A CKF based spatial alignment of radar and infrared sensors

More electric aircrafts (MEAs) comprise a vast amount of power electronic loads, which usually behave as constant power loads (CPLs). The incremental negative impedance of CPLs threatens system stability. To ensure an effective control of power flow in MEAs, eliminating the undesired behavior of CPLs is a necessity. This aim requires spontaneous power estimation of the time-varying uncertain loads. In this paper, an adaptive backstepping controller, which is interconnected to a 3 rd degree cubature Kalman filter (CKF), is developed for a DC MG feeding non-ideal CPLs. At first, the load power is considered as an artificial state and augmented into the system states, which enables estimation of not only the DC MG states but also the unknown value of the load power. The estimated load power is then forwarded to a backstepping controller. The systematic approach of this controller allows obtaining the control signal, which is the duty ratio of the switch, to not only system stabilizing but also tracking a desired voltage of the DC bus under the load power variations. The proposed adaptive controller is tested on a DC MG that has one CPL. The conducted experimental results verify the proposed nonlinear control in tracking the desired voltage of the DC bus under slow and fast variations of the load power.Index Terms-More electric aircraft power system, DC microgrid (MG), Constant power load (CPL), Cubature Kalman filter (CKF), Adaptive backstepping controller.

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“…After convergence of the filter, the last element of the estimated state is the estimated power of the CPL. The CKF steps are as follows [29]:…”

Section: Cubature Kalman Filter For Power Estimationmentioning

confidence: 99%

“…For asymptotic convergence of 2 toward the origin, it is needed that ̇2 < 0 when 2 ≠ 0. Considering ′ = −( + ) 2 + ( 2 − 1) 1 (47) where is a positive gain matrix, yields to ̇2 = − 1 2 − 2 2 < 0 (48) Using (29) and (40), (47) can be simplified as ′ = −( + )( 2 + 1 ) −…”

Section: Finding the Control Expression ′mentioning

confidence: 99%

Tracking Control for a DC Microgrid Feeding Uncertain Loads in More Electric Aircraft: Adaptive Backstepping Approach

Yousefizadeh

Bendtsen

Vafamand

et al. 2019

IEEE Trans. Ind. Electron.

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“…However, limited by the third-order cubature rule, it is impossible to accurately calculate the Gaussian weight integrals of some simple polynomial functions. Therefore, the estimation accuracy is still limited in some filtering problems (Jianjun et al , 2010). Particle filter (PF) can solve non-linear problems well and can be applied to non-Gaussian problems (Lamberti et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

A small UAV tracking algorithm based on AIMM-UKF

Hou

2021

AEAT

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Purpose The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs. Design/methodology/approach In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model. Findings Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value. Originality/value This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.

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A spatial alignment algorithm for time varying biases of radar and infrared sensors

Wang

Huang

2011

Procedia Engineering

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A CKF based spatial alignment of radar and infrared sensors

Cited by 10 publications

References 8 publications

Tracking Control for a DC Microgrid Feeding Uncertain Loads in More Electric Aircraft: Adaptive Backstepping Approach

Tracking Control for a DC Microgrid Feeding Uncertain Loads in More Electric Aircraft: Adaptive Backstepping Approach

A small UAV tracking algorithm based on AIMM-UKF

A spatial alignment algorithm for time varying biases of radar and infrared sensors

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