Visual Closed-Loop Dynamic Model Identification of Parallel Robots Based on Optical CMM Sensor

Parallel robots present outstanding advantages compared with their serial counterparts; they have both a higher force-to-weight ratio and better stiffness. However, the existence of closed-chain mechanism yields difficulties in designing control system for practical applications, due to its highly coupled dynamics. This paper focuses on the dynamic model identification of the 6-DOF parallel robots for advanced model-based visual servoing control design purposes. A visual closed-loop output-error identification method based on an optical coordinate-measuring-machine (CMM) sensor for parallel robots is proposed. The main advantage, compared with the conventional identification method, is that the joint torque measurement and the exact knowledge of the built-in robot controllers are not needed. The time-consuming forward kinematics calculation, which is employed in the conventional identification method of the parallel robot, can be avoided due to the adoption of optical CMM sensor for real time pose estimation. A case study on a 6-DOF RSS parallel robot is carried out in this paper. The dynamic model of the parallel robot is derived based on the virtual work principle, and the built dynamic model is verified through Matlab/SimMechanics. By using an outer loop visual servoing controller to stabilize both the parallel robot and the simulated model, a visual closed-loop output-error identification method is proposed and the model parameters are identified by using a nonlinear optimization technique. The effectiveness of the proposed identification algorithm is validated by experimental tests.

Download Full-text

An Unmanned Helicopter Model Identification Method Based on the Immune Particle Swarm Optimization Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.3890 ◽

2013 ◽

Vol 347-350 ◽

pp. 3890-3893 ◽

Cited By ~ 1

Author(s):

Ting Ting Yang ◽

Ai Jun Li

Keyword(s):

Particle Swarm Optimization ◽

Dynamic Model ◽

Model Identification ◽

Particle Swarm ◽

Flight Test ◽

Pso Algorithm ◽

Model Parameters ◽

Unmanned Helicopter ◽

Swarm Optimization ◽

Identification Method

An unmanned helicopter dynamic model identification method based on immune particle swarm optimization (PSO) algorithm is approved in this paper. In order to improve the search efficiency of PSO and avoid the premature convergence, the PSO algorithm is combined with the immune algorithm. The unmanned helicopter model parameters are coded as particle, the error of flight test and math simulation model is objective function, and the dynamic model of unmanned helicopter is identified. The simulation result shows that the method has high identification precision and can realistically reflect the dynamic characteristics.

Download Full-text

On error analysis of a closed-loop subspace model identification method

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2021.06.132 ◽

2021 ◽

Vol 54 (9) ◽

pp. 701-706

Author(s):

Hiroshi Oku ◽

Kenji Ikeda

Keyword(s):

Error Analysis ◽

Closed Loop ◽

Model Identification ◽

Identification Method

Download Full-text

Closed Loop Identification by Optimization Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1084.636 ◽

2015 ◽

Vol 1084 ◽

pp. 636-641

Author(s):

Valeriy F. Dyadik ◽

Nikolay S. Krinitsyn ◽

Vyacheslav A. Rudnev

Keyword(s):

Closed Loop ◽

Uranium Hexafluoride ◽

Optimization Method ◽

Model Parameters ◽

Control Loop ◽

Loop Control ◽

Identification Method ◽

Closed Loop Control System ◽

Closed Loop Identification ◽

Loop Control System

The article is devoted to the adaptation of the controller parameters during its operation as a part of a control loop. The possibility to identify the parameters of the controlled plant model in the closed control loop has been proved by a computer simulation. The described active identification method is based on the response processing of the closed loop control system to standard actions. The developed algorithm has been applied to determine the model parameters of the flaming fluorination reactor used for the production of uranium hexafluoride. Designed identification method improves the quality of the product and the efficiency of the entire production.

Download Full-text

Experimental validation on the integrated design and control of a parallel robot

Robotica ◽

10.1017/s0263574705001967 ◽

2005 ◽

Vol 24 (2) ◽

pp. 173-181 ◽

Cited By ~ 9

Author(s):

Qing Li

Keyword(s):

Dynamic Model ◽

High Speed ◽

Dynamic Models ◽

Integrated Design ◽

Parallel Robot ◽

Parallel Robots ◽

Approximation Techniques ◽

Control Approach ◽

Modeling Errors ◽

And Control

Due to the demands from the robotic industry, robot structures have evolved from serial to parallel. The control of parallel robots for high performance and high speed tasks has always been a challenge to control engineers. Following traditional control engineering approaches, it is possible to design advanced algorithms for parallel robot control. These approaches, however, may encounter problems such as heavy computational load and modeling errors, to name it a few. To avoid heavy computation, simplified dynamic models can be obtained by applying approximation techniques, nevertheless, performance accuracy will suffer due to modeling errors. This paper suggests applying an integrated design and control approach, i.e., the Design For Control (DFC) approach, to handle this problem. The underlying idea of the DFC approach can be illustrated as follows: Intuitively, a simple control algorithm can control a structure with a simple dynamic model quite well. Therefore, no matter how sophisticate a desired motion task is, if the mechanical structure is designed such that it results in a simple dynamic model, then, to design a controller for this system will not be a difficult issue. As such, complicated control design can be avoided, on-line computation load can be reduced and better control performance can be achieved. Through out the discussion in the paper, a 2 DOF parallel robot is redesigned based on the DFC concept in order to obtain a simpler dynamic model based on a mass-balancing method. Then a simple PD controller can drive the robot to achieve accurate point-to-point tracking tasks. Theoretical analysis has proven that the simple PD control can guarantee a stable system. Experimental results have successfully demonstrated the effectiveness of this integrated design and control approach.

Download Full-text

A parameter identification method for robot dynamic models using a balancing mechanism

Robotica ◽

10.1017/s026357470000672x ◽

1989 ◽

Vol 7 (4) ◽

pp. 327-337 ◽

Cited By ~ 8

Author(s):

T. G. Lim ◽

H. S. Cho ◽

W. K. Chung

Keyword(s):

Dynamic Model ◽

Dynamic Models ◽

Least Square ◽

Estimation Accuracy ◽

Model Parameters ◽

Robot Dynamics ◽

Dynamic Parameters ◽

Joint Torques ◽

Motion Data ◽

Identification Method

SUMMARYAccurate modeling of robot dynamics is a prerequisite for the design of model-based control schemes and enhancement of the performance of the robot. The dynamic parameters associated with a pseudo-inertia matrix are often difficult to identify accurately because the inertia torques are small in comparison to gravity loadings, thus creating signal processing problem. The identification method presented in this paper utilizes a balancing mechanism which increases the estimation accuracy of the dynamic parameters. The balancing mechanism has the effect of amplifying the inertia-related torque signal by eliminating gravity loadings acting on the robot joints. A series of motion data were experimentally obtained through sequential test steps. By incorporating the measured information about joint torques, angular positions, velocities and accelerations the least square algorithm was used to identify the dynamic parameters. The estimated values were converted to those of the original robot model to obtain its dynamic model parameters. The identified robot dynamic model was shown to be accurate enough to predict the actual robot motions.

Download Full-text