A nonlinear recursive algorithm is formulated for state vector and covariance estimation of boost trajectories. The thrust acceleration vector of the booster is modeled by a vector-differential equation that includes ...
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A nonlinear recursive algorithm is formulated for state vector and covariance estimation of boost trajectories. The thrust acceleration vector of the booster is modeled by a vector-differential equation that includes effects of propellant depletion and attitude motions resulting from gravity-turn maneuvers and other steering maneuvers. This new dynamics model is incorporated in an extended Kalman filter with nine state variables that describe the inertial components of position, velocity, and thrust acceleration. Additional algorithms are described for filter initialization using angle-only measurements from a geostationary sensor and for detection and estimation of the final staging event using measurement residuals. Tracking accuracy and covariance fidelity are assessed by Monte Carlo simulation.
The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the contro...
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The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the controller. In this paper, a filtered-x recursive maximum correntropy (FxRMC) algorithm is proposed based on the maximum correntropy criterion (MCC) to reduce the effect of outliers. The proposed FxRMC algorithm does not requires any priori information of the noise characteristics and outperforms the filtered-x least mean square (FxLMS) algorithm for impulsive noise. Meanwhile, in order to adjust the kernel size of FxRMC algorithm online, a recursive approach is proposed through taking into account the past estimates of error signals over a sliding window. Simulation and experimental results in the context of active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.
The isolated rupture degree for a connected graph G is defined as ir(G) = max{i(G-S)-vertical bar S vertical bar-m(G-S) : S is an element of C(G)}, where i(G-S) and m(G-S), respectively, denote the number of component...
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The isolated rupture degree for a connected graph G is defined as ir(G) = max{i(G-S)-vertical bar S vertical bar-m(G-S) : S is an element of C(G)}, where i(G-S) and m(G-S), respectively, denote the number of components which are isolated vertices and the order of a largest component in G-S. C(G) denotes the set of all cut-sets of G. The isolated rupture degree is a new graph parameter which can be used to measure the vulnerability of networks. In this paper, we firstly give a recursive algorithm for computing the isolated rupture degree of trees, and determine the maximum and minimum isolated rupture degree of trees with given order and maximum degree. Then, the exact value of isolated rupture degree of gear graphs are given.. In the final, we determine the rupture degree of the Cartesian product of two special graphs and a special permutation graph.
作者:
HOU, HSElectronics and Optics Division
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The fast Hartley transform (FHT) is similar to the Cooley-Tukey fast Fourier transform (FFT) but performs much faster because it requires only real arithmetic computations compared to the complex arithmetic computatio...
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The fast Hartley transform (FHT) is similar to the Cooley-Tukey fast Fourier transform (FFT) but performs much faster because it requires only real arithmetic computations compared to the complex arithmetic computations required by the FFT. Through use of the FHT, discrete cosine transforms (DCT) and discrete Fourier transforms (DFT) can be obtained. The recursive nature of the FHT algorithm derived in this paper enables us to generate the next higher order FHT from two identical lower order FHT"s. In practice, this recursive relationship offers flexibility in programming different sizes of transforms, while the orderly structure of its signal flow-graphs indicates an ease of implementation in VLSI.
In this paper the authors develop a recursive algorithm for identification of a possibly overparameterised state-space model for time-varying linear systems. Each parameter of the model is either known, lies in a boun...
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In this paper the authors develop a recursive algorithm for identification of a possibly overparameterised state-space model for time-varying linear systems. Each parameter of the model is either known, lies in a bounded region, or is unknown. The cost to be minimised, subject to the parameter constraints, is formed from the output prediction error together with penalty terms. The algorithm is adaptive with respect to the penalty terms. Furthermore, the paper develops a state-space balancing method consistent with structure, if possible, and also considers the introduction of a time-varying forgetting factor.
An output feedback control law has been formulated in a stochastic setting, based on the principles of minimum variance filtering and dynamic programming, for application to processes that are subjected to randomly va...
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An output feedback control law has been formulated in a stochastic setting, based on the principles of minimum variance filtering and dynamic programming, for application to processes that are subjected to randomly varying distributed delays. The proposed estimation and control law for delay compensation is built on the concept of the conventional linear quadratic Gaussian (LQG), called delay compensated linear quadratic Gaussian (DCLQG). Although the certainty equivalence property of LQG does not hold for DCLQG in general, the combined state estimation and state feedback approach of DCLQG offers a suboptimal solution to the control problem under randomly varying distributed delays. DCLQG is potentially applicable to analysis and synthesis of control systems for vehicle management of future generation aircraft where a computer network is employed for distributed processing and on-line information exchange between diverse control and decision-making functions. Results of simulation experiments are presented to demonstrate efficacy of the proposed DCLQG algorithm for flight control of an advanced aircraft.
