A real-time H∞ cubature Kalman filter based on SVD and its application to a small unmanned helicopter

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.

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Real-Time Cubature Kalman Filter Parameter Estimation of Blood Pressure Response Characteristics Under Vasoactive Drugs Administration

2020 American Control Conference (ACC) ◽

10.23919/acc45564.2020.9147309 ◽

2020 ◽

Author(s):

Shahin Tasoujian ◽

Saeed Salavati ◽

Karolos Grigoriadis ◽

Matthew Franchek

Keyword(s):

Blood Pressure ◽

Parameter Estimation ◽

Kalman Filter ◽

Real Time ◽

Blood Pressure Response ◽

Pressure Response ◽

Vasoactive Drugs ◽

Filter Parameter ◽

Response Characteristics ◽

Cubature Kalman Filter

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Strong tracking cubature Kalman filter for real-time orbit determination for impulse maneuver satellite

2017 36th Chinese Control Conference (CCC) ◽

10.23919/chicc.2017.8028187 ◽

2017 ◽

Cited By ~ 2

Author(s):

Zhaoming Li ◽

Wenge Yang ◽

Dan Ding

Keyword(s):

Kalman Filter ◽

Real Time ◽

Orbit Determination ◽

Cubature Kalman Filter

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A Novel Fifth-Degree Cubature Kalman Filter for Real-Time Orbit Determination by Radar

Mathematical Problems in Engineering ◽

10.1155/2017/8526804 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Zhaoming Li ◽

Wenge Yang ◽

Dan Ding ◽

Yurong Liao

Keyword(s):

Kalman Filter ◽

Real Time ◽

Orbit Determination ◽

Nonlinear Function ◽

State Equation ◽

Bayesian Filtering ◽

Measurement Equation ◽

J2 Perturbation ◽

Cubature Kalman Filter ◽

Weighted Integral

A novel fifth-degree cubature Kalman filter is proposed to improve the accuracy of real-time orbit determination by ground-based radar. A fully symmetric cubature rule, approaching the Gaussian weighted integral of a nonlinear function in general form, is introduced, and the specific points and weights are calculated by matching the monomials of degree not greater than five with the exact values. On the basis of the above rule, a novel fifth-degree cubature Kalman filter, which can achieve a higher accuracy than UKF and CKF, is derived under the Bayesian filtering framework. Then, to describe the nonlinear system more accurately, the orbital dynamics equation with J2 perturbation is used as the state equation, and the nonlinear relationship between the radar measurement elements and orbital states is built as the measurement equation. The simulation results show that, compared with the traditional third-degree algorithm, the proposed fifth-degree algorithm has a higher accuracy of orbit determination.

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Accurate Real-Time Estimation of Power System Transients Using Constrained Symmetric Strong Tracking Square-Root Cubature Kalman Filter

IEEE Access ◽

10.1109/access.2019.2951309 ◽

2019 ◽

Vol 7 ◽

pp. 165692-165709 ◽

Cited By ~ 1

Author(s):

Meghabriti Pramanik ◽

Aurobinda Routray ◽

Pabitra Mitra

Keyword(s):

Kalman Filter ◽

Power System ◽

Real Time ◽

Time Estimation ◽

Square Root ◽

Cubature Kalman Filter ◽

Power System Transients ◽

Real Time Estimation

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Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

Applied Sciences ◽

10.3390/app112210772 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10772

Author(s):

Wan Wenkang ◽

Feng Jingan ◽

Song Bao ◽

Li Xinxin

Keyword(s):

Kalman Filter ◽

Real Time ◽

Cooperative Control ◽

Operating Conditions ◽

Interacting Multiple Model ◽

Square Root ◽

Multiple Model ◽

Cubature Kalman Filter ◽

Vehicle State Estimation ◽

Three Degree Of Freedom

The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion state accurately and in real-time, while reducing the effect of uncertainty in noise statistical information, the vehicle state observer is designed based on interacting multiple model theory with square root cubature Kalman filter (IMM-SCKF). The IMM-SCKF algorithm sub-model considers different state noise and measurement noise, and the introduction of the square root filter reduces the complexity of the algorithm while ensuring accuracy and real-time performance. To estimate the vehicle longitudinal, lateral, and yaw motion states, the algorithm uses a three degree of freedom (3-DOF) vehicle dynamics model and a nonlinear brush tire model, which is then validated in a Carsim-Simulink co-simulation platform for multiple operating conditions. The results show that the IMM-SCKF algorithm’s fusion output results can effectively follow the sub-model with smaller output errors, and that the IMM-SCKF algorithm’s results are superior to the traditional SCKF algorithm’s results.

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