High-precision roll attitude estimation of decoupled canards relative to the projectile body using bipolar hall-effect sensors

2021 ◽  
pp. 1-9
Author(s):  
Tingting Yin ◽  
Zhong Yang ◽  
Youlong Wu ◽  
Fangxiu Jia
Keyword(s):  
Hall Effect ◽  
Real Time ◽  
High Precision ◽  
Solution Method ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Fuzzy Algorithms ◽  
Positioning Method ◽  
Hall Effect Sensors

The high-precision roll attitude estimation of the decoupled canards relative to the projectile body based on the bipolar hall-effect sensors is proposed. Firstly, the basis engineering positioning method based on the edge detection is introduced. Secondly, the simplified dynamic relative roll model is established where the feature parameters are identified by fuzzy algorithms, while the high-precision real-time relative roll attitude estimation algorithm is proposed. Finally, the trajectory simulations and grounded experiments have been conducted to evaluate the advantages of the proposed method. The positioning error is compared with the engineering solution method, and it is proved that the proposed estimation method has the advantages of the high accuracy and good real-time performance.

Synergistic real-time compensation of tracking and contouring errors for precise parametric curved contour following systems

2017 ◽  
Vol 232 (19) ◽  
pp. 3367-3383 ◽  
Author(s):  
De-Ning Song ◽  
Jian-Wei Ma ◽  
Zhen-Yuan Jia ◽  
Feng-Ze Qin ◽  
Xiao-Xuan Zhao
Keyword(s):  
Real Time ◽  
High Precision ◽  
Tracking Error ◽  
Estimation Algorithm ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
System Structure ◽  
Parametric Curve ◽  
Contouring Error ◽  
Contour Following

The tracking and contouring errors are inevitable in real computer numerical control contour following because of the reasons such as servo delay and dynamics mismatch. In order to improve the motion accuracy, this paper proposes a synergistic real-time compensation method of tracking and contouring errors for precise parametric curve following of the computer numerical control systems. The tracking error for each individual axis is first compensated, by using the feed-drive models with the consideration of model uncertainties, to enhance the tracking performances of all axes. Further, the contouring error is estimated and compensated to improve the contour accuracy directly, where a high-precision contouring-error estimation algorithm, based on spatial circular approximation of the desired contour neighboring the actual motion position, is presented. Considering that the system structure is coupled after compensation, the stability of the coupled system is analyzed for design of the synergistic compensator. Innovative contributions of this study are that not only the contouring-error can be estimated with a high precision in real time, but also the tracking and contouring performances can be simultaneously improved although there exist modeling errors and disturbances. Simulation and experimental tests demonstrate the effectiveness and advantages of the proposed method.

scholarly journals Real-Time Estimation of Glenohumeral Joint Rotation Center With Cable-Driven Arm Exoskeleton (CAREX)—A Cable-Based Arm Exoskeleton

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Ying Mao ◽  
Xin Jin ◽  
Sunil K. Agrawal
Keyword(s):  
Real Time ◽  
Glenohumeral Joint ◽  
Kinematic Model ◽  
Estimation Method ◽  
Time Estimation ◽  
Estimation Algorithm ◽  
Joint Rotation ◽  
Rotation Center ◽  
Arm Rehabilitation ◽  
Novel Approach

In the past few years, the authors have proposed several prototypes of a Cable-driven upper ARm EXoskeleton (CAREX) for arm rehabilitation. One of the assumptions of CAREX was that the glenohumeral joint rotation center (GH-c) remains stationary in the inertial frame during motion, which leads to inaccuracy in the kinematic model and may hamper training performance. In this paper, we propose a novel approach to estimate GH-c using measurements of shoulder joint angles and cable lengths. This helps in locating the GH-c center appropriately within the kinematic model. As a result, more accurate kinematic model can be used to improve the training of human users. An estimation algorithm is presented to compute the GH-c in real-time. The algorithm was implemented on the latest prototype of CAREX. Simulations and preliminary experimental results are presented to validate the proposed GH-c estimation method.

scholarly journals Real-Time On-Orbit Estimation Method for Microthruster Thrust Based on High-Precision Orbit Determination

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qinglin Yang ◽  
Weijing Zhou ◽  
Hao Chang
Keyword(s):  
Real Time ◽  
High Precision ◽  
Orbit Determination ◽  
Estimation Method ◽  
Reference Value ◽  
Space Applications ◽  
Measuring Device ◽  
Cubature Kalman Filter ◽  
Precision Orbit Determination ◽  
Orbit Estimation

In order to enable the micro-nanosatellites equipped with microthrusters to better complete various space applications, it is necessary to estimate the thrust performance of the microthrusters in real-time on orbit. This paper proposes a real-time on-orbit estimation method for microthrust based on high-precision orbit determination. By establishing a high-precision orbit dynamic model, the microthrust generated by a microthruster is modeled as a first-order Markov model, combined with a high-precision GNSS measuring device, and the satellite position is obtained through the cubature Kalman filter algorithm, velocity, and thrust real-time on-orbit estimates. For a thrust of 100 μN, the error accuracy of the on-orbit estimation is 3.98%; for a thrust of 500 μN, the error accuracy is 1.79%; for a thrust of 5 mN, the error accuracy can be reduced to 1.43%; and when the thrust is 500 μN, the accuracy of orbit determination is 16 cm. This method solves the problem that the traditional on-orbit thrust estimation method cannot perform real-time on-orbit estimation of microthrust on the order of hundreds of μN. The real-time on-orbit estimation of microthrust of micro-nanosatellites equipped with microthrusters of the order of hundreds of micronewtons or even several mN to tens of mN has certain reference value.

