“…We integrate our cable effect model in an effective and practical control framework (disturbance rejection sliding mode control, DRSMC 37 ) for accomplishing the robotic manipulation of heavy cables. The DRCMS method uses the ADRC methodology to improve the robustness and accuracy of a traditional SMC controller, which provides a practical and effective trajectory tracking control framework with a strong disturbance rejection ability for robots.…”
Section: Resultsmentioning
confidence: 99%
“…10 . Algorithm 2 summarizes the control framework of the algorithm in pseudocode, detailed definitions can be seen in our preliminary study presented in 24 , 37 . Figure 10 Control structure of the cable model based DRSMC framework.…”
Section: Resultsmentioning
confidence: 99%
“…Then, we use our DRSMC method to complete the same robot trajectory. Both of these controllers work at the frequency of 1 kHz, and the control parameters are tuned using the same method as described in 37 . Both the PID controller and the DRSMC controller can achieve stable positioning errors of less than 0.01 mm when no payload is applied.…”
The robotic manipulation of a heavy industrial cable is challenging to model and control because of the high number of degrees of freedom and the rigid-flexible coupling dynamics. In this paper, we report the development of modeling the cable effect and control methodology for robotic cable manipulation. Our cable effect model is based on the 2D convolutional neural network, which is a deep learning-based method uses the effective cable representation method to achieve the accurate, generalizable, and efficient estimation of the cable coupling forces and torques. Practical problems such as the measurement limits and time efficiency are considered in our method for real applications. With these approaches, we are the first to solve the problem of dynamic payload effect caused by heavy industrial cables in experimental cases. The used control methodology combines the active disturbance rejection control framework with the sliding mode control method, which can acquire promising tracking performance. We integrate our cable effect model into the control scheme, and demonstrate it satisfies the high-quality robotic manipulation of heavy cables. The performance of the proposed method is assessed with both a simulated system and real robot system. The results show that our method can estimate the cable coupling effect with over 85% accuracy and accomplish manipulation with a positioning error less than 0.01 mm. This reveals that our method is promising for robotic manipulation of heavy industrial cables and can accomplish the challenging cable insertion task.
“…We integrate our cable effect model in an effective and practical control framework (disturbance rejection sliding mode control, DRSMC 37 ) for accomplishing the robotic manipulation of heavy cables. The DRCMS method uses the ADRC methodology to improve the robustness and accuracy of a traditional SMC controller, which provides a practical and effective trajectory tracking control framework with a strong disturbance rejection ability for robots.…”
Section: Resultsmentioning
confidence: 99%
“…10 . Algorithm 2 summarizes the control framework of the algorithm in pseudocode, detailed definitions can be seen in our preliminary study presented in 24 , 37 . Figure 10 Control structure of the cable model based DRSMC framework.…”
Section: Resultsmentioning
confidence: 99%
“…Then, we use our DRSMC method to complete the same robot trajectory. Both of these controllers work at the frequency of 1 kHz, and the control parameters are tuned using the same method as described in 37 . Both the PID controller and the DRSMC controller can achieve stable positioning errors of less than 0.01 mm when no payload is applied.…”
The robotic manipulation of a heavy industrial cable is challenging to model and control because of the high number of degrees of freedom and the rigid-flexible coupling dynamics. In this paper, we report the development of modeling the cable effect and control methodology for robotic cable manipulation. Our cable effect model is based on the 2D convolutional neural network, which is a deep learning-based method uses the effective cable representation method to achieve the accurate, generalizable, and efficient estimation of the cable coupling forces and torques. Practical problems such as the measurement limits and time efficiency are considered in our method for real applications. With these approaches, we are the first to solve the problem of dynamic payload effect caused by heavy industrial cables in experimental cases. The used control methodology combines the active disturbance rejection control framework with the sliding mode control method, which can acquire promising tracking performance. We integrate our cable effect model into the control scheme, and demonstrate it satisfies the high-quality robotic manipulation of heavy cables. The performance of the proposed method is assessed with both a simulated system and real robot system. The results show that our method can estimate the cable coupling effect with over 85% accuracy and accomplish manipulation with a positioning error less than 0.01 mm. This reveals that our method is promising for robotic manipulation of heavy industrial cables and can accomplish the challenging cable insertion task.
“…Sliding mode control (SMC) has good robustness to unknown external disturbance, parameter changes, and model disturbance and is widely used in the design of singleaxis drive controller. However, traditional SMC cannot effectively deal with the rapidly changing interference, and the design relies too much on the controlled objects of the mathematical model, which may cause chattering due to modeling errors and uncertainties [12]. A neural networkbased PID controller was designed while the controller is operational in an online mode for high-performance magnet synchronous machine position control.…”
A sliding mode control for active disturbance rejection was utilized for uniaxial tracking control, and a cross-coupled contour error compensation controller based on nonlinear position domain (PD) was devised for enhanced contour machining precision and robustness of biaxial moving platform. For single-axis tracking control, the total disturbance of the system was initially considered to be the external disturbance plus internal unmodeled dynamics, and the overall disturbance was estimated and compensated in real time using a linear extended state observer. A sliding mode controller was built on this foundation to obtain satisfactory uniaxial control performance. Secondly, based on the estimated value of each axis component of contour error, a nonlinear PD contour error compensation controller was designed to coordinate the tracking motion between axes and further reduced the contour error. The proposed work is compared against state-of-the-art works from literature for various metrics such as tracking error curves, average comparison of tracking errors, and cross-coupled controller parameters. The simulation results showed that the proposed method can effectively improve the contour processing accuracy of the system.
Aiming at the problem of compliance control of industrial robot, in order to overcome the influence of unfavorable factors such as nonlinearity and strong coupling of industrial robot system on robot compliance control, so as to improve robot control accuracy and avoid damage to robot or workpiece caused by environmental contact force. Therefore, a robot admittance control method based on sliding mode control (SMC) strategy is proposed in this paper. The dynamic model of the robot with parameter uncertainty is established, and the exponential reaching law sliding mode control method is introduced into the robot admittance controller to improve the flexibility of the robot. Finally, the six-axis robot is simulated by the simulation platform to verify the feasibility and effectiveness of the proposed method.
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