Critical Analysis of Control and Filtering Algorithms Used in Real Dynamic Positioning Systems

2005 ◽  
Author(s):  
Eduardo A. Tannuri ◽  
Helio M. Morishita ◽  
Vinicius L. M. Veras ◽  
Glenan A. Lago
Keyword(s):  
Kalman Filter ◽  
Critical Analysis ◽  
Wind Waves ◽  
Mathematical Formulation ◽  
Estimation Algorithm ◽  
Peak Frequency ◽  
Floating Body ◽  
Dynamic Positioning ◽  
Sea Waves ◽  
Positioning Systems

Dynamic positioning systems (DPS) comprise the utilization of active propulsion to maintain the position and heading of a vessel. Sensors are used to measure the actual position of the floating body, and a control algorithm is responsible for the calculation of forces to be applied to each propeller, in order to counteract all environmental forces, including wind, waves and current loads. The controller cannot directly compensate motions in the sea waves frequency range, since they would require an enormous power to be attenuated, possibly causing damage to the propeller system. A filtering algorithm is then used to separate high frequency components from the low frequency ones, which are indeed controlled. Usual commercial systems apply Kalman filtering technique to perform such task, which includes a full model of the system. Furthermore, an adaptive on-line estimation algorithm is also used to evaluate the wave peak frequency, since the model in Kalman Filter depends on such parameter. The controller itself is based on a simple proportional-derivative (PD) actions. This paper presents all the mathematical formulation of the Kalman Filter, adaptive algorithm and the controller used in commercial DPS and performs a critical analysis of those models. Some illustrative results of a dynamic positioned shuttle vessel are presented, considering the incidence of waves, current and winds.

Kalman Filter for Dynamic Positioning Ships Using Position and Acceleration Feedback

2014 ◽  
Vol 543-547 ◽  
pp. 2362-2367
Author(s):  
Ye Hai Xie
Keyword(s):  
Computer Simulation ◽  
Kalman Filter ◽  
Stability Theory ◽  
Linear Acceleration ◽  
Dynamic Positioning ◽  
Positioning Systems ◽  
Measured Position ◽  
Acceleration Feedback ◽  
The Stability

Considering the problem of dynamic positioning Systems for the slowly-varying disturbances, a new kalman filter using position and acceleration feedback is presented. The kalman filiter separates the WF motions from the measured position and linear acceleration, and estimates the LF position, velocity and acceleration. The stability of this filiter is proven by applying input-to-state (ISS) stability theory. Finally, the computer simulation is given to demonstrate the effectiveness of the proposed method.

scholarly journals Estimation of Observer Parameters for Dynamic Positioning Ships

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaogong Lin ◽  
Yehai Xie ◽  
Dawei Zhao ◽  
Shusheng Xu
Keyword(s):  
Computer Simulation ◽  
Spectral Analysis ◽  
Particle Swarm Optimization ◽  
Spectral Estimation ◽  
Peak Frequency ◽  
Adaptive Observer ◽  
Dynamic Positioning ◽  
Swarm Optimization ◽  
Positioning Systems ◽  
On Line

Considering the problem of dynamic positioning systems for the slowly varying disturbances, a parametrically adaptive observer is presented. The peak frequency of observer is adjusted on-line by autoregressive (AR) spectral estimation; other parameters of observer are optimized using particle swarm optimization (PSO). The peak frequency can be calculated by spectral analysis of the pitch, roll, and heave measurements. In the spectral estimation, Levinson-Durbin algorithm is used to solve the Yule-Walker equations. Finally, the computer simulation is given to demonstrate the effectiveness of the proposed method.

scholarly journals Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning

2021 ◽  
Vol 11 (15) ◽  
pp. 6805
Author(s):  
Khaoula Mannay ◽  
Jesús Ureña ◽  
Álvaro Hernández ◽  
José M. Villadangos ◽  
Mohsen Machhout ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Experimental Tests ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Smart Devices ◽  
Final Position ◽  
Positioning Systems ◽  
Local Positioning System ◽  
Tightly Coupled ◽  
Position Estimate

Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs.

Motion estimation algorithm with Kalman filter

1994 ◽  
Vol 30 (15) ◽  
pp. 1204-1206 ◽  
Author(s):  
C.-M. Kuo ◽  
P.-C. Lu ◽  
H.-C. Lin ◽  
C.-H. Hsieh
Keyword(s):  
Kalman Filter ◽  
Motion Estimation ◽  
Estimation Algorithm ◽  
Motion Estimation Algorithm

scholarly journals Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Siwen Guo ◽  
Jin Wu ◽  
Zuocai Wang ◽  
Jide Qian
Keyword(s):  
Kalman Filter ◽  
Process Model ◽  
Attitude Determination ◽  
Angular Rate ◽  
Estimation Algorithm ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Orientation Estimation ◽  
Kinematic Equation ◽  
Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

Aspects of Importance Related to Arctic DP Operations

2012 ◽  
Author(s):  
Arne Gürtner ◽  
Bror Henrik Heier Baardson ◽  
Glenn-Ole Kaasa ◽  
Erik Lundin
Keyword(s):  
Boundary Conditions ◽  
New Technology ◽  
Open Water ◽  
The Arctic ◽  
Dynamic Positioning ◽  
Positioning Systems ◽  
Business Opportunities ◽  
New Business ◽  
Drifting Ice

International operators are seeking, investigating and pursuing new business opportunities in the Arctic. While operating in the Arctic, there will be a considerable need for vessels to keep their position during various operations which may include lifting, installation, crew change, evacuation, and maybe drilling. Opposed to open water, the drifting ice poses severe limitations as to how stationkeeping operations may be carried out. Dynamic positioning systems are currently developed aiding stationkeeping without mooring systems. There is a considerable need to enhance the open water DP systems for use in a new forcing environment. Essentially a new technology has to be developed with time. For that reason, considerable knowledge is required concerning current limitations and boundary conditions. This paper addresses some of the generic challenges related to DP operations in ice together with relevant learnings which are employed in mentioned DP enhancements.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

scholarly journals A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

2018 ◽  
Vol 8 (11) ◽  
pp. 2028 ◽  
Author(s):  
Xin Lai ◽  
Dongdong Qiao ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Distribution Area ◽  
Model Parameters ◽  
Model Accuracy ◽  
Soc Estimation ◽  
Fuzzy Fusion ◽  
Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

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