System Identification of Force of a Silver Coated Twisted and Coiled Polymer Muscle

2017 ◽  
Author(s):  
Mohsen Jafarzadeh ◽  
Lianjun Wu ◽  
Yonas Tadesse
Keyword(s):  
System Identification ◽  
Differential Equations ◽  
Linear Time ◽  
Electrical Power ◽  
Artificial Muscle ◽  
Variable Stiffness ◽  
Extensive Study ◽  
Fast Method ◽  
Linear Time Invariant ◽  
State Space System

The demand of using artificial muscle similar to the human muscle is significantly increased during past decades. Recently, silver-plated Twisted and Coiled Polymer (TCP) muscle was employed in many research projects. A first order differential equations (1st ODE) was used to predict the force of this muscle, assuming that the TCP muscle acts similar to a mechanical spring that has variable stiffness depending on the electrical power supplied. Thus, extensive study should be performed on different types of TCP muscles to reach a conclusion. In this paper, a black box system identification method is used to examine the behavior of TCP muscles under different input conditions. Different order differential equations are compared with experimental results. Prediction error method (PEM) is used for estimation of the force of silver-plated TCP muscle with several linear time invariant (LTI) discrete time state space system. In addition, we suggest a fast method (rule of thumb) to model a TCP muscle. Moreover, two key parameters have been introduced to compare the quality of the TCP muscle from force perspective.

Identification of Multivariable Nonlinear Dynamic System Based on PID Neural Network

2015 ◽  
Vol 719-720 ◽  
pp. 475-481
Author(s):  
Hua Shu ◽  
Huai Lin Shu
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Dynamic System ◽  
Nonlinear Dynamic ◽  
Linear Time ◽  
Nonlinear Dynamic System ◽  
Identification Methods ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Pid Neural Network

System identification is the basis for control system design. For linear time-invariant systems have a variety of identification methods, identification methods for nonlinear dynamic system is still in the exploratory stage. Nonlinear identification method based on neural network is a simple and effective general method that does not require too much priori experience about the system to be identified. Through training and learning, the network weights are corrected to achieve the purpose of system identification. The paper is about the identification of multivariable nonlinear dynamic system based on PID neural network. The structure and algorithm of PID neural network are introduced and the properties and characteristics are analyzed. The system identification is completed and the results are fast convergence.

scholarly journals Complete Controllability of Impulsive Fractional Linear Time-Invariant Systems with Delay

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xian-Feng Zhou ◽  
Song Liu ◽  
Wei Jiang
Keyword(s):  
Dynamical Systems ◽  
Differential Equations ◽  
Fractional Differential Equations ◽  
Linear Time ◽  
Complete Controllability ◽  
Linear Time Invariant ◽  
Impulsive Fractional Differential Equations ◽  
Fractional Differential ◽  
Linear Time Invariant Systems ◽  
Time Invariant

Some flaws on impulsive fractional differential equations (systems) have been found. This paper is concerned with the complete controllability of impulsive fractional linear time-invariant dynamical systems with delay. The criteria on the controllability of the system, which is sufficient and necessary, are established by constructing suitable control inputs. Two examples are provided to illustrate the obtained results.

Floquet Experimental Modal Analysis for System Identification of Linear Time-Periodic Systems

2007 ◽  
Author(s):  
Matthew S. Allen
Keyword(s):  
System Identification ◽  
Linear Time ◽  
Time Varying ◽  
Response Model ◽  
State Matrix ◽  
Linear Time Invariant ◽  
Response Data ◽  
Time Varying Systems ◽  
Time Invariant ◽  
Time Periodic

A variety of systems can be faithfully modeled as linear with coefficients that vary periodically with time or Linear Time-Periodic (LTP). Examples include anisotropic rotorbearing systems, wind turbines, satellite systems, etc… A number of powerful techniques have been presented in the past few decades, so that one might expect to model or control an LTP system with relative ease compared to time varying systems in general. However, few, if any, methods exist for experimentally characterizing LTP systems. This work seeks to produce a set of tools that can be used to characterize LTP systems completely through experiment. While such an approach is commonplace for LTI systems, all current methods for time varying systems require either that the system parameters vary slowly with time or else simply identify a few parameters of a pre-defined model to response data. A previous work presented two methods by which system identification techniques for linear time invariant (LTI) systems could be used to identify a response model for an LTP system from free response data. One of these allows the system’s model order to be determined exactly as if the system were linear time-invariant. This work presents a means whereby the response model identified in the previous work can be used to generate the full state transition matrix and the underlying time varying state matrix from an identified LTP response model and illustrates the entire system-identification process using simulated response data for a Jeffcott rotor in anisotropic bearings.

