Servo System Identification Based on Curve Fitting to Phase-Plane Trajectory

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
Xiaoli Shi ◽  
Yong Han ◽  
Jianhua Wu ◽  
Zhenhua Xiong
Keyword(s):  
System Identification ◽  
Curve Fitting ◽  
Phase Plane ◽  
Least Squares Method ◽  
Approximation Error ◽  
Servo System ◽  
Identification Problem ◽  
Identification Accuracy ◽  
System Parameters ◽  
Plane Trajectory

Abstract Many methods have been proposed to identify servo system parameters. However, problems still remain in widely applied offline identification methods, for example, the describing-function-based relay feedback method has the ineradicable approximation error, and acceleration information is indispensable for the least-squares method. In order to identify systems accurately and efficiently with less servo system information, this article proposes a novel method to identify servo system parameters through curve fitting to the phase-plane trajectory under the help of one optimization method. Specifically, the phase-plane trajectory expression of the single-degree-of-freedom system is derived; the process on how to convert the servo system identification problem to a curve-fitting optimization problem is described in detail; and the guidelines of the initial parameter setting are introduced. Simulations and experiments are carried out to verify the efficiency of the proposed method. Finally, a feed-forward control based on the identified parameters is designed to further validate the identification accuracy.

CIRCULAR INSTANTANEOUS FREQUENCY

2010 ◽  
Vol 02 (01) ◽  
pp. 39-64 ◽  
Author(s):  
P. FRANK PAI
Keyword(s):  
System Identification ◽  
Finite Difference ◽  
Curve Fitting ◽  
Instantaneous Frequency ◽  
Phase Plane ◽  
Time Signal ◽  
Curvature Radius ◽  
Circle Fitting ◽  
Intrinsic Mode Functions ◽  
Frequency Tracking

This work defines the unique instantaneous frequency (IF) of an arbitrary time signal to be the circular instantaneous frequency (cIF) of the curvature radius of the signal's trajectory on the phase plane, where the signal's conjugate part is obtained from Hilbert transform (HT). Because a general signal of a dynamical system of multiple degrees of freedom contains multiple modal vibrations, its cIF varies dramatically and is not useful for system identification and other applications. If the signal is decomposed into modal vibration components without moving average, each component has no local extrema within each fundamental period and no local loops on the phase plane, each component's referred instantaneous frequency (rIF) with respect to the origin on the phase plane may be non-circular but is always non-negative, and the time-varying rIF and referred instantaneous amplitude (rIA) are convenient for combining the use of perturbation analysis for system identification. The empirical mode decomposition (EMD) of Hilbert–Huang transform (HHT) is valuable for decomposing a general nonlinear nonstationary signal into zero-mean intrinsic mode functions (IMFs), and HT enables accurate calculation of rIF and rIA of each IMF. Although the concept of circular frequency cannot be used for signal decomposition, it enables the development of time-domain-only techniques for online frequency tracking. A 5-point frequency tracking method is developed to eliminate the incapability of the original 4-point Teager–Kaiser algorithm (TKA) for frequency tracking of signals with moving averages. Moreover, a 3-point conjugate-pair decomposition (CPD) method is derived based on circle-fitting using a pair of conjugate harmonic functions. It is shown that both CPD and TKA are based on the concept of circle fitting, but TKA uses finite difference and CPD uses curve fitting in numerical implementation. However, the accuracy of TKA is easily destroyed by noise because of the use of finite difference. On the other hand, because CPD is based on curve fitting, noise filtering is an implicit capability and its accuracy increases with the number of processed data points. The rIF from HHT and the cIF from CPD and TKA are different by definition. Moreover, because the instantaneous frequency and amplitude are assumed to be constant in CPD and TKA, the cIF from CPD and TKA also deviates from the exact cIF.

System identification and robust position control for electro-hydraulic servo system using hybrid model predictive control

2017 ◽  
Vol 24 (18) ◽  
pp. 4145-4159 ◽  
Author(s):  
Hai-Bo Yuan ◽  
Hong-Cheol Na ◽  
Young-Bae Kim
Keyword(s):  
System Identification ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Position Control ◽  
Servo System ◽  
Model Parameters ◽  
Integral Control ◽  
Hybrid Controller ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

This paper examined system identification using grey-box model estimation and position-tracking control for an electro-hydraulic servo system (EHSS) using hybrid controller composed of proportional-integral control (PIC) and model predictive control (MPC). The nonlinear EHSS model is represented by differential equations. We identify model parameters and verify their accuracy against experimental data in MATLAB to evaluate the validity of this mathematical model. To guarantee improved performance of EHSS and precision of cylinder position, we propose a hybrid controller composed of PIC and MPC. The controller is designed using the Control Design and Simulation module in the Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW). A LabVIEW-based experimental rig is developed to apply the proposed controller in real time. Then, the validity and performance superiority of the hybrid controller were confirmed by comparing them with the MPC and PIC results. Results of real-life experiments show improved robustness and dynamic and static properties of EHSS.

A Modified Multi-innovation Algorithm to Turntable Servo System Identification

2020 ◽  
Vol 39 (9) ◽  
pp. 4339-4353
Author(s):  
Linwei Li ◽  
Huanlong Zhang ◽  
Xuemei Ren
Keyword(s):  
System Identification ◽  
Servo System

Selfish herd optimization algorithm based on chaotic strategy for adaptive IIR system identification problem

2019 ◽  
Vol 24 (10) ◽  
pp. 7637-7684
Author(s):  
Ruxin Zhao ◽  
Yongli Wang ◽  
Chang Liu ◽  
Peng Hu ◽  
Hamed Jelodar ◽  
...  
Keyword(s):  
System Identification ◽  
Optimization Algorithm ◽  
Identification Problem ◽  
Selfish Herd

Servo System Identification Using Relay Feedback: A Time-Domain Approach

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
Jia Liu ◽  
Jianhua Wu ◽  
Zhenhua Xiong ◽  
Xiangyang Zhu
Keyword(s):  
System Identification ◽  
Dynamic Characteristics ◽  
Time Domain ◽  
Oscillation Amplitude ◽  
Servo System ◽  
Time Domain Method ◽  
Relay Feedback ◽  
Servo Systems ◽  
Domain Method ◽  
The Time Domain

In servo systems, the dynamic characteristics may not only differ between axes but may also vary with moving directions for a single axis. The direction dependent characteristics would result in additional tracking or positioning error and degrade the performance of the system. In this paper, relay feedback tests are successfully applied to identify the dynamic characteristics in servo systems. A time-domain method is used to analyze the relay feedback other than the conventional describing function (DF) method. The time-domain method utilizes the same oscillation parameters (oscillation amplitude and half period) as the DF method for system identification. However, the time-domain method takes several advantages: First, the direction dependent characteristics of the system can be properly revealed; second, no approximation is made in this method, so that the exact expressions of the amplitudes and the periods of the limit cycles under relay feedback can be derived. A feedforward compensator is then designed using the estimated values of the system parameters. Simulation results show that the identification results through the time-domain method are more accurate than the DF method and are more robust under different relay parameters. Real time experiments show that the feedforward compensator designed by the proposed method compensates disturbances related to the direction and hence improves the tracking and positioning performance of the servo system.

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