Control of a two-DOF parallel robot with unknown parameters using a novel robust adaptive approach

Ansari-Rad, Saeed; Tamizi, Mehran Ghafarian; Mirfakhar, Amin; Masouleh, Mehdi Tale; Kalhor, Ahmad

doi:10.1016/j.isatra.2021.02.001

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…Rad proposed a new robust adaptive overdrive control scheme for industrial multipurpose parallel robots with unknown control parameters. The system can be identified and controlled simultaneously in the presence of external disturbances, and the system showed proper stability when the external excitation is applied to the base (Ansari Rad et al, 2021).…”

Section: Overview Of Control Theory and Methodsmentioning

confidence: 99%

A review for control theory and condition monitoring on construction robots

Shi

Bai

et al. 2023

Journal of Field Robotics

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The application of robotic technologies in building construction leads to great convenience and productivity improvement, and construction robots (CRs) bring enormous opportunities for the way we conduct design and construction. To get a better understanding of the trends and track the application of CRs for on‐site conditions, this paper conducts a systematic review of control models and status monitoring of CRs, which are two key aspects that determine construction accuracy and efficiency. Control accuracy and flexibility are primary needs for CRs applied in different scenes, so the control methods based on driving models are vitally important. Status monitoring on CRs contains knowledge in fault detection, intelligence maintenance, and fault‐tolerant control, and multiple objectives need to be met and optimized in the whole drive chain. Moreover, the state‐of‐the‐art is comprehensively summarized, and new insights are also provided to carry on promising researches.

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Section: Overview Of Control Theory and Methodsmentioning

confidence: 99%

A review for control theory and condition monitoring on construction robots

Shi

Bai

et al. 2023

Journal of Field Robotics

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“…The parameters 𝐹 𝐶 , 𝐹 𝑆 , 𝜃 ̇𝑆 and 𝜎 2 are found through the comparison between two Stribeck Curves, one created by the experimental data and the other by the numerical simulation using ( 8) to (10). The four values are found using the vector 𝑋𝑠 in (13), the error 𝑒 𝑠 (𝑋 𝑆 , 𝜃 ̇𝑚) in ( 14) and the cost function 𝐽 𝑠 to be minimized in (15).…”

Section: Static Parametersmentioning

confidence: 99%

“…The work [14] presents a controller based on a Fuzzy-PID control and self-tuning SMC, including in the investigation a Time Delay Estimation (TDE-Time Delay Estimation). The works of [15] and [16] consider the use of adaptive control for robotic manipulators. Numerical simulations presented in both works demonstrate that the controllers performed well, with the advantages of presenting excellent tracking with high accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Model of a Robotic Manipulator with One Degree of Freedom with Friction Component

Luz

Balthazar

Ribeiro

et al. 2023

IJRCS

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This research aims to develop a dynamic model of a robotic manipulator with one degree of freedom by incorporating the LuGre friction model. The study combines a mathematical model with experimental data analysis, using the Stribeck curve and Non-linear Least Square method for Parameter Identification. The purpose of the study is to improve the accuracy of the model and enhance the performance of robotic manipulators. The LuGre model is chosen for its ability to capture the nonlinear behavior of friction, which is a significant source of error in robot control systems. The effectiveness of the proposed representation is evaluated by comparing the simulation results of the dynamic model with experimental data obtained from a prototype. The results indicate that the model accurately captures the nonlinear behavior of friction, and the proposed approach can be used to develop more accurate models for control purposes.

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“…Step 1: Assume y d is the desired angle and has a second derivative. Define the error as e 1 = y − y d (4) where e 1 = e 11 e 12 e 13 T . β is the estimate of x 2 .…”

Section: Design Of Bs-rbfnn Signal Controllermentioning

confidence: 99%

“…This method can reduce the chattering of the joint, but can not eliminate it completely. For some parameters that change real-time, in order to make the robot system have better tracking effect, an adaptive control method was proposed [4]. With the development of science and technology, some intelligent control algorithms were applied to the control system, such as neural network [5], fuzzy [6], etc.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Control of Backstepping Neural Network and Port-Controlled Hamiltonian for Robot System

Shang

et al. 2021

Proceedings of 2021 Chinese Intelligent Automation Conference

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It is difficult for a single control method to track the position quickly and accurately at the end of the robot. To solve this problem, a cooperative control strategy of backstepping adaptive neural network (BS-RBFNN) signal control and port controlled Hamiltonian (PCH) energy control is proposed. BS-RBFNN solves the problem of rapidity when the system is in dynamic state, PCH control solves the problem of accuracy when the system is in steady state. The cooperative function based on the error is designed, and the cooperative control of the robot joint system is realized by using this function. The robot joint position servo system can not only realize the rapid adjustment of the dynamic position, but also realize the high precision tracking control in the steady state. Simulation results show that the system achieves fast dynamic response and accurate steady-state position tracking by using cooperative control method.

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Control of a two-DOF parallel robot with unknown parameters using a novel robust adaptive approach

Cited by 11 publications

References 33 publications

A review for control theory and condition monitoring on construction robots

A review for control theory and condition monitoring on construction robots

Dynamic Model of a Robotic Manipulator with One Degree of Freedom with Friction Component

Cooperative Control of Backstepping Neural Network and Port-Controlled Hamiltonian for Robot System

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