Recursive self-tuning algorithm for adaptive Kalman filtering

El-Fattah, Yousri M.

doi:10.1049/ip-d.1983.0056

Cited by 8 publications

(8 citation statements)

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“…Notice that the use of (15a) and (17) is possible because of the existence of planned input to drive the responder properly and cause its output to be close to the desired value. Otherwise, the term could not be estimated properly.…”

Section: Execution Of a Plan And Feedback Controlmentioning

confidence: 96%

“…The difficulty of the problem increases significantly if a parameterized model of the responder's dynamics is available and the values of the parameters are unknown. Then, in the case of linear dynamics, linear observations, and Gaussian measurement noise and system uncertainties, the parameter identification is optimally based on Kalman filtering [16], [17]. Once the parameters are identified the model is known, and planning could be performed by solving directly for the input values.…”

Section: ) Satisfies the Conditions: A)mentioning

confidence: 99%

“…4(c). If discrete time is used, let , and let the responder system be modeled with the following equations: (16) Then (15a) is written as (17) and…”

Section: Execution Of a Plan And Feedback Controlmentioning

confidence: 99%

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Planning with a functional neural network architecture

Panagiotopoulos

Newcomb²,

Singh³

1999

IEEE Trans. Neural Netw.

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This article introduces the concept of planning in an interactive environment between two systems: the challenger and the responder. The responder's task is to produce behavior that relates to the challenger's behavior through some response function. In this setup, we concentrate planning on the responder's actions and use the produced plan in order to control the responder. In general, the responder is assumed to be a nonlinear system whose input-output (I/O) map may be expressed by a Volterra series. The planner uses an estimate of the challenger's future output sequence, the response function, and a model of the responder's I/O relation implemented through a functional artificial neural network (FANN) architecture, in order to produce the input sequence that will be applied to the responder in the future, in parallel-time with the challenger's corresponding output sequence. The responder accepts input from the planner, which may be combined with feedback information, in order to produce an output sequence that relates to the challenger's output sequence according to the response function. The importance of planning for the generation of smooth behavior is discussed, and the effectiveness of the planner's implementation using neural network technology is demonstrated with an example.

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Section: Execution Of a Plan And Feedback Controlmentioning

confidence: 96%

Section: ) Satisfies the Conditions: A)mentioning

confidence: 99%

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Planning with a functional neural network architecture

Panagiotopoulos

Newcomb²,

Singh³

1999

IEEE Trans. Neural Netw.

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“…In this section, we discuss asymptotic properties of the proposed adaptive UKF similarly as in. 20 First we assume that the UKF is stable andx k is uniformly bounded. Then from the UKF equation (2)-(5) and the adaptive UKF equation 17…”

Section: Asymptotic Behaviormentioning

confidence: 99%

Adaptive UKF and Its Application in Fault Tolerant Control of Rotorcraft UAV

Jiang

Zhao

2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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Fault tolerant control (FTC) is essential for rotorcraft UAV (RUAV). Due to the inherently unstable dynamics, either flight test or real application of a RUAV is in high risk while a minutial failure may lead to the whole system collapse. In this paper, a novel adaptive unscented Kalman filter (AUKF) is proposed for onboard failure coefficient estimation and a new FTC method is designed against the actuator failure of RUAV. In the AUKF, the error between the covariance matrices of innovation and their corresponding estimations/predictions in normal UKF is utilized as a cost function. Based on the MIT rule, an adaptive algorithm is developed to update the covariance of process noise by minimizing the cost function. The updated covariance is then fed back into the normal UKF. Such an adaptive mechanism intends to release the dependence of UKF on a prior knowledge of the noise environment and improve the convergence speed and estimation accuracy of normal UKF. By introducing the actuator health coefficients (AHCs) into the dynamics equation of a RUAV, the proposed AUKF is utilized to online estimate both the flight states and the AHCs. A fault adaptive control is further designed based on the estimated states and AHCs. Simulations are conducted on the dynamics of a model helicopter developed in Shenyang Institute of Automation. The comparisons between the adaptive-UKF-based FAC and the normal-UKF-based one show the effectiveness and improvements of the proposed method.

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“…In this paper, inspired by the study of El-Fattah, 17 a new self-tuning Kalman filter is presented to decrease the destructive effects of the uncertainties in the measurement model in the LOS guidance loop. The key idea is to identify online the uncertain parameters existing in the observation model and consequently, in the filter equations; similar to Wang et al., 15 Li et al., 18 and Liu et al., 19 which identify noise characteristics used in the Kalman filter relations.…”

Section: Introductionmentioning

confidence: 99%

Self-tuning Kalman filter for compensation of transmission delay and loss in line-of-sight guidance

Nikfetrat

Esfanjani

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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A self-tuning Kalman filter is introduced to reduce the destructive effects of the delayed and lost measurements in the guidance systems employing command to line-of-sight strategy. A sequence of Bernoulli distributed random variables with uncertain probabilities are used to model the delayed and lost observations. Besides the state estimation, the uncertain parameters of the measurement model are identified online using the covariance of innovation sequence. Simulation results are given to demonstrate the merits of the suggested approach.

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Recursive self-tuning algorithm for adaptive Kalman filtering

Cited by 8 publications

References 1 publication

Planning with a functional neural network architecture

Planning with a functional neural network architecture

Adaptive UKF and Its Application in Fault Tolerant Control of Rotorcraft UAV

Self-tuning Kalman filter for compensation of transmission delay and loss in line-of-sight guidance

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