A Ship Flotation Control System Based on Pump-Driven Tank

2011 ◽  
Author(s):  
Juan Guo ◽  
Meng Tang ◽  
Zaojian Zou
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Structural Damage ◽  
Model Simulation ◽  
Theoretical Calculations ◽  
Wave Disturbance ◽  
Ship Motion ◽  
State Variables ◽  
Initial State ◽  
Asymmetric Loading

Extensive development in ship motion control strategies and systems in recent decades has called for higher requirements in control system accuracy and reliability. In this paper, a ship flotation control system based on pump-driven active tank is established. A special case is discussed, where the ship is heeling under an asymmetric loading either by structural damage or asymmetric consumption of ammunition. The purpose of the control system is to keep the ship in upright floating position in waves by transferring liquid between the tanks. Kalman filter is designed to eliminate the wave disturbance, in order to identify the heeling angle caused by asymmetric loading change. The effect of wave disturbance at different wave encounter angles, wave heights, as well as ship speeds is analyzed. Tuning of filter parameters such as initial state variables, initial error covariance and noise covariance is performed to achieve better filtering performance for different parameters of waves and ship motion. To verify the control model, simulation is conducted for a 3340t ship and the simulation results are compared with the theoretical calculations. The research results show the applicability and efficiency of Kalman filter. The concept of the control system presented in the paper is helpful to improve ship stability and safety when ship upright floating condition is disturbed.

Sensor Fault Diagnosis for an UAV Control System Based on a Strong Tracking Kalman Filter

2014 ◽  
Vol 687-691 ◽  
pp. 270-274 ◽  
Author(s):  
Feng Tian ◽  
Jian Yang Zheng ◽  
Tong Zhang
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Fault Diagnosis ◽  
Real Time ◽  
Flight Control ◽  
Joint Estimation ◽  
Filter Method ◽  
State Variables ◽  
Flight Control System ◽  
Sensor Fault

The fault diagnosis of unmanned aerial vehicle (UAV) flight control system is an important research of UAV in health management. The sensor is the link which easiest to have problems of the flight control system. Making timely and accurate prediction of its faults is particularly important. A strong tracking Kalman Filter method for the sensor fault diagnosis of UAV flight control system was presented in this paper. The parameters of the system were extended to the state variables, the sensor fault observer was constructed, and the joint estimation of states and parameters of flight control system were gotten. The method can be used to real-time estimate the unmeasured states and time-varying parameters. The results of simulation experiments show that the method has a good real-time and accuracy in the sensor fault diagnosis of flight control system.

Simultaneous estimation of state and unknown road roughness input for vehicle suspension control system based on discrete Kalman filter

2019 ◽  
Vol 234 (6) ◽  
pp. 1610-1622
Author(s):  
Gi-Woo Kim ◽  
Sun-Woo Kang ◽  
Jung-Sik Kim ◽  
Jong-Seok Oh
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
The State ◽  
Vehicle Suspension ◽  
Simultaneous Estimation ◽  
Unknown Input ◽  
State Variables ◽  
The Road ◽  
Road Roughness ◽  
Suspension Control

This study presents an improved discrete Kalman filter for simultaneously estimating both all state variables and the unknown road roughness input for a vehicle suspension control system that plays a key role in the ride quality and handling performance while driving the vehicle. The suspension system is influenced by the road roughness input, which causes undesirable vibrations associated with vehicle instability. It is therefore important to estimate the road roughness and state variables information when designing the model-based controller for the vehicle suspension control system associated with the vehicle’s vertical dynamics. However, the implementation of conventional estimation theories for the suspension control system is challenging because the road roughness acts as an unknown input and is difficult to be measured or estimated while driving. This study presents an improved Kalman filter with unknown input, which can simultaneously estimate the state variables and road roughness without any prior information about the vehicle suspension control system. The proposed road roughness input estimator is evaluated by using an in-vehicle test bed with a laser-type profilometer. Finally, the state estimation performance of the proposed estimator for a vehicle suspension control system is validated by using CarSim software.

