Nonlinear Robust Control of Unknown Robot Manipulator Systems With Actuators and Disturbances Using System Identification and Integral Sliding Mode Disturbance Observer

Chwa, Dongkyoung; Kwon, Heejun

doi:10.1109/access.2022.3163306

Cited by 18 publications

(5 citation statements)

References 43 publications

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“…The corresponding DH parameter value was converted to FK using the transformation matrix, and each joint value of the DRM for the pose of the target point was inversely calculated using DLS-IK. The FK and IK of the robot manipulator are defined based on DH parameters [29], [30]. First, FK converts the input angle 𝜃 𝑖𝑛𝑝𝑢𝑡 into the TCP pose in the Cartesian coordinate system based on a homogeneous transformation matrix 𝑇 𝑖 𝑖+1 (1)-( 5).…”

Section: Digital Transformation Of a Drm From The Real-world To The Dtmentioning

confidence: 99%

Enhancement of Control Performance for Degraded Robot Manipulators Using Digital Twin and Proximal Policy Optimization

Park,

Lee,

Kim

et al. 2024

IEEE Access

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In this study, we propose a novel method for enhancing the control performance of a degraded robot manipulator by leveraging digital twins and proximal policy optimization, a specific deep reinforcement learning algorithm. Recently, various robotic technologies with high levels of controllability, safety, and reliability that incorporate the fourth industrial technology have been developed. Nevertheless, repairs or replacements owing to the performance degradation of sophisticated robot hardware or control systems are still time-, cost-, and manpower-consuming. To address these challenges, we propose a new strategy:1) approximate the unstable dynamic characteristics of six-degree-of-freedom low-performance robot manipulators to a digital twin with parameter tuning of a physics engine, 2) improve the accuracy and stability of reaching target points under diverse conditions through deep reinforcement learning using the domain randomization method, and 3) deploy a trained policy on an actual robot manipulator with degraded capabilities to validate the control performance improvement. Our method reduced the position error of a real robot manipulator by 63.0% and 39.0% compared to the built-in control method and the proportionalintegral-derivative control method, respectively. Randomizing parameters in the physics engine of the digital twin during training allowed the method to simulate the imprecise motions of an actual degraded robot manipulator, facilitating the development of a more robust policy. Notably, our method has the potential to be applicable to all types of articulated robots, and presents a promising solution for maintaining performance while reducing long-term costs.

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Section: Digital Transformation Of a Drm From The Real-world To The Dtmentioning

confidence: 99%

Enhancement of Control Performance for Degraded Robot Manipulators Using Digital Twin and Proximal Policy Optimization

Park,

Lee,

Kim

et al. 2024

IEEE Access

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“…where a denotes the threshold start, n denotes the number of variations, and b denotes the threshold end. 4) objective function of LASSO that is shown in (5) was performed to obtain the independent variable gain coefficient Ξ. The LASSO objective function was also implemented in MATLAB lasso() .…”

Section: ) Nonlinearizing Data and Perform Regressionmentioning

confidence: 99%

“…By using the mathematical equations of the models enabled us to gain an in-depth understanding of the characteristics and dynamics of the robot manipulator, which in turn enabled the design of effective and optimized control systems [1], [2] . The dynamic modeling of a robot manipulator can be performed using three approaches: manual modeling [3], [4], computer-based modeling [5], [6], and hybrid modeling [7]. Robot manipulator dynamics modeling using manual or analytical approaches, such as the Lagrange-Euler [8] and Newton-Euler formulation [9], require a high level of knowledge of dynamics and physics laws.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Equation Discovery of 3-DoF Robotic Manipulator Dynamics Using LASSO Model Selection Criteria With Variable Segregation Algorithm

Istiqphara,

Wahyunggoro,

Cahyadi

2024

IEEE Access

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The challenge in controlling a manipulator robot is to model the system to obtain an efficient control system design. One approach that can be used to model the dynamics of a manipulator robot is data-driven modeling. However, in its implementation, data-driven modeling is highly sensitive to sensor noise, which significantly affects the accuracy of the system identification. In addition, the existing approach yields only a generalized form of the differential equation for each joint, which has not been divided into inertial, Coriolis, and gravitational variables that can be used for other purposes. In this study, a LASSO model selection criteria with a variable segregation algorithm (LMSCVS) is proposed to derive the dynamic equation of a 3-DoF manipulator robot, segregating the generalized form variables into Coriolis and centrifugal, inertia, and gravitational variables. Additionally, a Dynamic Expression Nonlinearization(DEx-N) algorithm is introduced to generate nonlinear candidates more efficiently to express the dynamics of the robot manipulator. The experimental results on the ROB3 hardware demonstrate that the proposed method successfully discovers mathematical equations, resulting in higher accuracy and sparsity compared to the previous method. The processing time of the proposed method is also significantly faster. Based on these results, the proposed method has a better performance in identifying real systems that usually have noise in the sensor data and in discovering the equation of robot manipulator dynamics for broader purposes.

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“…Zhang et al [7] proposed robot manipulators, a novel control framework combining the amended extended high gain observer (EHGO) and non-discrete proportional integral derivative sliding mode controller (PID-SMC). The Lyapunov technique has suggested improving the efficiency of EHGO [8] by a proposed study of controlling robot manipulator systems based on a robust controller with entirely unexplored dynamics. Furthermore, the authors suggested a sliding method with integration strategy disorder observer (SI-ISMDOB) based on the sliding control SMC technique.…”

Section: Introductionmentioning

confidence: 99%

An optimal motion path planning control of a robotic manipulator based on the hybrid PI-sliding mode controller

2023

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This paper proposes a hybrid proportional-integral (PI-sliding) mode controller to improve and adjust the point-to-point path planning of a three-link robotic arm with three degrees of freedom. The main objectives of the proposed control unit are to control the tracking process to reach the desired path handle the outgoing vibrations, and dampen them in the links of the robotic arm during its movement to ensure accuracy in completing the work. Seventh-degree polynomial paths represented the segments of locomotion connecting the first, middle, and last points at the combined space through predefined route points via minimal travel time. While not exceeding a predetermined maximum torque, without hitting any obstacle in the robot's workspace. The results showed that the proposed control design provides a robust control performance and fast response corresponding with conventional sliding mode controller (SMC) and PI controller. Then the outcomes provide the best results for the demanded mission according to the whished intakes with minimal error. The system equations are solved using the techniques available in MATLAB software then the results of the model are validated by the results of simulations.

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Nonlinear Robust Control of Unknown Robot Manipulator Systems With Actuators and Disturbances Using System Identification and Integral Sliding Mode Disturbance Observer

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 18 publications

References 43 publications

Enhancement of Control Performance for Degraded Robot Manipulators Using Digital Twin and Proximal Policy Optimization

Enhancement of Control Performance for Degraded Robot Manipulators Using Digital Twin and Proximal Policy Optimization

Enhanced Equation Discovery of 3-DoF Robotic Manipulator Dynamics Using LASSO Model Selection Criteria With Variable Segregation Algorithm

An optimal motion path planning control of a robotic manipulator based on the hybrid PI-sliding mode controller

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