Simultaneous Estimation of Contraction Ratio and Parameter of McKibben Pneumatic Artificial Muscle Model Using Log-Normalized Unscented Kalman Filter

2016 ◽  
Author(s):  
Takashi Kodama ◽  
Atushi Okabe ◽  
Kiminao Kogiso
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Artificial Muscle ◽  
Muscle Model ◽  
Simultaneous Estimation ◽  
Pneumatic Artificial Muscle ◽  
Contraction Ratio

THE UNSCENTED KALMAN FILTER, A POWERFUL TOOL FOR DATA ANALYSIS

2004 ◽  
Vol 14 (06) ◽  
pp. 2093-2105 ◽  
Author(s):  
A. SITZ ◽  
U. SCHWARZ ◽  
J. KURTHS
Keyword(s):  
Kalman Filter ◽  
Data Analysis ◽  
Dynamical Systems ◽  
Unscented Kalman Filter ◽  
Nonlinear Dynamical Systems ◽  
Simultaneous Estimation ◽  
Bayesian Filter ◽  
Nonlinear Dynamical

We present a derivation of the unscented Kalman filter (UKF) as an approximation to the optimal Bayesian filter equations. The potentials of the UKF are then demonstrated for the problem of simultaneous estimation of states and parameters from noise corrupted data of nonlinear dynamical systems. The UKF even works for the chaotic Chua system which includes nondifferentiable terms.

scholarly journals Active Model-Based Hysteresis Compensation and Tracking Control of Pneumatic Artificial Muscle

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 364
Author(s):  
Yanding Qin ◽  
Haoqi Zhang ◽  
Xiangyu Wang ◽  
Jianda Han
Keyword(s):  
Unscented Kalman Filter ◽  
Reference Model ◽  
Artificial Muscle ◽  
Pneumatic Artificial Muscle ◽  
Tracking Accuracy ◽  
External Disturbances ◽  
Hysteresis Compensation ◽  
Modeling Errors ◽  
Active Modeling ◽  
Main Factor

The hysteretic nonlinearity of pneumatic artificial muscle (PAM) is the main factor that degrades its tracking accuracy. This paper proposes an efficient hysteresis compensation method based on the active modeling control (AMC). Firstly, the Bouc–Wen model is adopted as the reference model to describe the hysteresis of the PAM. Secondly, the modeling errors are introduced into the reference model, and the unscented Kalman filter is used to estimate the state of the system and the modeling errors. Finally, a hysteresis compensation strategy is designed based on AMC. The compensation performances of the nominal controller with without AMC were experimentally tested on a PAM. The experimental results show that the proposed controller is more robust when tracking different types of trajectories. In the transient, both the overshoot and oscillation can be successfully attenuated, and fast convergence is achieved. In the steady-state, the proposed controller is more robust against external disturbances and measurement noise. The proposed controller is effective and robust in hysteresis compensation, thus improving the tracking performance of the PAM.

scholarly journals Estimation of states and parameters of multi-axle distributed electric vehicle based on dual unscented Kalman filter

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041988008
Author(s):  
Xiaofei Pei ◽  
Zhenfu Chen ◽  
Bo Yang ◽  
Duanfeng Chu
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
Dynamic Performance ◽  
Joint Estimation ◽  
Estimation Accuracy ◽  
Simultaneous Estimation ◽  
Heavy Vehicle ◽  
Important Trend

Distributed electric drive technology has become an important trend because of its ability to enhance the dynamic performance of multi-axle heavy vehicle. This article presents a joint estimation of vehicle’s state and parameters based on the dual unscented Kalman filter. First, a 12-degrees-of-freedom dynamic model of an 8 × 8 distributed electric vehicle is established. Considering the dynamic variation of some key parameters for heavy vehicle, a real-time parameter estimator is introduced, based on which simultaneous estimation of vehicle’s state and parameters is implemented under the dual unscented Kalman filter framework. Simulation results show that the dual unscented Kalman filter estimator has a high estimation accuracy for multi-axle distributed electric vehicle’s state and key parameters. Therefore, it is reliable for vehicle dynamics control without the influence of unknown or varying parameters.

scholarly journals Pneumatic Artificial Muscle Based on Novel Winding Method

Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 100
Author(s):  
Disheng Xie ◽  
Zhuo Ma ◽  
Jianbin Liu ◽  
Siyang Zuo
Keyword(s):  
Artificial Muscle ◽  
Force Model ◽  
Maximum Output ◽  
Pneumatic Artificial Muscle ◽  
Ratio Model ◽  
Wearable Robots ◽  
Contraction Ratio ◽  
Output Force

This paper proposes a pneumatic artificial muscle based on a novel winding method. By this method, the inflation of silicone tubes is transformed to the contraction of muscle, whereas the expansion keeps on one side of the muscle, i.e., the expansion of the actuator does not affect the object close to it. Hence the muscle is great for wearable robots without squeezing on the user’s skin. Through necessary simplification, the contraction ratio model and force model are proposed and verified by experiments. The prototype of this paper has a maximum contraction ratio of 35.8% and a maximum output force of 12.24 N with only 5 mm thickness. The high compatibility proves it excellent to be the alternative for wearable robots.

A novel metaheuristic model-based approach for accurate online broken bar fault diagnosis in induction motor using unscented Kalman filter and ant lion optimizer

2019 ◽  
Vol 42 (8) ◽  
pp. 1537-1546 ◽  
Author(s):  
Marouane Rayyam ◽  
Malika Zazi
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Unscented Kalman Filter ◽  
Superior Performance ◽  
Simultaneous Estimation ◽  
Estimation Errors ◽  
Model Based ◽  
Ant Lion Optimizer ◽  
Detection And Identification ◽  
Ant Lion

This paper introduces a novel metaheuristic model-based scheme for fault monitoring in squirrel cage induction motors (SCIMs). This method relies on the combination of the ant lion optimizer (ALO) and the unscented Kalman filter (UKF) to detect and quantify the number of broken bars. Contrary to the UKF-based fault diagnosis, the improved ALO-UKF algorithm tunes optimally and automatically the noise covariance matrices Q and R, which reduces the estimation errors, and then obtains an effective and accurate fault diagnosis. Firstly, a mathematical model of the fault under study has been developed based on rotor parameter value as signature. Secondly, a sixth order ALO-UKF algorithm has been synthesized for simultaneous estimation of rotor resistance and speed. Several broken bar fault conditions have been simulated. Simulation results show the effectiveness and robustness of the proposed ALO-UKF scheme in broken bar detection and identification, and exhibit a more superior performance than the simple-UKF and EKF algorithms in term of stability, accuracy and response time.

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