PSO Aided Adaptive Complementary Filter for Attitude Estimation

Poddar, Shashi; Narkhede, Parag; Kumar, V. K.; Kumar, Amod

doi:10.1007/s10846-017-0507-8

Cited by 20 publications

(9 citation statements)

References 29 publications

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“…During simulation, the gain parameter K P for Mahony’s and the β parameter for Madgwick’s CF is set as shown in Table 2 (Poddar et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

“…However, the accuracy of this scheme is dependent on an external sensor and the trajectory under consideration. Some of the recent works toward adaptive CF aims at obtaining the gain parameters automatically with the help of particle swarm optimization (Poddar et al, 2017) and multi-model adaptive CF framework with different gain values for linear CF (LCF) (Kottath et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Least square estimation-based adaptive complimentary filter for attitude estimation

Narkhede

Raj

Kumar

et al. 2018

Transactions of the Institute of Measurement and Control

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Attitude estimation is one of the core fundamentals for navigation of unmanned vehicles and other robotic systems. With the advent of low cost and low accuracy micro-electro-mechanical systems (MEMS) based inertial sensors, these devices are used ubiquitously for all such commercial grade systems that need motion information. However, these sensors suffer from time-varying bias and noise parameters, which need to be compensated during system state estimation. Complementary filtering is one of such techniques that is used here for estimating attitude of a moving vehicle. However, the complementary filter structure is dependent on user fed gain parameters, KP and KI and needs a mechanism by which they can be obtained automatically. In this paper, an attempt has been made towards addressing this issue by applying least square estimation technique on the error obtained between estimated and measured attitude angles. The proposed algorithm simplifies the design of nonlinear complementary filter by computing the filter gains automatically. The experimental investigation has been carried out over several datasets, confirming the advantage of obtaining gain parameters automatically for the complementary filtering structure.

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“…During simulation, the gain parameter K P for Mahony’s and the β parameter for Madgwick’s CF is set as shown in Table 2 (Poddar et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Least square estimation-based adaptive complimentary filter for attitude estimation

Narkhede

Raj

Kumar

et al. 2018

Transactions of the Institute of Measurement and Control

Self Cite

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“…A fixed weight factor is implemented in linear CF [ 12 ], but the fixed gains cannot converge properly under complex circumstances. While neglecting any motion accelerations, the error e which is shown in Figure 3 is used as a cost-function, then intelligence stochastic searching algorithm [ 22 ] and the least square error method [ 26 ] are introduced to obtain optimal

and

. Gain-scheduled CF is introduced [ 18 ], the gains are selected from three predetermined values and switched discontinuously.…”

Section: Toolface Estimation Methodsmentioning

confidence: 99%

“…For the noiseless measurement, the

can be defined as a constant, i.e.,

being the termed weighting factor. This type of CF is named linear CF [ 22 ] or first order CF [ 12 ], but it does not act as HPF or LPF.…”

Section: Preliminariesmentioning

confidence: 99%

Dynamic Toolface Estimation for Rotary Steerable Drilling System

Wang

Geng

Wang

et al. 2018

Sensors

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In drilling engineering, Toolface is an angle used to describe bit direction. It is a challenging task to accurately estimate Toolface while drilling because of the downhole harsh conditions, but it is a primary step for the dynamic point-the-bit rotary steerable system (DPRSS). A new dynamic Toolface estimator is present, which fuses measurements from two accelerometers and one gyro. A dual-accelerometer Toolface measuring method is designed to compensate the circumferential acceleration of DPRSS. A nonlinear Complementary Filter (CF) is used to suppress the effect of vibration and axial acceleration. The frequency-domain characteristics of nonlinear CF are analyzed and its natural frequency is determined adaptively based on real time drilling conditions. This new estimator is validated on a DPRSS prototype under typical drilling modes; it is demonstrated with high robustness and follows the references satisfactorily.

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“…In [14], a multiple model adaptive complementary filter algorithm is proposed. In [15], Poddar et al studied the combination of PSO algorithm and complementary filtering algorithm. In [16], Li et al designed a method for correcting external acceleration.…”

Section: Introductionmentioning

confidence: 99%

A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation

Wen

Liu

Huang

et al. 2019

Sensors

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There are many algorithms that can be used to fuse sensor data. The complementary filtering algorithm has low computational complexity and good real-time performance characteristics. It is very suitable for attitude estimation of small unmanned aerial vehicles (micro-UAVs) equipped with low-cost inertial measurement units (IMUs). However, its low attitude estimation accuracy severely limits its applications. Though, many methods have been proposed by researchers to improve attitude estimation accuracy of complementary filtering algorithms, there are few studies that aim to improve it from the data processing aspect. In this paper, a real-time first-order differential data processing algorithm is proposed for gyroscope data, and an adaptive adjustment strategy is designed for the parameters in the algorithm. Besides, the differential-nonlinear complementary filtering (D-NCF) algorithm is proposed by combine the first-order differential data processing algorithm with the basic nonlinear complementary filtering (NCF) algorithm. The experimental results show that the first-order differential data processing algorithm can effectively correct the gyroscope data, and the Root Mean Square Error (RMSE) of attitude estimation of the D-NCF algorithm is smaller than when the NCF algorithm is used. The RMSE of the roll angle decreases from 1.1653 to 0.5093, that of the pitch angle decreases from 2.9638 to 1.5542, and that of the yaw angle decreases from 0.9398 to 0.6827. In general, the attitude estimation accuracy of D-NCF algorithm is higher than that of the NCF algorithm.

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PSO Aided Adaptive Complementary Filter for Attitude Estimation

Cited by 20 publications

References 29 publications

Least square estimation-based adaptive complimentary filter for attitude estimation

Least square estimation-based adaptive complimentary filter for attitude estimation

Dynamic Toolface Estimation for Rotary Steerable Drilling System

A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation

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