A Higher-Order Method for Dynamic Optimization of Controllable LTI Systems

2011 ◽  
Author(s):  
Damiano Zanotto ◽  
Sunil K. Agrawal ◽  
Giulio Rosati
Keyword(s):  
Differential Equations ◽  
Dynamic Optimization ◽  
Linear Time ◽  
Traditional Approach ◽  
Linear Time Invariant ◽  
System P ◽  
Lti System ◽  
Higher Order Method ◽  
Time Invariant ◽  
Controllability Index

This work describes a new procedure for dynamic optimization of controllable Linear time-invariant (LTI) systems. Unlike the traditional approach, which results in 2n first order differential equations, the method proposed here yields a set of m differential equations, whose highest order is twice the controllability index of the system p. This paper generalizes the approach presented in a previous work [1] to any controllable LTI system.

scholarly journals Laboratory Calibration of D-dot Sensor Based on System Identification Method

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3255 ◽  
Author(s):  
Ke Wang ◽  
Yantao Duan ◽  
Lihua Shi ◽  
Shi Qiu
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Electric Fields ◽  
Linear Time ◽  
Low Frequency ◽  
Calibration Method ◽  
Sensitivity Coefficient ◽  
Corner Frequency ◽  
Linear Time Invariant ◽  
The Time Domain

D-dot sensors can realize the non-contact measurement of transient electric fields, which is widely applied to electromagnetic pulse (EMP) measurements with characteristics of the wide frequency band, high linearity, and good stability. In order to achieve accurate calibration of D-dot sensors in the laboratory environment, this paper proposed a new calibration method based on system identification. Firstly, the D-dot sensor can be considered as a linear time-invariant (LTI) system under corner frequency, thus its frequency response can be characterized by the transfer function of a discrete output error (OE) model. Secondly, based on the partial linear regression of the transfer function curve, the sensitivity coefficient of the D-dot sensor is obtained. By increasing the influence weight of low-frequency components, this proposed method has better calibration performance when the waveform is distorted in the time domain, and can artificially adapt to the operating frequency range of the sensor at the same time.

scholarly journals A Unified Frequency Domain Model to Study the Effect of Demyelination on Axonal Conduction

2016 ◽  
Vol 7 ◽  
pp. BECB.S38554 ◽  
Author(s):  
Saurabh Chaubey ◽  
Shikha J. Goodwin
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Frequency Domain ◽  
Linear Time ◽  
Signal Propagation ◽  
Nerve Fibers ◽  
Domain Model ◽  
Linear Time Invariant ◽  
Cable Properties ◽  
Identification Approach

Multiple sclerosis is a disease caused by demyelination of nerve fibers. In order to determine the loss of signal with the percentage of demyelination, we need to develop models that can simulate this effect. Existing time-based models does not provide a method to determine the influences of demyelination based on simulation results. Our goal is to develop a system identification approach to generate a transfer function in the frequency domain. The idea is to create a unified modeling approach for neural action potential propagation along the length of an axon containing number of Nodes of Ranvier (N). A system identification approach has been used to identify a transfer function of the classical Hodgkin-Huxley equations for membrane voltage potential. Using this approach, we model cable properties and signal propagation along the length of the axon with N node myelination. MATLAB/ Simulink platform is used to analyze an N node-myelinated neuronal axon. The ability to transfer function in the frequency domain will help reduce effort and will give a much more realistic feel when compared to the classical time-based approach. Once a transfer function is identified, the conduction as a cascade of each linear time invariant system-based transfer function can be modeled. Using this approach, future studies can model the loss of myelin in various parts of nervous system.

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