Observer/Kalman-Filter Time-Varying System Identification

Majji, Manoranjan; Juang, Jer‐Nan; Junkins, John L.

doi:10.2514/1.45768

Cited by 50 publications

(30 citation statements)

References 19 publications

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“…Majji et al [34]). The closed-loop observer thus constructed is now forced to have an asymptotically stable origin.…”

Section: Basic Formulationmentioning

confidence: 99%

“…Majji et al [33,34]). Fundamental difference equation dictating the time evolution of the state is given by…”

Section: Basic Formulationmentioning

confidence: 99%

“…Also, note that the order p k of the GTV-ARX model can also change with time (we coin the term "generalized" to describe this variability in the order). This variation and (time-varying observer/Kalman filter identification) complexity provides a large number of observer gains at the disposal of the analyst under the TOKID framework, as discussed in our recent paper [34]. In using this input-output relationship [Eq.…”

Section: B Time-varying Deadbeat Observersmentioning

confidence: 99%

“…We make such generalizations using our recent work in time-varying system identification (cf. Majji et al [33,34]). It has to be pointed out that our recent generalization is more general than the classical results of Kaczorek [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Time-Varying Deadbeat Controller Design

Majji

Juang

Junkins

2012

Journal of Guidance, Control, and Dynamics

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A time-varying generalization of the classical deadbeat controller is derived directly from input-output data in the present paper. The natural extension of the time invariant deadbeat condition to the time-varying case is presented by using the key developments of the observer/Kalman filter time-varying system identification theory reported recently by the authors. In stark contrast to the time invariant deadbeat controller design, it is shown that the timevarying deadbeat controller parameters do not follow the recursive relationship that was developed in the past. In the special case of periodic systems, where repeated experimental data are available naturally, it seems that such a controller can be designed to set the output of the closed-loop system to rest after a few time steps. Numerical simulation example demonstrates the efficacy of the deadbeat controller design method proposed in this paper. A deadbeat controller, realized from the departure motion dynamics of a spacecraft, is then shown to enhance the performance of an attitude control system by minimizing the deviations from a reference trajectory.

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“…Majji et al [34]). The closed-loop observer thus constructed is now forced to have an asymptotically stable origin.…”

Section: Basic Formulationmentioning

confidence: 99%

“…Majji et al [33,34]). Fundamental difference equation dictating the time evolution of the state is given by…”

Section: Basic Formulationmentioning

confidence: 99%

Section: B Time-varying Deadbeat Observersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time-Varying Deadbeat Controller Design

Majji

Juang

Junkins

2012

Journal of Guidance, Control, and Dynamics

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“…Depending on the available sensor data, this refinement could be performed using similar methods to the existing airdrop identification methods mentioned previously [4][5][6][7][8][9][10][11]. Another possibility is that the model made from GPS data using the proposed procedure could be simply augmented to capture unmodeled dynamics observed in the additional sensor channels using more general system identification techniques such as those recently developed by Majji et al [15,16].…”

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confidence: 99%

Specialized System Identification for Parafoil and Payload Systems

Ward

Costello

Slegers

2012

Journal of Guidance, Control, and Dynamics

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There are a number of peculiar aspects to parafoil and payload systems that make it difficult to apply conventional system identification procedures used for aerospace systems. Parafoil and payload systems are unique because typically there is very little sensor information available, the sensors that are available are separated from the canopy by a complex network of flexible rigging, the systems are very sensitive to wind and turbulence, the systems exhibit a number of nonlinear behaviors, and the systems exhibit a high degree of variability from flight to flight. The current work describes a robust system identification procedure developed to address the specific difficulties posed by airdrop systems. By employing a two-phase approach that separately considers atmospheric winds estimation and aerodynamic coefficient estimation, a nonlinear, 6-degree-of-freedom dynamic simulation model is generated using only Global Positioning System data from the flight test. The key to this approach is the use of a simplified aerodynamic representation of the canopy, which requires identification of only the steady-state response to control input to completely define the dynamic model. The proposed procedure is demonstrated by creating a simulation model using Global Positioning System data from actual flight tests. To validate the procedure, the dynamic response of the simulation model is then compared to inertial measurement unit data that were not used in any way to develop the simulation model, with excellent results.

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Adaptive control and signal processing literature survey (No. 19)

2010

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk ADAPTIVE SYSTEMSAhn H-S, Chen Y. Periodic adaptive learning compensation of state-dependent disturbance. IET Control Theory and Applications 2010; 4(4):529-538. Angelov P, Kordon A. Adaptive inferential sensors based on evolving fuzzy models. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetic 2010; 40(2):529-539. Bechlioulis C, Rovithakis G. Prescribed performance adaptive control for multi-input multi-output affine in the control nonlinear systems. IEEE Transactions on Automatic Control 2010; 55(5):1220-1226. Bekiaris-Liberis N, Krstic M. Delay-adaptive feedback for linear feedforward systems. Systems and Control Letters 2010; 59(5):277-283. Chen M, Ge S, How B. Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities. IEEE Transactions on Neural Networks 2010; 21(5):796-812. Choi K, Yoo S, Park J, Choi Y. Adaptive formation control in absence of leader's velocity information. IET Control Theory and Applications 2010; 4(4):521-528. Fuentes D, Feng Y, Elliott A, Shetty A, McNichols R, Oden J, Stafford R. Adaptive real-time bioheat transfer models for computer-driven MR-guided laser induced thermal therapy. IEEE Transactions on Biomedical Engineering 2010; 57(5):1024-1030. Hu C, Yao B, Wang Q. Integrated direct/indirect adaptive robust contouring control of a biaxial gantry with accurate parameter estimations. Automatica 2010; 46(4):701-707. Jiménez-Fabián R, Alvarez-Icaza L. An adaptive observer for a shear building with an energy-dissipation device. Control Engineering Practice 2010; 18(4):331-338. Kaylani A, Georgiopoulos M, Mollaghasemi M, Anagnostopoulos G, Sentelle C, Zhong M. An adaptive multiobjective approach to evolving ART architectures. IEEE Transactions on Neural Networks 2010; 21(4):529-550. Lin W, Ma H. Synchronization between adaptively coupled systems with discrete and distributed time-delays. IEEE Transactions on Automatic Control 2010; 55(4): 819-830. Ma L, Cao C, Hovakimyan N, Dobrokhodov V, Kaminer I. Adaptive vision-based guidance law with guaranteed performance bounds. Journal of Guidance, Control, and Dynamics 2010; 33(3):834-852. Olm J, Ramos G, Costa-Castelló R. Adaptive compensation strategy for the tracking/rejection of signals with time-varying frequency in digital repetitive control systems. Journal of Process Control 2010; 20(4):551-558. Peng S, Hwang W-L. Null space pursuit: an operator-based approach to adaptive signal separation. IEEE Transact...

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Observer/Kalman-Filter Time-Varying System Identification

Cited by 50 publications

References 19 publications

Time-Varying Deadbeat Controller Design

Time-Varying Deadbeat Controller Design

Specialized System Identification for Parafoil and Payload Systems

Adaptive control and signal processing literature survey (No. 19)

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