High-precision Mars Entry Integrated Navigation Under Large Uncertainties

2013 ◽  
Vol 67 (2) ◽  
pp. 327-342 ◽  
Author(s):  
Shuang Li ◽  
Xiuqiang Jiang ◽  
Yufei Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Precision ◽  
Integrated Navigation ◽  
State Variables ◽  
Parameter Uncertainties ◽  
Initial State ◽  
Navigation Algorithm ◽  
Radio Measurements ◽  
Six Degree Of Freedom

In this paper, we present a high-precision Mars entry integrated navigation algorithm under large uncertainties via a desensitised extended Kalman filter (DEKF). Firstly, a new six degree-of-freedom Mars entry dynamics model is derived based on the angular velocity outputs of a gyro, which is free of modelling errors in the aerodynamic and control torques. Secondly, both the accelerometer outputs and radio measurements between orbiters and entry vehicle are used as the observations embedded in a navigation filter to perform state estimation and suppress the measurement noise. Finally, a desensitised extended Kalman filter, exhibiting the desirable property of efficiently reducing the sensitivity of state variables with respect to model and parameter uncertainties, is adopted in order to overcome the adverse effects of initial state errors and uncertainties during Mars atmospheric entry and further improve entry navigation accuracy. The numerical simulation results show that the DEKF-based integrated navigation algorithm developed in this paper can achieve a better navigation performance with higher accuracy when compared with the standard extended Kalman filter (EKF)-based integrated navigation algorithm in the presence of larger state errors and parameter uncertainties.

scholarly journals Parameters Estimation and Prediction of Water Movement and Solute Transport in Layered, Variably Saturated Soils Using the Ensemble Kalman Filter

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1520
Author(s):  
Zheng Jiang ◽  
Quanzhong Huang ◽  
Gendong Li ◽  
Guangyong Li
Keyword(s):  
Kalman Filter ◽  
Solute Transport ◽  
Ensemble Kalman Filter ◽  
Water Movement ◽  
Parameters Estimation ◽  
Saturated Soils ◽  
Hydrological Models ◽  
State Variables ◽  
Ensemble Size ◽  
Highly Nonlinear

The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling method. However, this type of method cannot fully consider the uncertainties of measurements, boundary conditions, and parameters, resulting in inaccurate estimations of parameters and predictions of state variables. The ensemble Kalman filter (EnKF) is well-suited to data assimilation and parameter prediction in Situations with large numbers of variables and uncertainties. Thus, in this study, the EnKF was used to estimate the parameters of water movement and solute transport in layered, variably saturated soils. Our results indicate that when used in conjunction with the HYDRUS-1D software (University of California Riverside, California, CA, USA) the EnKF effectively estimates parameters and predicts state variables for layered, variably saturated soils. The assimilation of factors such as the initial perturbation and ensemble size significantly affected in the simulated results. A proposed ensemble size range of 50–100 was used when applying the EnKF to the highly nonlinear hydrological models of the present study. Although the simulation results for moisture did not exhibit substantial improvement with the assimilation, the simulation of the salinity was significantly improved through the assimilation of the salinity and relative solutetransport parameters. Reducing the uncertainties in measured data can improve the goodness-of-fit in the application of the EnKF method. Sparse field condition observation data also benefited from the accurate measurement of state variables in the case of EnKF assimilation. However, the application of the EnKF algorithm for layered, variably saturated soils with hydrological models requires further study, because it is a challenging and highly nonlinear problem.

scholarly journals An Initial Value Estimation Method for the Kalman and Extended Kalman Filters in Underground Metal Detection

2019 ◽  
Vol 9 (19) ◽  
pp. 4113 ◽  
Author(s):  
Yadong Wan ◽  
Zhen Wang ◽  
Peng Wang ◽  
Zhiyang Liu ◽  
Na Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Detection Methods ◽  
Polarizability Tensor ◽  
Filter Method ◽  
Excellent Performance ◽  
Initial Value ◽  
Initial State ◽  
Metal Detection ◽  
Value Estimation