This paper presents an algorithm for the efficient numerical analysis and simulation of modest to heavily constrained multi-rigid-body dynamic systems. The algorithm can accommodate the spatial motion of general multi...
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This paper presents an algorithm for the efficient numerical analysis and simulation of modest to heavily constrained multi-rigid-body dynamic systems. The algorithm can accommodate the spatial motion of general multi-rigid-body systems containing arbitrarily many closed loops in O(n + m) operations overall for systems containing n generalized coordinates, and m independent algebraic constraints. The presented approach does not suffer from the performance (speed) penalty encountered by most other of the so-called 'O(n)' state-space formulations, when dealing with constraints which tend to actually show O(n + m + nm + nm(2) + m(3)) performance. Additionally, these latter formulations may require additional constraint violation stabilization procedures (e. g. Baumgarte's method, coordinate partitioning, etc.) which can contribute significant additional computation. The presented method suffers less from this difficulty because the loop closure constraints at both the velocity and acceleration level are directly embedded within the formulation. Due to these characteristics, the presented algorithm offers superior computing performance relative to other methods in situations involving both large n and m.
Nonlinear phenomena, which are so important in nature and society, are considered here in relation to the world of algorithms and computations. To have a mathematical model for this world, formal computability spaces ...
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Nonlinear phenomena, which are so important in nature and society, are considered here in relation to the world of algorithms and computations. To have a mathematical model for this world, formal computability spaces are introduced. It is demonstrated that the traditional approach to algorithms, which is based on such popular models as Turing machines, results in linear subspaces of the computability space. Nonlinear phenomena appear when we go to the more powerful class of such super-recursive algorithms as inductive Turing machines. It is demonstrated how this nonlinearity imports much higher computing power of inductive Turing machines in comparison with conventional Turing machines. This provides a base to consider problems of chaos, emergent computations and infinity from the algorithmic point of view.
The problem of the online identification of multi-input multi-output (MIMO) state-space models in the framework of discrete-time subspace methods is considered in this paper. Several algorithms, based on a recursive f...
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The problem of the online identification of multi-input multi-output (MIMO) state-space models in the framework of discrete-time subspace methods is considered in this paper. Several algorithms, based on a recursive formulation of the MIMO Output Error State-Space (MOESP) identification class, are developed. The main goals of the proposed methods are to circumvent the huge complexity of eigenvalues or singular values decomposition techniques used by the offline algorithm and to provide consistent state-space matrices estimates in a noisy framework. The underlying principle consists in using the relationship between array signal processing and subspace identification to adjust the propagator method (originally developed in array signal processing) to track the subspace spanned by the observability matrix. The problem of the (coloured) disturbances acting on the system is solved by introducing an instrumental variable in the minimized cost functions. A particular attention is paid to the algorithmic development and to the computational cost. The benefits of these algorithms in comparison with existing methods are emphasized with a simulation study in time-invariant and timevarying scenarios. (C) 2007 Elsevier B.V. All rights reserved.
Phased-mission common bus (PMCB) systems are systems with a common bus structure, performing missions with consecutive and non-overlapping phases of operations. PMCB systems are found throughout industry, e.g., power ...
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Phased-mission common bus (PMCB) systems are systems with a common bus structure, performing missions with consecutive and non-overlapping phases of operations. PMCB systems are found throughout industry, e.g., power generating systems, parallel computing systems, transportation systems, and are sometimes characterized by their common cause failures. Reliability evaluation of PMCB systems plays an important role in system design, operation, and maintenance. However, current studies have focused on either phased-mission systems or common bus systems because of their complexity. The challenge in practice is to consider phased-mission systems together with common bus structures and common cause failures. To solve this problem, we propose an evaluation algorithm for PMCB systems with common cause failures by coupling the structure function of a common bus performance sharing system and an existing recursive algorithm. To weigh the efficiency of the proposed algorithm, its complexity is discussed. To improve the reliability of PMCB systems, we adopt the genetic algorithm method to search for the optimal allocation strategies of the service elements. We use both analytical and numerical examples to illustrate the application of the proposed algorithm. (C) 2017 Elsevier Ltd. All rights reserved.
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