A Study on the Response of the Rehabilitation Lower Device using Sliding Mode Controller

Duc, Dao Minh; Tuy, Tran Xuan; Phuoc, Pham Dang

doi:10.48084/etasr.4312

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…According to the simulation results obtained, it's about 140 times slower. In addition, since both of these solutions use an uncertainty estimation block for compensation control, they will be applicable for adaptive control of industrial robot systems with additional noise in the input, i.e., applying the method described by (4), (15). This is also a difference from the original model (1) because interference in the actuators is unavoidable.…”

Section: Two Structure Diagrams For Robot Control Using Iterative Lea...mentioning

confidence: 99%

See 1 more Smart Citation

Improve the quality of Industrial Robot Control Using an Iterative Learning Method with Online Optimal Learning and Intelligent Online Learning Function Parameters

Ha,

Thuong,

Vinh

2024

Preprint

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It is unavoidable that the characteristics of the robot system alter inexactly or cannot be correctly determined during movement and are impacted by external disturbances. There are many adaptive control methods, such as sliding control or neural control, to improve the quality of trajectory tracking for robot motion systems. Still, those methods require a large amount of computation related to solving the problem of constrained nonlinear optimization problem. The article presents some techniques for determining an intelligent controller's online learning function parameters, including two circuits: the inner circuit is a state feedback linearized controller, and the outer course is an Interactive learning controller and does not use the robot's mathematical mode with function parameters online optimal learning and intelligent online learning closely following the joint model to stabilize the robot system. Finally, the 2-DOF planar robot simulation is performed on Matlab software R2022b. The findings indicate that outstanding tracking performance is achievable.

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Section: Two Structure Diagrams For Robot Control Using Iterative Lea...mentioning

confidence: 99%

“…where A ,A 12 are two arbitrarity chosen matrices and (15) we see that the route cost calculation of model ( 1) is contained in this vectorη . Based on the equivalent model ( 14), the proposed intelligent controller for the robot is as follows:…”

Section: Algorithm Content According To the First Control Diagrammentioning

confidence: 99%

Improve the quality of Industrial Robot Control Using an Iterative Learning Method with Online Optimal Learning and Intelligent Online Learning Function Parameters

Ha,

Thuong,

Vinh

2024

Preprint

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show abstract

“…It has a compact size, large working space, and high mechanical rigidity but floating actuators. The disadvantage of the parallel mechanism has been optimized by later studies, namely increasing bandwidth or increasing working space as in Yoon's study [8] with three pantograph mechanisms, which have the motors fixed at the base for better dynamic bandwidth due to low travel. Lee presented a lightweight haptic device using a transmission mechanism [6].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis and Design of an SMCNN of a 6-DOF Parallel Mechanisms using Translation Driver Motor

Tuan¹,

Thien²,

Ngoc³

2023

IJETAE

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—Recently, many technological improvement apply in the discovery of various designs of haptic devices. Several mechanism structures including serial, parallel, and hybrid-kinematic manipulators can be considered for making a haptics device. The most successful haptic mechanisms are parallel-type, because of low moving inertia, large force reflection, and high stiffness. This research shows the 6-DOF parallel haptics device based on the parallel mechanism using a translation driver motor mounted on each leg. Firstly, we introduce a 6-DOF parallel mechanism using a translation driver motor haptics device model. Due to the unsure parameters, we focus on solving the mathematics model with the nonlinear parameters of the 6-DOF parallel mechanism. Secondly, to fix the kinematics and dynamics nonlinear uncertainties parameters, the SMCNN controller for 6-DOF parallel mechanism application using a translation driver motor is designed. The Sliding model control base on artificial intelligence neural network is used to calculate the unsure factors. In this technique, to prove the stability of the system the Lyapunop theory is used. Finally, the authors the simulation results of two control algorithms with different uncertain components are presented and comparing them to demonstrate the effectiveness of the new control method. The control method is demonstrated by way of implementing the set of rules in artificial surroundings with realistic parameters, in which the received consequences are fairly promising. The obtained from SMCNN algorithm results are highly promising and accurate.

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“…Nonlinear controllers to address the limitations of PID controllers, such as the fuzzy controller [20,21], the neural network based controller [22], and the nonlinear ADRC controller [23], have been employed in several recent studies. In addition, the sliding mode controller and its various versions are always the leading candidates for the tasks of controlling nonlinear objects [17,[24][25][26][27]. Nonetheless, PAM is a nonlinear object, and the parameter estimation for the model of the PAM-based system is approximate and if the system disturbance is very high, it definitely affects the control quality of the aforementioned methods.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sliding Mode Controller for a PAM-based Actuator

Dao

Dinh²,

Vu³

et al. 2023

Eng. Technol. Appl. Sci. Res.

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The Pneumatic Artificial Muscle (PAM) is a promising actuator for developing the human-robot interaction system. However, modeling and controlling PAM-based actuators are a significant difficulty due to the inherent uncertainty and hysteresis of PAM. Besides, the control approach of a PAM-based system also deals with unknown disturbances that always exist in any system. This study developed a sliding mode controller that employs an adaptive law to deal with issues and improve control performance. Furthermore, the stability of the proposed controller is proven based on the Lyapunov stability criterion. Finally, through a series of tests, the effectiveness of the proposed control approach is verified.

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A Study on the Response of the Rehabilitation Lower Device using Sliding Mode Controller

Cited by 5 publications

References 17 publications

Improve the quality of Industrial Robot Control Using an Iterative Learning Method with Online Optimal Learning and Intelligent Online Learning Function Parameters

Improve the quality of Industrial Robot Control Using an Iterative Learning Method with Online Optimal Learning and Intelligent Online Learning Function Parameters

Dynamic Analysis and Design of an SMCNN of a 6-DOF Parallel Mechanisms using Translation Driver Motor

An Adaptive Sliding Mode Controller for a PAM-based Actuator

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