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

Innovative Active Vehicle Safety Using Integrated Stabilizer Pendulum and Direct Yaw Moment Control

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Avesta Goodarzi ◽  
Fereydoon Diba ◽  
Ebrahim Esmailzadeh
Keyword(s):  
Control System ◽  
Model Simulation ◽  
Dynamic Control ◽  
Superior Performance ◽  
Steering Control ◽  
Pendulum System ◽  
Direct Yaw Moment Control ◽  
Yaw Moment Control ◽  
Moment Control ◽  
Yaw Moment

Basically, there are two main techniques to control the vehicle yaw moment. First method is the indirect yaw moment control, which works on the basis of active steering control (ASC). The second one being the direct yaw moment control (DYC), which is based on either the differential braking or the torque vectoring. An innovative idea for the direct yaw moment control is introduced by using an active controller system to supervise the lateral dynamics of vehicle and perform as an active yaw moment control system, denoted as the stabilizer pendulum system (SPS). This idea has further been developed, analyzed, and implemented in a standalone direct yaw moment control system, as well as, in an integrated vehicle dynamic control system with a differential braking yaw moment controller. The effectiveness of SPS has been evaluated by model simulation, which illustrates its superior performance especially on low friction roads.

Fuzzy-EKF Controller for Intelligent Wheelchair Navigation

2016 ◽  
Vol 25 (2) ◽  
pp. 107-121 ◽  
Author(s):  
Malek Njah ◽  
Mohamed Jallouli
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Energy Management ◽  
Research Laboratory ◽  
Control Method ◽  
Severe Disabilities ◽  
Indoor Navigation ◽  
Electric Wheelchair ◽  
Security Environment ◽  
Intelligent Wheelchair

AbstractThe electric wheelchair gives more autonomy and facilitates movement for handicapped persons in the home or in a hospital. Among the problems faced by these persons are collision with obstacles, the doorway, the navigation in a hallway, and reaching the desired place. These problems are due to the difficult manipulation of an electric wheelchair, especially for persons with severe disabilities. Hence, we tried to add more functionality to the standard wheelchair in order to increase movement range, security, environment access, and comfort. In this context, we have developed an automatic control method for indoor navigation. The proposed control system is mounted on the electric wheelchair for the handicapped, developed in the research laboratory CEMLab (Control and Energy Management Laboratory-Tunisia). The proposed method is based on two fuzzy controllers that ensure target achievement and obstacle avoidance. Furthermore, an extended Kalman filter was used to provide precise measurements and more effective data fusion localization. In this paper, we present the simulation and experimental results of the wheelchair navigation system.

Numerical Simulation of Ship Collision and Validations With Experimental Results

2021 ◽  
Author(s):  
Xiangbiao Wang ◽  
Chun Bao Li ◽  
Ling Zhu
Keyword(s):  
Numerical Simulation ◽  
Model Test ◽  
Structural Damage ◽  
Added Mass ◽  
Ship Motion ◽  
Time Dependent ◽  
Structural Deformation ◽  
Structure Interaction ◽  
Ship Collision ◽  
Good Agreement

Abstract Ship collision accidents occur from time to time in recent years, and this would cause serious consequences such as casualties, environmental pollution, loss of cargo on board, damage to the ship and its equipment, etc. Therefore, it is of great significance to study the response of ship motion and the mechanism of structural damage during the collision. In this paper, model experiments and numerical simulation are used to study the ship-ship collision. Firstly, the Coupled Eulerian-Lagrangian (CEL) was used to simulate the fluid-structure interaction for predicting structural deformation and ship motion during the normal ship-ship collision. Meanwhile, a series of model tests were carried out to validate the numerical results. The validation presented that the CEL simulation was in good agreement with the model test. However, the CEL simulation could not present the characteristics the time-dependent added mass.

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