scholarly journals Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 108331-108342
Author(s):  
Rui Yang ◽  
Li-Yi Li ◽  
Ming-Yi Wang ◽  
Cheng-Ming Zhang ◽  
Yi-Ming Zeng-Gu
Keyword(s):  
Kalman Filter ◽  
Extended State ◽  
Tuning Method ◽  
Force Ripple

Extended state Kalman filter-based path following control of underactuated autonomous vessels

2021 ◽  
pp. 014233122199441
Author(s):  
Yi Zhang ◽  
Wenchao Xue ◽  
Li Sun ◽  
Jiong Shen
Keyword(s):  
Kalman Filter ◽  
Path Following ◽  
State Observer ◽  
Extended State Observer ◽  
Extended State ◽  
Model Predictive Controller ◽  
Point Tracking ◽  
Path Following Control ◽  
Comparison Results ◽  
Predictive Controller

Path following control of underactuated autonomous vessels remains a challenging issue in recent years due to its inherent underactuation and nonlinearities as well as the widely existing disturbances in the marine environment. In order to accommodate all the difficulties simultaneously, a novel extended state Kalman filter, which adopts the idea of extended state observer in estimating and compensating system lumped disturbance and optimizes the filter gain in a real-time fashion using Kalman filter technique, is constructed to estimate system states and disturbances in the presence of model uncertainties and measurement noise. Based on the estimated states and disturbances, an enhanced model predictive controller is proposed to steer the underactuated autonomous vessels along a predefined path at a desired speed after considering system state and input constraints. Simulation results have proved the superiority of extended state Kalman filter over traditional extended state observer and extended Kalman filter under various disturbance and noise scenarios. Moreover, the comparison results with conventional proportion-integration-differentiation controller have demonstrated the feasibility and efficacy of the proposed extended state Kalman filter-based model predictive controller in both set-point tracking and disturbance rejection.

Force ripple compensation of the directly-driven linear motors via iterative tuning feed-forward controller

2019 ◽  
Vol 233 (9) ◽  
pp. 1239-1247
Author(s):  
Jianhua Wu ◽  
Chao Liu ◽  
YongJiang Liu ◽  
Zhenhua Xiong ◽  
Han Ding
Keyword(s):  
High Performance ◽  
Tracking Performance ◽  
Harmonic Model ◽  
Tracking Errors ◽  
Flexible Coupling ◽  
Feed Forward ◽  
Tuning Method ◽  
Linear Motors ◽  
Force Ripple ◽  
Feed Forward Controller

Linear motors are promising in improving the manufacturing equipments’ performance because of eliminating the flexible coupling component. However, the force ripple produced by the linear motors directly causes the feed fluctuation and thus degrades the motion precision. This article utilizes a feed-forward controller added to the feedback one to compensate its effect for the sake of simplicity and robustness. Considering that the force ripple is periodic to the position, a position-dependent multi-order harmonic model is built and used as the feed-forward controller. In order to obtain the controller parameters for various applications, an iterative tuning method is proposed. This method has the advantage that both the high performance for different trajectories tracking and the robustness to disturbances are guaranteed. Experiments on a linear motor illustrate that the parameters converge rapidly. The results show that the tracking performance is improved greatly and the tracking errors are reduced by 60% at least.

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