A Hysteresis Compensation Control Method for Piezoelectric Actuators Based on Truncated Least Squares Support Vector Machine

2019 ◽  
Author(s):  
Zhibiao Ma ◽  
Xiangdong Liu ◽  
Xuefei Mao ◽  
Zhen Li
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Control Method ◽  
Piezoelectric Actuators ◽  
Support Vector ◽  
Hysteresis Compensation ◽  
Compensation Control

Robust Tracking Control for 3 DOF Helicopter Based on Least Squares Support Vector Machine

2013 ◽  
Vol 313-314 ◽  
pp. 370-373
Author(s):  
Jing Mei Zhang ◽  
Lei Xue ◽  
Rui Min Zhang ◽  
Chang Yin Sun
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Tracking Control ◽  
Control Method ◽  
Tracking Error ◽  
Stability And Convergence ◽  
Support Vector ◽  
Robust Tracking ◽  
Robust Tracking Control ◽  
Error Dynamic

A robust tracking control method for 3 DOF helicopter via least squares support vector machine with considering uncertainty and bounded disturbance is proposed in this paper. The inversion errors which is brought due to modeling errors and uncertainty can be compensated by least squares support vector machine, and the optimal regulator guaranteed dynamic characteristics of approximate linearization system and response quality of tracking error dynamic. Finally, the stability and convergence analysis of error dynamic system is proven by Lyapunov stability theory and numerical simulations have demonstrated the effectiveness of the proposed approach.

Hysteresis modeling of piezoelectric actuators with the frequency-dependent behavior using a hybrid model

2019 ◽  
Vol 234 (9) ◽  
pp. 1848-1858
Author(s):  
Cheng Qian ◽  
Qing Ouyang ◽  
Yulai Song ◽  
Wei Zhao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Hybrid Model ◽  
Piezoelectric Actuators ◽  
Support Vector ◽  
Rate Dependent ◽  
Nonlinear Hysteresis ◽  
Dependent Behavior ◽  
Hysteresis Modeling ◽  
Dependent Model

In order to overcome the problem of positioning inaccuracy caused by nonlinear hysteresis of piezoelectric actuators, a hybrid model based on least-squares support vector machine and Bouc–Wen model is proposed to model the rate-dependent hysteresis of piezoelectric actuators. A rate-independent Bouc–Wen model and its parameters identification method is established as the basis of least-squares Bouc–Wen model. Least-squares support vector machine, which is optimized by particle swarm optimization, is introduced to improve the Bouc–Wen model into a rate-dependent model by adjusting parameters of Bouc–Wen dynamically. Experiment is carried out to validate both Bouc–Wen model and the proposed least-squares Bouc–Wen model. The results show that the proposed least-squares Bouc–Wen method is a valid and a more precise method compared to the rate-independent Bouc–Wen model.

A New Control Method for Fermenter Temperature

2012 ◽  
Vol 236-237 ◽  
pp. 385-389
Author(s):  
Guang Hui Zeng ◽  
Yan Gan
Keyword(s):  
Support Vector Machine ◽  
Steady State ◽  
Least Squares ◽  
Predictive Control ◽  
Pid Control ◽  
Control Method ◽  
Support Vector ◽  
Control Performance ◽  
Simulation Results ◽  
Controlled Object

A new control method based on least squares support vector machine (LSSVM) and model predictive control (MPC) is proposed for the control of fermenter temperature. Existing PID control doesn’t consider the model of controlled object, so it tends to bring steady-state error. The proposed method utilizes LSSVM to obtain fermenter temperature’s model and then uses it to implement MPC. The simulation results show that our method has better control performance than traditional PID control

Q-learning System Based on Cooperative Least Squares Support Vector Machine

2009 ◽  
Vol 35 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Xue-Song WANG ◽  
Xi-Lan TIAN ◽  
Yu-Hu CHENG ◽  
Jian-Qiang YI
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Learning System ◽  
Support Vector ◽  
Q Learning

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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