Steering angle adaptive estimation system based on GNSS and MEMS gyro

Miao, Cunxiao; Chu, Huanxin; Cao, Juanjuan; Sun, Ze; Yi, Ranran

doi:10.1016/j.compag.2018.08.019

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…4) Recurrent layer: The output of this layer node depends not only on the current firing strength, but also ont the previous firing strength. In this layer, 1 z − denotes a time delay. The current output of this layer is represented as:…”

Section: Double Recurrent Perturbation Fuzzy Neural Networkmentioning

confidence: 99%

“…With the advancement of microelectromechanical system (MEMS) technology, the production of sensors based on chip is gradually realized, such as accelerometers, gyroscopes, piezoelectric elements, and magnetometers, in combination with the miniaturization of electronic devices. Micro gyroscopes a are used in automatic driving system [1], navigation system [2], automobile safety and other fields. Using various methods to reduce the structure error and disturbance in micro gyroscope can improve the control accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

2021

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In this paper, a fractional-order sliding mode control method based on a double recurrent perturbation fuzzy neural network (DRPFNN) is proposed for a micro gyroscope system with parameter uncertainty and external disturbance. The DRPFNN is used to realize the adaptive estimation of the unknown part, which ensures that the controller is independent of the accurate mathematical model. The sine-cosine perturbation membership function is used to deal with the uncertainty of rules in neural network, increasing the accuracy, reduce the calculation load, and simplifying computational complexity. In addition, double recurrent links are added to transmit more information with superior dynamic ability. Then, the proposed control system is composed of a DRPFNN estimator and a fractional-order sliding mode controller (FSMC). The fractional calculus operator is introduced into the sliding surface to improve the controller flexibility. The parameter adaptive laws in the neural network are designed to be adaptively stabilized to the optimal value. Finally, simulation studies verify the effectiveness of the proposed control method, showing it can obtain higher control accuracy and enhance robustness than the traditional sliding mode control method. INDEX TERMSDouble recurrent perturbation fuzzy neural network; Recurrent neural network; Sliding mode control; Fractional order; Micro gyroscope.

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Section: Double Recurrent Perturbation Fuzzy Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

2021

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“…However, they did not consider the utilization of the steering angle module in steering automation of the customized autonomous logistic vehicles. A novel GNSS and MEMS-based steering angle estimation method is proposed in Miao et al 7 The output from these sensors is given as an input to an adaptive Kalman filter to correct the errors precipitated by a gyroscope. This system has improved the performance as compared to hall sensors by 1°.…”

Section: Related Workmentioning

confidence: 99%

“…However, the GNSS/gyroscope based steering angle measurement systems are much reliant on the satellite data which have signal spoofing, precision, and jamming concerns. [7][8][9][10] Moreover, the TAS/electro-magnet based steering angle sensors return distorted signals when their positive and negative phase shafts switch arbitrarily which leads to continuous error in estimated steering angle. 11 Besides, the researchers [5][6][7][8][9][10][11] have not presented any scheme for the practical implementation of these sensors in the steering automation of customized self-driving vehicles.…”

Section: Introductionmentioning

confidence: 99%

Micro-electromechanical system based optimized steering angle estimation mechanism for customized self-driving vehicles

Butt

Riaz

Khalid

et al. 2021

Measurement and Control

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In an automated steering system of the self-driving vehicles, the steering wheel angle is measured by the absolute angular displacement sensors or relative angle sensors. However, these sensors either encompass global navigation satellite systems (GNSS)/gyroscope – Micro Electromechanical-Sensor (MEMS) based solutions or comprise of the complex gear-based mechanical structure which results in latency and additive bias in the accumulative steering angle assessment. To address these issues, we propose a novel steering angle assessment system based on enhanced gear mechanism along with the adapted rotation paradigm for the customized self-driving vehicles. Additionally, a digital signal processing system has been introduced to resolve the issues in the identification of absolute central and max-bounding steering wheels position in self-driving vehicles. In assistance with the proposed mechanism, an algorithm has also been proposed to optimize the computed steering angle to minimalize the effect of additive bias in the accuracy. The proposed mechanism has been installed in the customized self-driving testbed vehicle and rigor validation has been performed in the straight and curvy road scenarios. Finally, the comparison study has been carried out between the conventional relative sensor and the proposed mechanism to show the accuracy and effectiveness of the proposed mechanism in terms of error rate, stability, and deviation.

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“…[17] Huang et al [18] installed sensors such as encoders and inertial devices in a steering wheel to establish the relationship between the steering wheel and the steering angle of the front wheels to detect the steering angle. Miao et al [19] proposed a dual-antenna GNSS and MEMS gyroscope. For the rotation angle measurement system, the error between the measured wheel steering angle measured by a test and the output result of the absolute angle sensor was within 0.5°, and the error was within 1° during a curve test.…”

Section: Introduction mentioning

confidence: 99%

Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope

Wang¹,

Hu²,

He³

et al. 2022

International Journal of Agricultural and Biological Engineering

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Aiming at the application environment of paddy agricultural machinery with bumpy and undulating changes, the problems affecting the method for steering wheel angle measurement by MEMS gyroscope were analyzed, and a wheel angle measurement method combining Dual-MEMS gyroscope (dual MEMS gyroscope) and RTK-GNSS was designed. The adaptive weighting method was used to fuse the heading angle differentiation of RTK-GNSS, the MEMS gyroscope angle rate, and velocity data, and the rod-arm compensation was performed to accurately obtain the angle rates of the body and steering wheels of agricultural machinery; the difference between the combined angular rate of the steering wheel of the agricultural machinery and the angular rate of the agricultural machinery body was obtained, and the integrator is used to integrate the difference to get the wheel steering angle value, and the Kalman filter was designed to make feedback correction for the integration process of angle calculation to eliminate the errors caused by the gyroscope zero bias, random drift, and gyroscope rod arm effect, and to obtain the accurate value of wheel steering angle. A comparative test with the connecting rod wheel angle sensor was designed, and the results show that the maximum deviation is 4.99°, the average absolute average value is 1.61°, and the average standard deviation is 0.98°. The method in this study and the connecting rod wheel angle sensor were used on paddy farm machinery. The wheel angle measurement deviation of the proposed method and the connecting rod wheel angle sensor was not more than 1°, which is relatively small. It has good stability, speed adaptability, and dynamic responsiveness that meets the accuracy requirements of steering wheel angle measurement for paddy field agricultural machinery unmanned driving and can be used instead of connecting rod angle sensors for unmanned agricultural machinery.

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Steering angle adaptive estimation system based on GNSS and MEMS gyro

Cited by 12 publications

References 7 publications

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

Micro-electromechanical system based optimized steering angle estimation mechanism for customized self-driving vehicles

Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope

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