Adaptive Fuzzy Finite-Time Command Filtered Impedance Control for Robotic Manipulators

Lin, Gaorong; Yu, Jinpeng; Liu, Jiapeng

doi:10.1109/access.2021.3069152

Cited by 13 publications

(4 citation statements)

References 40 publications

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“…On the one hand, pHRI applications can be summed up into contact distinction and classification applications between operators and robots. For this aim, various studies have been developed in the field by applying techniques such as mathematical model matching or signal threshold filtering [52]- [57]. Developing a safe pHRI application based on these techniques depends on the integrated sensors the robot brings.…”

Section: ) Operation Levelmentioning

confidence: 99%

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

et al. 2021

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The demand for collaborative robots is growing in industrial environments due to their versatility and low prices. Thus, more collaborative solutions are emerging for industrial scenarios. However, implementing scenarios where robots work autonomously while synchronizing their operations in a safe industrial environment with shop-floor workers is not easy. To fill the gap existing in the safe implementation of industrial collaborative scenarios, this manuscript presents a review based on five identified challenges that gathers the primary vital aspects to bear in mind while developing applications for them. Thus, a four-level classification is proposed, which collects the identified challenges and the previous developments in the field of human-robot interaction. The five identified challenges pretends to be the missing enabling key for implementing industrial collaborative scenarios in modern industrial plants. Lastly, a discussion and conclusion are exposed to analyze the degree of development in the field and its potential growth.

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Section: ) Operation Levelmentioning

confidence: 99%

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

et al. 2021

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“…In [11], it is used as a part of controller estimator. The examples of systems controlled using Fuzzy logic are, i) Fuzzy Logic and Internet of Things (IoT) for water and energy saving [12], ii) Designed for non-minimumphase DC-DC converters [13], iii) Permanent Magnet Synchronous Motor (PMSM) speed control [14], iv) Synchronization for two Chua systems [15] v) Autonomous underwater robot [16] vi) Impedance control for robot manipulators [17].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of a novel robust Fuzzy control algorithm, MPC and PID controllers for an uncertain two-link planar manipulator robot with external disturbances

Abadi,

Hameed,

Hosseinabadi

et al. 2024

Preprint

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This paper presents a novel robust Fuzzy controller for a planar manipulator robot. Also, the Model Predictive Control (MPC) and PID techniques are used to compare the results with the proposed method. The control system of the manipulator robots must have some special features that guarantee stability, fast tracking, practicability of the control signals, smooth control, etc. A comparative analysis can provide a clearer picture of the control system’s performance. This study presents a comparative analysis between a novel robust Fuzzy technique, MPC and PID controllers for a two-link planar manipulator robot. Simulink/MATLAB is used for simulations of the controllers. The results show that the proposed robust Fuzzy method performs better and is more robust than other methods. The Integral of the Absolute value of the Error (IAE) and Integral of the Time Absolute value of the Error (ITAE) performance indexes are employed for a better and more logical comparison. Moreover, the control methods are compared with the essential control features such as accuracy, fast-tracking, robustness, etc. And in all the features, the proposed Fuzzy method is better than others.

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“…Le liang proposed a novel method of inner/outer loop impedance control based on natural gradient actor-critic reinforcement learning to compensate the nonlinear dynamics term to improve the computational efficiency of the system (Liang et al, 2021). An adaptive fuzzy impedance control for robotic manipulators based on finite-time command filtered method to improve the security and compliance of physical human–robot interaction has been proposed in the study by Lin et al (2021a,b). The powerful nonlinear fitting function of the neural network can be used to compensate for many uncertain factors, such as the uncertainty of the robot dynamic model, the uncertainty of the impedance parameters, and the unknown working environment so as to improve the control accuracy and efficiency of the robot.…”

Section: Introductionmentioning

confidence: 99%

The model reference adaptive impedance control for underwater manipulator compliant operation

Zhang

Han

Liu

et al. 2023

Transactions of the Institute of Measurement and Control

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It is essential for the diversity of operation targets during the underwater manipulator’s task. The joint friction, the time-varying characteristics of physical parameters, and inaccurate measurement of the dynamic bring trouble of model uncertainty. For these problems, a model reference adaptive impedance controller is proposed to achieve the manipulator flexible operation. The desired impedance model is designed for the outer loop force tracking, and the model reference adaptive impedance controller is employed for the inner loop. At the same time, the adaptive law is designed based on the operation space position and the desired position of the desired impedance model output. To eliminate the time-varying characteristics of physical parameters, a new bounded-gain-forgetting adaptive law is designed to compensate the uncertain error between the manipulator model and estimate model. It is ensured that the closed-loop manipulator dynamic is consistent with the desired impedance model, correspondingly the manipulator end operation force tracking the standard force signal is realized, and the desired position error of the end position of the manipulator to the desired impedance model output asymptotically converges to zero. The simulation experiment of a two-degree-of-freedom manipulator is implemented on the Matlab/Simulink platform. The results show that the designed controller has good force–position tracking asymptotical convergence ability under the uncertain dynamic, and the controller has robustness and stability performance.

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Adaptive Fuzzy Finite-Time Command Filtered Impedance Control for Robotic Manipulators

Cited by 13 publications

References 40 publications

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

Comparative analysis of a novel robust Fuzzy control algorithm, MPC and PID controllers for an uncertain two-link planar manipulator robot with external disturbances

The model reference adaptive impedance control for underwater manipulator compliant operation

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