints, such as closed-loop output response and control effort constraints. Numerical results will be obtained for a representative flexible structure model to illustrate the effectiveness of the solution algorithms.

Mclaren, Mark D.↗
NASA NTRS1999

Multi-Parent Clustering Algorithms from Stochastic Grammar Data Models

We introduce a statistical data model and an associated optimization-based clustering algorithm which allows data vectors to belong to zero, one or several "parent" clusters. For each data vector the algorithm makes a discrete decision among these alternatives. Thus, a recursive version of this algorithm would place data clusters in a Directed Acyclic Graph rather than a tree. We test the algorithm with synthetic data generated according to the statistical data model. We also illustrate the algorithm using real data from large-scale gene expression assays.

Mjoisness, Eric↗
NASA NTRS1992

Robustness of solutions to a benchmark control problem

The robustness of 10 solutions to a benchmark control design problem presented at the 1990 American Control Conference has been evaluated. The 10 controllers have second-to-eighth-order transfer functions and have been designed using several different methods, including H-infinity optimization, loop-transfer recovery, imaginary-axis shifting, constrained optimization, structured covariance, game theory, and the internal model principle. Stochastic robustness analysis quantifies the controllers' stability and performance robustness with structured uncertainties in up to six system parameters. The analysis provides insights into system response that are not readily derived from other robustness criteria and provides a common ground for judging controllers produced by alternative methods. One important conclusion is that gain and phase margins are not reliable indicators of the probability of instability. Furthermore, parameter variations actually may improve the likelihood of achieving selected performance metrics, as demonstrated by results for the probability of settling-time exceedance.

Stengel, Robert F.↗
NASA NTRS2012

Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions

A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.

Bole, Brian↗
NASA NTRS2005

Optimal Estimation of Clock Values and Trends from Finite Data

We show how to solve two problems of optimal linear estimation from a finite set of phase data. Clock noise is modeled as a stochastic process with stationary dth increments. The covariance properties of such a process are contained in the generalized autocovariance function (GACV). We set up two principles for optimal estimation: with the help of the GACV, these principles lead to a set of linear equations for the regression coefficients and some auxiliary parameters. The mean square errors of the estimators are easily calculated. The method can be used to check the results of other methods and to find good suboptimal estimators based on a small subset of the available data.

stationary increments↗
NASA NTRS2013

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Ono, Masahiro↗
NASA NTRS1987

Proof of quasi-adaptivity for the m-measurement feedback class of stochastic control policies

Bounds on expected performance are established which show that the m-measurement feedback (mM) policy for nonlinear stochastic control performs as well or better than the open-loop optimal control policy, and thus is quasi-adaptive in the sense of Witenhausen (1966). The chain of performance inequalities indicate a tendency for the mM policy performance to improve with increasing m. It is suggested that the present analytical method, based on the construction of artificial control sequences denoted as utility controls, can be used to establish performance bounds on other well-known policies, avoiding the extensive Monte Carlo simulations necessary in comparing stochastic control policies.

Bayard, David S.↗
NASA NTRS1991

A stochastic regulator for integrated communication and control systems. I - Formulation of control law. II - Numerical analysis and simulation

A state feedback control law for integrated communication and control systems (ICCS) is formulated by using the dynamic programming and optimality principle on a finite-time horizon. The control law is derived on the basis of a stochastic model of the plant which is augmented in state space to allow for the effects of randomly varying delays in the feedback loop. A numerical procedure for synthesizing the control parameters is then presented, and the performance of the control law is evaluated by simulating the flight dynamics model of an advanced aircraft. Finally, recommendations for future work are made.

Liou, Luen-Woei↗
NASA NTRS1972

Nonlinear filtering for random signals in statistically unknown noise.

Natural and effective formulation of the filtering problem involved in satellite orbit determination, aircraft navigation, and missile tracking. The problem arises because the environment of the sensor keeps changing from time to time, and it is quite impractical and sometimes impossible to collect the statistical data of the noise incurred in the observation. Computable filtering equations are deduced. The idea of invariant imbedding along with stochastic differential calculus is used to derive differential equations for the optimal estimate.

Loo, J. T.↗
NASA NTRS2022

Analyzing the Efficacy of Flexible Execution, Replanning, and Plan Optimization for a Planetary Lander

Plan execution in unknown environments poses a number of challenges: uncertainty in domain modeling, stochasticity at execution time, and the presence of exogenous events. These challenges motivate an integrated approach to planning and execution that is able to respond intelligently to variation. We examine this problem in the context of the Europa Lander mission concept, and evaluate a planning and execution framework that responds to feedback and task failure using two techniques: flexible execution and replanning with plan optimization. We develop a theoretical framework to estimate gains from these techniques, and we compare these predictions to empirical results generated in simulation. These results indicate that an integrated approach to planning and execution leveraging flexible execution, replanning, and utility maximization shows significant promise for future tightly-constrained space missions that must address significant uncertainty.

Chien, Steve↗