scholarly journals A Fast Weakly-Coupled Double-Layer ESKF Attitude Estimation Algorithm and Application

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1465
Author(s):  
Yue Yang ◽  
Xiaoxiong Liu ◽  
Weiguo Zhang ◽  
Xuhang Liu ◽  
Yicong Guo
Keyword(s):  
Real Time ◽  
Double Layer ◽  
Short Range ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Sensor Data ◽  
Test Environment ◽  
Attitude Error ◽  
Weakly Coupled ◽  
Solution Accuracy

Aimed at the problem of small unmanned aerial vehicle (UAV) attitude solution accuracy and real-time performance in short-range navigation flight, in this paper, we propose a fast weakly-coupled double-layer error-state Kalman filter (DL-ESKF) attitude estimation algorithm. Considering the application of short-range navigation, we designed an improved attitude error model for low-cost gyroscope/accelerometer/magnetometer devices. In addition, we reasonably simplified certain factors that affect the attitude solution to reduce the filtering calculation burden. For the data coupling phenomenon caused by the different sampling frequencies of the attitude sensor data in the filtering process, we designed a new attitude algorithm combined with the ESKF and hierarchical filter. The first layer of filters used an accelerometer and the second layer used a magnetometer to correct the attitude error. We also built an offline and real-time test platform to verify the performance of the proposed algorithm in a simulation and flight test environment compared with the classic attitude algorithms. The experimental results demonstrated that the proposed algorithm not only improved the attitude solution accuracy and stability but also reduced the filter running time.

scholarly journals Pipeline Gaussian Particle Filter and Hardware Design for Attitude Estimation with UAV

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Yali Xue ◽  
Hu Chen ◽  
Jie Chen ◽  
Jiahui Wang
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Gaussian Noise ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Gaussian State ◽  
Time Performance ◽  
Simulation Results ◽  
Non Gaussian

This paper based on the Gaussian particle filter (GPF) deals with the attitude estimation of UAV. GPF algorithm has better estimation accuracy than the general nonlinear non-Gaussian state estimation and is usually used to improve the system’s real-time performance whose noise is specific such as Gaussian noise during the mini UAV positioning and navigation. The attitude estimation algorithm is implemented on FPGA to verify the effectiveness of the Gaussian particle filter. Simulation results have illustrated that the GPF algorithm is effective and has better real-time performance than that of the particle filter.

Attitude Estimation with Complementary Filter

2010 ◽  
Vol 44-47 ◽  
pp. 3781-3784
Author(s):  
Rui Hua Chang ◽  
Xiao Dong Mu ◽  
Xiao Wei Shen
Keyword(s):  
Solid State ◽  
Real Time ◽  
Inertial Sensors ◽  
Low Cost ◽  
Estimation Error ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Complementary Filter

An attitude estimation method is presented for a robot using low-cost solid-state inertial sensors. The attitude estimates are obtained from a complementary filter by combining the measurements from the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The results show that the estimation error is less than 1 degree compare to the reference attitude. It is a simple, yet effective method for attitude estimation, suitable for real-time implementation on a robot.

scholarly journals Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2828
Author(s):  
Sara Luciani ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Andrea Tonoli
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Data Driven ◽  
Raspberry Pi ◽  
Hardware In The Loop

This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (SOC) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion battery that is numerically modeled via an RC equivalent circuit model that can also consider the environmental temperature influence. The proposed estimation technique relies on nonlinear autoregressive with exogenous input (NARX) artificial neural networks (ANNs) that are properly trained with multiple datasets. Those datasets include modeled current and voltage data, both for charge-sustaining and charge-depleting working conditions. The investigated method is then experimentally validated using a Raspberry Pi 4B card-sized board, on which the estimation algorithm is actually deployed, and real-time hardware, on which the battery model is developed, namely a Speedgoat baseline platform. These hardware platforms are used in a hardware-in-the-loop architecture via the UPD communication protocol, allowing the system to be validated in a proper testing environment. The resulting estimation algorithm can estimate the battery SOC in real-time, with 2% accuracy during real-time hardware testing.

scholarly journals High precision positioning system for autopilot based on multi-sensor fusion

2021 ◽  
Vol 257 ◽  
pp. 02061
Author(s):  
Haoru Luo ◽  
Kechun Liu
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
High Precision ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Positioning System ◽  
Signal Loss ◽  
Automatic Driving ◽  
Core Technology ◽  
Positioning Method

For autonomous vehicles, autonomous positioning is a core technology in their development. A good positioning system not only helps them efficiently complete autonomous operations, but also improves safety performance. At present, the use of global positioning system (GPS) is a more mainstream positioning method, but in indoor, serious shelter and other environments, GPS signal loss will lead to positioning failure. In order to solve this problem, this paper proposes a method of mapping before positioning, and designs a set of high precision real-time positioning system by combining the technology of multi-sensor fusion. The designed system was carried on a Wuling sightseeing bus, and the mapping and positioning tests were carried out in the Nanhu Campus of Wuhan University of Technology, the East Campus of Mafangshan Campus and the underground garage where GPS signals were lost. The test results show that the system can realize the high precision real-time positioning function of the autonomous vehicle. Therefore, the in-depth study and implementation of this system is of great significance to the promotion and application of the automatic driving industry.

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