scispace - formally typeset
Search or ask a question
Journal ArticleDOI

A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter.

23 Sep 2020-Sensors (Multidisciplinary Digital Publishing Institute)-Vol. 20, Iss: 19, pp 5459
TL;DR: To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed and an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data.
Abstract: To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coal rock structure and high-power equipment of shearer in automatic coal mining working face. Roll and pitch angles are estimated using the MEMS gyroscope and accelerometer, and the accuracy is not reliable with time. In order to eliminate the measurement error of the sensors and obtain the high-accuracy attitude estimation of the system, an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data. Then the gradient descent algorithm is used to optimize the key parameter of unscented Kalman filter, namely process noise covariance, to improve the accuracy of attitude calculation. Finally, an experiment and industrial application show that the average measurement error of yaw angle is less than 2° and that of pitch angle and roll angle is less than 1°, which proves the efficiency and feasibility of the proposed system and method.
Citations
More filters
Posted Content
01 Jan 2013
TL;DR: The SMC^2 algorithm proposed in this paper is a sequential Monte Carlo algorithm, defined in the theta-dimension, which propagates and resamples many particle filters in the x-dimension.
Abstract: We consider the generic problem of performing sequential Bayesian inference in a state-space model with observation process y, state process x and fixed parameter theta. An idealized approach would be to apply the iterated batch importance sampling (IBIS) algorithm of Chopin (2002). This is a sequential Monte Carlo algorithm in the theta-dimension, that samples values of theta, reweights iteratively these values using the likelihood increments p(y_t|y_1:t-1, theta), and rejuvenates the theta-particles through a resampling step and a MCMC update step. In state-space models these likelihood increments are intractable in most cases, but they may be unbiasedly estimated by a particle filter in the x-dimension, for any fixed theta. This motivates the SMC^2 algorithm proposed in this article: a sequential Monte Carlo algorithm, defined in the theta-dimension, which propagates and resamples many particle filters in the x-dimension. The filters in the x-dimension are an example of the random weight particle filter as in Fearnhead et al. (2010). On the other hand, the particle Markov chain Monte Carlo (PMCMC) framework developed in Andrieu et al. (2010) allows us to design appropriate MCMC rejuvenation steps. Thus, the theta-particles target the correct posterior distribution at each iteration t, despite the intractability of the likelihood increments. We explore the applicability of our algorithm in both sequential and non-sequential applications and consider various degrees of freedom, as for example increasing dynamically the number of x-particles. We contrast our approach to various competing methods, both conceptually and empirically through a detailed simulation study, included here and in a supplement, and based on particularly challenging examples.

50 citations

Journal ArticleDOI
TL;DR: The measurement model based on the kinematics of advanced hydraulic support is established, the calculation algorithm of yaw angle and roll angle based on ultrasonic ranging data is proposed, and the platform of attitude monitoring system is built to realize the attitude monitoring ofadvanced hydraulic support based on multi-sensor.

11 citations

Journal ArticleDOI
20 Feb 2021-Sensors
TL;DR: In this article, a microelectromechanical system (MEMS) accelerometer was used as a measuring element in the electrohydraulic drive control system, based on the value determined from the simplified model implemented on the controller.
Abstract: Various distance sensors are used as measuring elements for positioning linear electrohydraulic drives. The most common are magnetostrictive transducers or linear variable differential transformer (LVDT) sensors mounted inside the cylinder. The displacement of the actuator’s piston rod is proportional to the change in the value of the current or voltage at the output from the sensor. They are characterized by relatively low measurement noise. The disadvantage of presented sensors is the need to mount them inside the cylinders and the high price. The article presents preliminary research on the replacement of following sensors and the use of a microelectromechanical system (MEMS) accelerometer as a measuring element in the electrohydraulic drive control system. The control consisted of two phases: at first, the signal from the acceleration sensor was analyzed during the actuator movement, based on the value determined from the simplified model implemented on the controller. In the range of motion in which the dynamics were the lowest, the signal was integrated and the obtained value was used in the second phase of motion. In the correction phase, a new set point was determined. Conducting the research required building a dedicated research stand. The author conducted the simulation and experimental research.

8 citations

Journal ArticleDOI
D. A. Tolmachev1
TL;DR: In this paper , the attitude monitoring of advanced hydraulic support in the harsh underground environment has been investigated, where the fusion technology of wide-angle ultrasonic sensor and inclination sensor is applied.

8 citations

References
More filters
Proceedings ArticleDOI
11 Mar 2010
TL;DR: This paper describes and implements a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking, which represents an extended PDR methodology (IEz+) valid for operation in indoor spaces with local magnetic disturbances.
Abstract: The estimation of the position of a person in a building is a must for creating Intelligent Spaces. State-of-the-art Local Positioning Systems (LPS) require a complex sensornetwork infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we describe and implement a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking. IEZ makes use of an Extended Kalman filter (EKF), an INS mechanization algorithm, a Zero Velocity Update (ZUPT) methodology, as well as, a stance detection algorithm. As the IEZ methodology is not able to estimate the heading and its drift (non-observable variables), then several methods are used for heading drift reduction: ZARU, HDR and Compass. The main contribution of the paper is the integration of the heading drift reduction algorithms into a Kalman-based IEZ platform, which represents an extended PDR methodology (IEZ+) valid for operation in indoor spaces with local magnetic disturbances. The IEZ+ PDR methodology was tested in several simulated and real indoor scenarios with a low-performance IMU mounted on the foot. The positioning errors were about 1% of the total travelled distance, which are good figures if compared with other works using IMUs of higher performance.

