scholarly journals Mechatronic system parameter idenfitcation [i.e. identification] via genetic algorithms

2021 ◽  
Author(s):  
Mathew I. Adamson
Keyword(s):  
Tracking Error ◽  
Experimental System ◽  
Torque Sensor ◽  
Mechatronic System ◽  
Single Degree Of Freedom ◽  
Joint Friction ◽  
Optimum Configuration ◽  
Identification Technique ◽  
Friction Parameters ◽  
Sensor Gain

This thesis develops a novel way to identify both the joint friction parameters and a built in torque sensor gain and offset. The identification method is based on a genetic algorithm (GA). A model based friction compensation method and a real coded GA are selected from a variety of methods available. A model of a single degree of freedom mechatronic joint with a link is presented. Numerical simulations are run to determine the optimum configuration of the GA with respect to the population size and maximum number of generations necessary to identify the parameters to within 5% of their actual value. The GA identification technique is then used on an experimental mechatronic joint with a harmonic drive and built-in torque sensor. The friction parameters as well as the sensor gain and offset are identified in the experimental system and the position tracking error is reduced. Based on the experimental results, the method is found to be an effective way of identifying system parameters in a mechatronic joint.

scholarly journals Mechatronic system parameter idenfitcation [i.e. identification] via genetic algorithms

2021 ◽  
Author(s):  
Mathew I. Adamson
Keyword(s):  
Tracking Error ◽  
Experimental System ◽  
Torque Sensor ◽  
Mechatronic System ◽  
Single Degree Of Freedom ◽  
Joint Friction ◽  
Optimum Configuration ◽  
Identification Technique ◽  
Friction Parameters ◽  
Sensor Gain

This thesis develops a novel way to identify both the joint friction parameters and a built in torque sensor gain and offset. The identification method is based on a genetic algorithm (GA). A model based friction compensation method and a real coded GA are selected from a variety of methods available. A model of a single degree of freedom mechatronic joint with a link is presented. Numerical simulations are run to determine the optimum configuration of the GA with respect to the population size and maximum number of generations necessary to identify the parameters to within 5% of their actual value. The GA identification technique is then used on an experimental mechatronic joint with a harmonic drive and built-in torque sensor. The friction parameters as well as the sensor gain and offset are identified in the experimental system and the position tracking error is reduced. Based on the experimental results, the method is found to be an effective way of identifying system parameters in a mechatronic joint.

Nonlinear Model for Sub- and Superharmonic Motions of a MDOF Moored Structure, Part 1—System Identification

2005 ◽  
Vol 127 (4) ◽  
pp. 283-290 ◽  
Author(s):  
S. Raman ◽  
S. C. S. Yim ◽  
P. A. Palo
Keyword(s):  
System Identification ◽  
Nonlinear System Identification ◽  
Experimental System ◽  
Degree Of Freedom ◽  
System Model ◽  
Single Degree Of Freedom ◽  
Sdof System ◽  
Single Output ◽  
Motion Responses ◽  
Identification Technique

In this first part of a two-part study, the general nonlinear system identification methodology developed earlier by the authors for a single-degree-of-freedom (SDOF) system using the reverse-multi-input/single-output (R-MI/SO) technique is extended to a multi-degree-of-freedom (MDOF), sub-merged, moored structure with surge and heave motions. The physical nonlinear MDOF system model and the formulation of the R-MI/SO system-identification technique are presented. The corresponding numerical algorithm is then developed and applied to the experimental data of the MDOF system using only the subharmonic motion responses to identify the system parameters. The resulting model is then employed in Part 2 for a detailed analysis of both the sub and superharmonic dynamic behavior of the MDOF experimental system and a comparison of the MDOF response results and observations with those of the corresponding SDOF system examined earlier by the authors.

Multiparameter Real-World System Identification using Iterative Residual Tuning

2021 ◽  
pp. 1-12
Author(s):  
Adam Allevato ◽  
Mitch W Pryor ◽  
Andrea L. Thomaz
Keyword(s):  
System Identification ◽  
Robot Control ◽  
Tracking Error ◽  
Nonlinear System Identification ◽  
Visual Observation ◽  
Free System ◽  
Proposed Model ◽  
Derivative Free ◽  
Identification Technique ◽  
Using Data

Abstract In this work we consider the problem of nonlinear system identification, using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system or mechanism and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently-developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a 5-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation—where we outperform two baselines—and on a real system, where we achieve marble tracking error of 4% after just 5 optimization iterations.

