scholarly journals Study of the Algorithm of Backtracking Decoupling and Adaptive Extended Kalman Filter Based on the Quaternion Expanded to the State Variable for Underwater Glider Navigation

Sensors ◽  
2014 ◽  
Vol 14 (12) ◽  
pp. 23041-23066 ◽  
Author(s):  
Haoqian Huang ◽  
Xiyuan Chen ◽  
Zhikai Zhou ◽  
Yuan Xu ◽  
Caiping Lv
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
The State ◽  
Underwater Glider ◽  
State Variable ◽  
Adaptive Extended Kalman Filter

Application of the Extended Kalman Filter to Control of a Shape Memory Alloy Arm

2003 ◽  
Author(s):  
Mohammad H. Elahinia ◽  
Hashem Ashrafiuon ◽  
Mehdi Ahmadian ◽  
Daniel J. Inman
Keyword(s):  
Kalman Filter ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Extended Kalman Filter ◽  
State Vector ◽  
Angular Position ◽  
The State ◽  
State Variables ◽  
Time Step ◽  
State Variable

This paper presents a robust nonlinear control that uses a state variable estimator for control of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA) wire. A model for SMA actuated manipulator is presented. The model includes nonlinear dynamics of the manipulator, a constitutive model of the Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. The current experimental setup allows for the measurement of only one state variable which is the angular position of the arm. Due to measurement difficulties, the other three state variables, arm angular velocity and SMA wire stress and temperature, cannot be directly measured. A model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter (EKF). This estimator predicts the state vector at each time step and corrects its prediction based on the angular position measurements. The estimator is then used in a nonlinear and robust control algorithm based on Variable Structure Control (VSC). The VSC algorithm is a control gain switching technique based on the arm angular position (and velocity) feedback and EKF estimated SMA wire stress and temperature. The state vector estimates help reduce or avoid the undesirable and inefficient overshoot problem in SMA one-way actuation control.

Position Sensorless Control Based on Augmented Extended Kalman Filter for Permanent Magnet Linear Synchronous Motor

2011 ◽  
Vol 88-89 ◽  
pp. 350-354
Author(s):  
Hua Cai Lu ◽  
Ming Jiang ◽  
Li Sheng Wei ◽  
Bing You Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sensorless Control ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Variable ◽  
Detailed Explanation ◽  
System States ◽  
Position Sensorless

In order to achieve position sensorless control for PMLSM drive system, speed and position of the motor must be estimated. A novel sensorless position and speed estimation algorithm was designed for PMLSM drive by measuring terminal voltages and currents. That was state augmented extended Kalman filter (AEKF) estimation method. The resistance of the motor was augmented to the state variable. Then, the speed, position and the resistance were estimated simultaneously through extended Kalman filter (EKF). The influence of the resistance on the state estimation results could be reduced. As well as giving a detailed explanation of the new algorithm, experimental results were presented. It shows that the AEKF is capable of estimating system states accurately and reliability, and the proposed sensorless control system has a good dynamic response.

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.

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