A Dynamic Parameter Identification Method for Flexible Joints Based on Adaptive Control

Jin, Hongzhe; Liu, Zhangxing; Zhang, Hui; Liu, Yubin; Zhao, Jie

doi:10.1109/tmech.2018.2873232

Cited by 39 publications

(26 citation statements)

References 21 publications

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“…In this section, the performance of the proposed controller is verified for the SFJM system. The parameters of the link robot are given as follows [5] From the definition of the SFJM Figs. 2-3, it can be observed that the states response faster than the method in [24].…”

Section: Results Analysismentioning

confidence: 99%

“…The simulation results are shown as follows Figs. 6-9 show the tracking performances of the motor angle, link angle, motor angular velocity and link angular velocity with the proposed method and the methods in [5] and [18]. As depicted in the Figs.6-9, both the three methods can track the desired trajectories, however, on the one hand, the integration term of tracking error is included in this study, the response is faster than the methods in [5] and [18]; on the other hand, the estimated information is used to design the controller, the robustness of the method is also better.…”

Section: Results Analysismentioning

confidence: 99%

“…An industrial flexible joint was investigated in [4], where the full state was used to maintain the tracking ability. In [5], the parameter identification technique was adopted for flexible joint system without the angular acceleration information; an adaptive control method was presented. In [6], a full state feedback neural network (NN) control was proposed for a class of robotics with flexible joints, the robustness was enhanced and the stability was guaranteed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sliding Mode Fault Tolerant Tracking Control for a Single-Link Flexible Joint Manipulator System

Chen

Guo

2019

IEEE Access

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In this paper, the fault-tolerant tracking control problem for a class of single-link flexible joint manipulator (SFJM) system with uncertainty, fault, nonlinear function, and unmatched disturbance is investigated. An observer-based sliding mode control approach is designed. Concretely, first of all, the SFJM dynamic system with uncertainty, fault, and unmatched disturbance is established. Then, by transforming the system into two subsystems, a novel composite observer is proposed to estimate the fault and disturbance, respectively. Furthermore, a robust sliding mode controller, which contains a third-order sliding mode surface, a continuous control strategy, and a visual estimated fault signal, is constructed. In the control scheme, an adaptive law is also concluded to compensate for the estimation error. Finally, the proposed method is applied to the SFJM system and the simulation results illustrate the effectiveness of the proposed method.

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Section: Results Analysismentioning

confidence: 99%

Section: Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sliding Mode Fault Tolerant Tracking Control for a Single-Link Flexible Joint Manipulator System

Chen

Guo

2019

IEEE Access

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“…However, considering the mechanical wear, aging, temperature, and other nonlinear factors during the use of robots, real-time online identification has become a trend. Therefore, as mentioned in the literature [15,19,41,42,43,44,45], various optimization algorithms and intelligent control theories for improving identification effects must be combined to enable the robots to have the ability of autonomous identification and learning to achieve an efficient and accurate motion control, which have wide applications. In addition, future work will be expanded to include additional DOFs, such as the commonly used 6-DOF or redundant arms, rather than only a 2-DOF manipulator.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design

Jia

Zhang

Zang

et al. 2019

Sensors

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As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F1 and F3, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method.

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“…For instance, not all robot companies can provide the mass matrix and gravity vector information to users in advance. Recently, a lot of parameter identification methods for the robot dynamics have also been proposed, such as the convex programming approach [19], adaptive control algorithm [20]- [22], extended Kalman filter method [23], neural networks method [24]- [26] and so on. However, the common problem for these methods are that the identification accuracy can not be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Identification of the KUKA LBR iiwa Robot With Retrieval of Physical Parameters Using Global Optimization

Fan

Chen

et al. 2020

IEEE Access

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This paper focuses on the problem of extracting the physical dynamic parameters which are fundamental for computing the positive-definite link mass matrix. To solve this problem, a minimal set of dynamic parameters were firstly identified by the standard least squares method. In order to simplify the dynamics model, a new set of essential dynamic parameters were calculated by eliminating the poorly identified parameters with an iterative approach. Based on these dynamic parameters with better identification quality, a universally global optimization framework was proposed here to retrieve the set of physical dynamic parameters of a serial robot, in which parameter bounds, linear and nonlinear constraints with physical consistency can be easily considered, such as the triangle inequality of the link inertia tensors, the total link mass limitations, other user-defined constraints and so on. Finally, validation experiments were conducted on the KUKA LBR iiwa 14 R820 robot. The results show that the proposed optimization framework is effective, and the identified dynamic parameters can predict the robot joint torques accurately for the validation trajectories. INDEX TERMS dynamic parameter identification, physical parameters, nonlinear global optimization, KUKA LBR iiwa robot.

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A Dynamic Parameter Identification Method for Flexible Joints Based on Adaptive Control

Cited by 39 publications

References 21 publications

Sliding Mode Fault Tolerant Tracking Control for a Single-Link Flexible Joint Manipulator System

Sliding Mode Fault Tolerant Tracking Control for a Single-Link Flexible Joint Manipulator System

Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design

Dynamic Identification of the KUKA LBR iiwa Robot With Retrieval of Physical Parameters Using Global Optimization

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