Multiparameter Real-World System Identification Using Iterative Residual Tuning

2020 ◽  
Author(s):  
Adam Allevato ◽  
Mitch Pryor ◽  
Andrea L. Thomaz
Keyword(s):  
System Identification ◽  
Robot Control ◽  
Tracking Error ◽  
Nonlinear System Identification ◽  
Visual Observation ◽  
Free System ◽  
Proposed Model ◽  
Derivative Free ◽  
Identification Technique ◽  
Using Data

Abstract In this work we consider the problem of nonlinear system identification, using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently-developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a 5-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation — where we outperform two baselines — and on a real system, where we achieve marble tracking error of 4.02% after just 5 optimization iterations.

Globally exponential continuous controller/observer for position tracking in robot manipulators with hysteretic joint friction

Robotica ◽  
2009 ◽  
Vol 28 (5) ◽  
pp. 759-763 ◽  
Author(s):  
Srinivasulu Malagari ◽  
Brian J. Driessen
Keyword(s):  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Degree Of Freedom ◽  
Position Tracking ◽  
Observer Error ◽  
Joint Friction

SUMMARYIn this work, we present a continuous observer and continuous controller for a multiple degree of freedom robot manipulator with hysteretic joint friction. The fictitious hysteresis state is of course unknown to the controller and must be estimated. The joint velocities are assumed measured here. For this considered plant, we propose and present a continuous observer/controller that estimates or observes the hysteresis state and drives the position tracking error to zero. We prove that the combined tracking error and observer error converges to zero globally exponentially.

Research on Modal Identification of Structures Using Robust Second-Order Blind Identification Technique

2013 ◽  
Vol 389 ◽  
pp. 712-720
Author(s):  
Jian Hua Du ◽  
Hong Wu Huang ◽  
Dian Dian Lan
Keyword(s):  
Second Order ◽  
Modal Identification ◽  
Mode Shapes ◽  
Blind Identification ◽  
Separation Algorithm ◽  
Single Degree Of Freedom ◽  
Second Order Statistics ◽  
Single Degree ◽  
Identification Technique ◽  
Simulation Results

The paper discusses the basic principle of blind source separation algorithm applying in structural modal identification. By improving the signal-whitening method, a robust second-order blind identification (RSOBI) algorithm is established on the basis of second-order statistics. The modal responses and mode shapes can be obtained using the RSOBI algorithm from the observed data of structures in time domain. Frequency and damping are estimated from the modal responses by traditional single degree of freedom methods. The simulation results show that the RSOBI algorithm has good performance in modal identification of structures.

scholarly journals Multislope PI Modeling and Feedforward Compensation for Piezoelectric Beam

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ming Xu ◽  
Jia-qi Zhang ◽  
Cheng Rong ◽  
Jing Ni
Keyword(s):  
Minimum Mean Square Error ◽  
Tracking Error ◽  
Experimental System ◽  
High Accuracy ◽  
Positioning System ◽  
Hysteresis Model ◽  
Compensation Control ◽  
Piezoelectric Beam ◽  
Hysteresis Nonlinearity ◽  
The Mean

The hysteresis nonlinearity greatly reduces the tracking precision of piezoceramic actuators for expected displacement in a high-accuracy positioning system. In order to effectively compensate the hysteresis for piezoelectric ceramics, a novel modeling method, namely, multislope PI (Prandtl–Ishlinskii) was proposed. In view of the minimum mean square error (MSE) criterion, the weights of an improved PI model were identified by the quadratic programming optimization algorithm. For verifying the accuracy of the proposed multislope PI hysteresis model, a feedforward compensation control for piezoceramic beam was achieved. The corresponding experimental system was established, and the displacement tracking experiments were carried out. The results indicated that the mean tracking error was 0.2828 μm and within 1% of full scale, as well as the MSE was 0.3100 μm. Compared with the conventional PI model, the proposed multislope PI model demonstrated a significant improvement in positioning performance for the piezoceramic beam.

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