scholarly journals Adaptive Notch Filter for Piezo-Actuated Nanopositioning System via Position and Online Estimate Dual-Mode

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1525
Author(s):  
Chengsi Huang ◽  
Hongcheng Li
Keyword(s):  
Resonant Frequency ◽  
High Speed ◽  
Closed Loop ◽  
Notch Filter ◽  
Recursive Least Squares ◽  
Dual Mode ◽  
Fast Tracking ◽  
Adaptive Notch Filter ◽  
Loop Bandwidth ◽  
Mass Load

Due to the excellent advantages of high speed, high precision, and driving force, piezoelectric actuators nanopositioning systems have been widely used in various micro/nanomachining fields. However, the inherent resonance dynamic of the nanopositioning system generated by the flexure-hinge greatly deteriorates the positioning performance and limits the closed-loop bandwidth. Even worse, the notch filter for eliminating the effect of resonance does not work due to the varying resonant frequency resulting from the external disturbance or mass load. To this end, an adaptive notch filter for piezo-actuated nanopositioning system via position and online estimate dual-mode (POEDM) has been proposed in this paper, which can estimate the varying resonant frequency in real-time and suppress the resonance to improve the closed-loop bandwidth. First, a novel variable forgetting factor recursive least squares (VFF-RLS) algorithm for estimating resonant frequency online is presented, which is robust to the noise and provides the performances of both fast tracking and stability. Then, a POEDM method is proposed to achieve the online identification of the resonant frequency in the presence of noise and disturbance. Finally, a series of validation simulations are carried out, and the results indicate that, the frequency of input signal and the bandwidth have been achieved up to 12.5% and 87.5% of the first resonant frequency, respectively.

Yaw Dynamic Modeling for Improved High Speed Control of a Farm Tractor

2000 ◽  
Author(s):  
David M. Bevly ◽  
J. Christian Gerdes ◽  
Bradford W. Parkinson
Keyword(s):  
System Identification ◽  
High Speed ◽  
Closed Loop ◽  
Dynamic Models ◽  
Tracking Error ◽  
Analytical Models ◽  
Control Effort ◽  
Farm Tractor ◽  
Loop Bandwidth ◽  
Lqr Controller

Abstract This paper presents the system identification of a farm tractor in order to improve automatic control at higher speeds and understand controller limitations from neglecting the yaw dynamics. Yaw dynamic models are developed for multiple speeds to show the effect of velocity on the model. The identified modeled yaw dynamics do not resemble any traditional analytical models. Additionally, the effect of velocity on the closed loop bandwidth of the controller for a given set of LQR controller weights is presented. Results are given that validate that as the closed loop bandwidth of LQR control weights approaches the regime of the unmodelled yaw dynamics, the controller can go unstable. Finally results show an improvement of the lateral tracking error (with a decrease in control effort) using the system identification model.

scholarly journals Software Sensor to Enhance Online Parametric Identification for Nonlinear Closed-Loop Systems for Robotic Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3653
Author(s):  
Lilia Sidhom ◽  
Ines Chihi ◽  
Ernest Nlandu Kamavuako
Keyword(s):  
Degrees Of Freedom ◽  
Closed Loop ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Parametric Identification ◽  
Recursive Least Squares ◽  
Unknown Parameters ◽  
Closed Loop Identification ◽  
Input Variables ◽  
Robotic Applications

This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differentiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algorithm’s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.

Sign in / Sign up

Export Citation Format

Share Document