460 citations


"A Portable Support Attitude Sensing..." refers methods in this paper

  • ...In order to reduce yaw drift of attitude rotation estimation in indoor scenarios, an improved EKF combining INS mechanization algorithm and zero velocity update methodology is proposed to improve the accuracy of yaw estimation [27], but the Jacobian matrix is used to update the state transition matrix and the observation matrix in the linearization steps of system equation in attitude determination, which leads to poor stability and large estimation bias....

    [...]

Posted Content
TL;DR: In a number of examples, it is shown that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.
Abstract: Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.

298 citations


"A Portable Support Attitude Sensing..." refers background in this paper

  • ...set, which can be used in any state space models [33,34]....

    [...]

Journal ArticleDOI
TL;DR: Particle learning (PL) as mentioned in this paper extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles.
Abstract: Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.

291 citations

Posted Content
TL;DR: The SMC2 algorithm is proposed, a sequential Monte Carlo algorithm, defined in the θ‐dimension, which propagates and resamples many particle filters in the x‐ dimension, which explores the applicability of the algorithm in both sequential and non‐sequential applications and considers various degrees of freedom.
Abstract: We consider the generic problem of performing sequential Bayesian inference in a state-space model with observation process y, state process x and fixed parameter theta. An idealized approach would be to apply the iterated batch importance sampling (IBIS) algorithm of Chopin (2002). This is a sequential Monte Carlo algorithm in the theta-dimension, that samples values of theta, reweights iteratively these values using the likelihood increments p(y_t|y_1:t-1, theta), and rejuvenates the theta-particles through a resampling step and a MCMC update step. In state-space models these likelihood increments are intractable in most cases, but they may be unbiasedly estimated by a particle filter in the x-dimension, for any fixed theta. This motivates the SMC^2 algorithm proposed in this article: a sequential Monte Carlo algorithm, defined in the theta-dimension, which propagates and resamples many particle filters in the x-dimension. The filters in the x-dimension are an example of the random weight particle filter as in Fearnhead et al. (2010). On the other hand, the particle Markov chain Monte Carlo (PMCMC) framework developed in Andrieu et al. (2010) allows us to design appropriate MCMC rejuvenation steps. Thus, the theta-particles target the correct posterior distribution at each iteration t, despite the intractability of the likelihood increments. We explore the applicability of our algorithm in both sequential and non-sequential applications and consider various degrees of freedom, as for example increasing dynamically the number of x-particles. We contrast our approach to various competing methods, both conceptually and empirically through a detailed simulation study, included here and in a supplement, and based on particularly challenging examples.

258 citations


"A Portable Support Attitude Sensing..." refers methods in this paper

  • ...However, the particle filter is a sequential importance sampling filtering method based on Bayesian estimation [32] and its idea is based on the Monte Carlo method to represent probability with particle...

    [...]

Journal ArticleDOI
TL;DR: The SMC^2 algorithm proposed in this paper is a sequential Monte Carlo algorithm, defined in the theta-dimension, which propagates and resamples many particle filters in the x-dimension.
Abstract: We consider the generic problem of performing sequential Bayesian inference in a state-space model with observation process y, state process x and fixed parameter theta. An idealized approach would be to apply the iterated batch importance sampling (IBIS) algorithm of Chopin (2002). This is a sequential Monte Carlo algorithm in the theta-dimension, that samples values of theta, reweights iteratively these values using the likelihood increments p(y_t|y_1:t-1, theta), and rejuvenates the theta-particles through a resampling step and a MCMC update step. In state-space models these likelihood increments are intractable in most cases, but they may be unbiasedly estimated by a particle filter in the x-dimension, for any fixed theta. This motivates the SMC^2 algorithm proposed in this article: a sequential Monte Carlo algorithm, defined in the theta-dimension, which propagates and resamples many particle filters in the x-dimension. The filters in the x-dimension are an example of the random weight particle filter as in Fearnhead et al. (2010). On the other hand, the particle Markov chain Monte Carlo (PMCMC) framework developed in Andrieu et al. (2010) allows us to design appropriate MCMC rejuvenation steps. Thus, the theta-particles target the correct posterior distribution at each iteration t, despite the intractability of the likelihood increments. We explore the applicability of our algorithm in both sequential and non-sequential applications and consider various degrees of freedom, as for example increasing dynamically the number of x-particles. We contrast our approach to various competing methods, both conceptually and empirically through a detailed simulation study, included here and in a supplement, and based on particularly challenging examples.

258 citations