Dynamic model identification method of manipulators for inside DEMO engineering

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.

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A recursive propagator-based subspace method for vehicle handling dynamic system model identification

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407017746840 ◽

2017 ◽

Vol 233 (3) ◽

pp. 495-516

Author(s):

Weimiao Yang ◽

Xiqiang Guan ◽

Jianwu Zhang

Keyword(s):

Dynamic Model ◽

Linear Time ◽

Model Identification ◽

Unbiased Estimation ◽

Subspace Identification ◽

Identification Algorithm ◽

Vehicle Handling ◽

Noise Interference ◽

Identification Method ◽

Input Test

A high-precision vehicle handling dynamic model is of great importance in both the analysis of system performance and development of stability controller. This study introduces a new modeling algorithm based on subspace identification. Different from traditional batchwise subspace identification method, the new modeling method is proposed in the framework of online realization and unbiased estimation, which is ensured by a recursive propagator method (PM) combined with a vector autoregressive with exogenous (VARX) input model. Also, an observable and controllable 4-order linear time-varying (LTV) vehicle handling dynamic model, including rolling motion, is presented. To validate the identification algorithm, real vehicle standard road tests, including the step input test and impulse input test, are conducted, and sampling data are collected to apply into the identification procedure. The feasibility and accuracy of this online identification method are demonstrated. Furthermore, the stability of identification method is validated by an identification process based on road test data with measurement noise interference, and the superiority of the proposed identifiable LTV model is proved compared to the linear time-invariant (LTI) model.

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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.

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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

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Dynamic Model Identification of Collaborative Robots Using a Three-Loop Iterative Method

10.1109/icarm52023.2021.9536165 ◽

2021 ◽

Author(s):

Jie Deng ◽

Weiwei Shang ◽

Bin Zhang ◽

Shengchao Zhen ◽

Shuang Cong

Keyword(s):

Iterative Method ◽

Dynamic Model ◽

Model Identification ◽

Collaborative Robots

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T-S Fuzzy Systems Optimization Identification Based on FCM and PSO

10.21203/rs.3.rs-38345/v1 ◽

2020 ◽

Author(s):

Yaxue Ren ◽

Fucai Liu ◽

Jingfeng Lv ◽

Aiwen Meng ◽

Yintang Wen

Keyword(s):

Membership Function ◽

Gaussian Function ◽

Model Identification ◽

Fuzzy Model ◽

Pso Algorithm ◽

Identification Accuracy ◽

Fine Tuning ◽

Recursive Least Squares ◽

Identification Method ◽

Two Parameters

Abstract The division of fuzzy space is very important in the identification of premise parameters and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

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Rotorcraft control response using linearised and non-linear flight dynamic models with different inflow models

The Aeronautical Journal ◽

10.1017/aer.2017.8 ◽

2017 ◽

Vol 121 (1238) ◽

pp. 553-575 ◽

Cited By ~ 1

Author(s):

T. Sakthivel ◽

C. Venkatesan

Keyword(s):

Dynamic Model ◽

Dynamic Models ◽

Model Identification ◽

Flight Test ◽

Step Size ◽

Response Characteristics ◽

Wide Range ◽

Non Linear ◽

Steady Level ◽

Control Response

ABSTRACTThe aim of the present study is to develop a relatively simple flight dynamic model which should have the ability to analyse trim, stability and response characteristics of a rotorcraft under various manoeuvring conditions. This study further addresses the influence of numerical aspects of perturbation step size in linearised model identification and integration timestep on non-linear model response. In addition, the effects of inflow models on the non-linear response are analysed. A new updated Drees inflow model is proposed in this study and the applicability of this model in rotorcraft flight dynamics is studied. It is noted that the updated Drees inflow model predicts the control response characteristics fairly close to control response characteristics obtained using dynamic inflow for a wide range of flight conditions such as hover, forward flight and recovery from steady level turn. A comparison is shown between flight test data, the control response obtained from the simple flight dynamic model, and the response obtained using a more detailed aeroelastic and flight dynamic model.

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