Estimating short-period dynamics using an extended Kalman filter

Bauer, Jeff; Andrisani, Dominick

doi:10.2514/6.1990-1277

Cited by 6 publications

(7 citation statements)

References 2 publications

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“…Equations (10) through (14) define the EKF algorithm [4,5]. The constant matrices K j d are present in Equation (10).…”

Section: Extended Kalman Filter Algorithm For Damage Monitoringmentioning

confidence: 99%

“…Because the vibration database is available, an EKF algorithm is employed to estimate these parameters as realistically as possible. In state and parameter estimation frameworks, generally, derivatives of the parameters are required in the EKF algorithm . Stiffness loss in real time could be time varying.…”

Section: Modeling Stiffness Loss At Second‐order Structural Systemsmentioning

confidence: 99%

“…Such an active self‐healing mechanism may be necessary in aerospace, mechanical and civil engineering disciplines, where the structure with damage assessment and mitigation is performed to extend the life of the structure or until repairs are performed [ and references therein]. In this framework, this paper considers the transient response data of a vibrating structure as measurements for an extended Kalman filter (EKF) algorithm and computes possible stiffness variations at the springs. While the damage size is being assessed, mode shapes contributing to tensile and compressive loads at the damaged spring are considered.…”

Section: Introductionmentioning

confidence: 99%

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Active self-healing mechanisms for discrete dynamic structures

Ashokkumar

2013

Struct. Control Health Monit.

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SUMMARY In this paper, vibration database for damage growth monitoring and mitigation (active self‐healing) in discrete structures operating with dynamic loads is considered. SHM in this context assumes a known damage location present in the structure and then determines its size at which it is present. The variations in the linear spring stiffness across the structure are considered as an indication of the damage growth. A transient response of the vibrating structure is assumed available to monitor the damage size using an extended Kalman filter algorithm. The growth of this damage is considered mitigated when the potential energy due to mode shapes that take tensile and compressive loads is minimized. These mode shapes are determined by a state feedback controller for which an actuator combination (more than one actuator) is assumed available. A linear spring–mass–damper system is considered to illustrate the active self‐healing mechanisms presented in this paper. Copyright © 2013 John Wiley & Sons, Ltd.

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“…Equations (10) through (14) define the EKF algorithm [4,5]. The constant matrices K j d are present in Equation (10).…”

Section: Extended Kalman Filter Algorithm For Damage Monitoringmentioning

confidence: 99%

Section: Modeling Stiffness Loss At Second‐order Structural Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Active self-healing mechanisms for discrete dynamic structures

Ashokkumar

2013

Struct. Control Health Monit.

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“…In this paper, a feasibility study is performed to show that extended Kalman filter (EKF) is an excellent tool to design the autonomous control agents (ACAs). In this case, the EKF algorithm [3], in state and parameter estimation framework is used. That is, it takes the trajectory generation module as an input (measurements) and delivers an ACA to reconstruct its trajectories sequentially such that an adaptive mission profile is realized.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Control Agents for Adaptive MAV Mission Profiles

Ashokkumar

2013

International Journal of Micro Air Vehicles

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When a mission profile of an unmanned micro air vehicle is known a priori, one of the strategies adopted in autonomous control is to first generate a compatible trajectory in off-line and then implement a controller to track the trajectory. However, in decision rich intelligent aerial robots and in 6DOF operations of unmanned aerial vehicles, the mission profiles are usually known during an operation. Hence the trajectories are instantaneously inferred. Here an adaptive controller with capabilities to track these trajectories is required. In this paper, it is shown that an extended Kalman filter is an excellent tool to design such a controller which accepts the trajectory generation module as an input and then reconstructs its trajectories. To illustrate this feature, a 3DOF micro air vehicle in pitch plane is considered and an adaptive controller (referred as autonomous control agent) using an extended Kalman filter is applied to infer typical adaptive mission profiles. INTRODUCTIONAutonomous control of aerial robots in 6DOF is quite challenging. Unlike unmanned aerial vehicles for surveillance and reconnaissance operations, the adaptive mission profiles of aerial robots are usually inferred during its operation. Consequently, the trajectories are instantaneously inferred. When an adaptive control law is required to track these trajectories, design and implementation procedures using a conventional controller is handicapped. For instance, consider a known mission profile. Compatible trajectory in offline is generated and a human-made conventional controller is implemented online so that the trajectory is tracked to achieve the known mission profile. This approach has been demonstrated through flight testing [1,2]. To adopt these techniques for instantaneously generated trajectories belonging to an adaptive mission profile, an autonomous control agent (similar to an adaptive controller) with capabilities to reconstruct the instantaneous trajectories are required. In this paper, a feasibility study is performed to show that extended Kalman filter (EKF) is an excellent tool to design the autonomous control agents (ACAs). In this case, the EKF algorithm [3], in state and parameter estimation framework is used. That is, it takes the trajectory generation module as an input (measurements) and delivers an ACA to reconstruct its trajectories sequentially such that an adaptive mission profile is realized. Suppose a linear state feedback structure is used, the gains of the ACA become time-varying and the closed loop system becomes non-autonomous. In this case, stability is verified by checking the eigenvalues of the piecewise linear system. ACA presented in this paper fills in the adaptive controller layer conceived early in the development of a multi-layered autonomous control [4]. These layers also consist of trajectory generation, trajectory reconstruction, trajectory planning modules [5,6]. Adaptive controller emphasized in this investigation reconstructs the trajectory generated through waypoints [5]. Since ...

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“…Several methods [1][2][3][4][5] have been investigated for identifying local linear dynamic models from flight data in real time. One of these methods [4,6] is based on a recursive Fourier transform and equation-error modeling in the frequency domain.…”

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confidence: 99%

Real-Time Dynamic Modeling: Data Information Requirements and Flight-Test Results

Morelli

Smith

2009

Journal of Aircraft

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Practical aspects of identifying dynamic models for aircraft in real time were studied. Topics included the formulation of an equation-error method in the frequency domain to estimate nondimensional stability and control derivatives in real time, data information content for accurate modeling results, speed of convergence, and data information management techniques such as data forgetting, incorporating prior information, and optimized excitation. Real-time dynamic modeling was applied to simulation data and flight-test data from a modified F-15B fighter aircraft and to operational flight data from a subscale jet transport aircraft. Estimated parameter standard errors, prediction cases, and comparisons with results from postflight analysis using the output-error method in the time domain were used to demonstrate the accuracy of the identified real-time models.Nomenclature a x , a y , a z = body-axis translational accelerometer measurements, g or ft=s

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Estimating short-period dynamics using an extended Kalman filter

Cited by 6 publications

References 2 publications

Active self-healing mechanisms for discrete dynamic structures

Active self-healing mechanisms for discrete dynamic structures

Autonomous Control Agents for Adaptive MAV Mission Profiles

Real-Time Dynamic Modeling: Data Information Requirements and Flight-Test Results

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