Experimental study on robust adaptive control with insufficient excitation of a 3-DOF spherical parallel robot for stabilization purposes

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

doi:10.1016/j.mechmachtheory.2020.104026

Cited by 11 publications

(3 citation statements)

References 43 publications

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“…Consider the system shown in Eqs. ( 42) and (43). For designing a backstepping controller, the following steps have been followed.…”

Section: Self-tuning Backstepping Controllermentioning

confidence: 99%

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Adaptive backstepping controller based on a novel framework for dynamic solution of an ankle rehabilitation spherical parallel robot

Ahmadi N,

Kamali Eigoli,

Taghvaeipour

2024

Robotica

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This research offers an adaptive model-based methodology for autonomous control of 3-RRR spherical parallel manipulator (RSPM) based on a novel modeling framework. RSPM is an overconstrained parallel mechanism that has a variety of applications in medical procedures such as ankle rehabilitation because of its precision and accuracy. However, obtaining a complete explicit dynamic model of these mechanisms for tracking purposes has been a problematic challenge due to their inherent singularities, coupling effects of the limbs, and redundant constraints imposed by the intermediate joints. This paper presents a novel algorithm to obtain the analytical kinematic solutions of RSPMs based on the closed-loop vector method, which includes constraint analysis. By incorporating constrained kinematics into the dynamic model, a comprehensive explicit dynamic solution of the non-overconstrained version 3-RCC of RSPM is developed in task space, based on screw theory and the linear homogeneous property of algebraic equations on the manipulator twist. Based on the proposed computational framework, a robust self-tuning backstepping control (STBC) strategy is applied to the robot to overcome the effect of external disturbances and time-varying uncertainties. Furthermore, an observer-based compensation (OBC) method is presented for dealing with the nonlinear hysteresis loops of the ankle during trajectory tracking purposes. The closed-loop stability of the whole system including STBC and OBC is theoretically performed by Lyapunov methods. The proposed methodologies are validated by realistic co-simulations in different scenarios. For instant, in the presence of external disturbances, the maximum tracking error norm of STBC is 37.5% less than the sliding mode approach.

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“…Consider the system shown in Eqs. ( 42) and (43). For designing a backstepping controller, the following steps have been followed.…”

Section: Self-tuning Backstepping Controllermentioning

confidence: 99%

“…In ref. [43], a general solution was proposed for the inverse and forward dynamics of parallel robots in joint space using dynamic models of legs and a moving platform. The dynamic modeling techniques in the literature are not convenient when designing model-based controllers for RSPMs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive backstepping controller based on a novel framework for dynamic solution of an ankle rehabilitation spherical parallel robot

Ahmadi N,

Kamali Eigoli,

Taghvaeipour

2024

Robotica

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“…As a type of advanced mechatronic system, robot manipulators have attracted intensive research attention, especially for the control strategies (Anjum et al, 2021; Rahmani et al, 2020; Sun et al, 2019; Zhang et al, 2021). State-of-the-art control schemes such as sliding mode control (SMC), backstepping control, and soft computing methods have been widely used in the robot control field (Chen et al, 2019; Ma et al, 2022; Rad et al, 2020; Xu et al, 2014). However, with the increasing requirements of lightweight, flexibility, high tracking accuracy, and fast convergence, the design of control schemes has brought great importance to the disposal of adaptive capability with the modeling uncertainties, time-varying parametric, and extraneous disturbances under ambient working environment.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators

Xiao

et al. 2023

Transactions of the Institute of Measurement and Control

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Robot manipulators as an indispensable part of automatic operation are becoming increasingly important in intelligent manufacturing systems, such as grinding and assembly. Although control methods of robot manipulators have been extensively studied, high-precision robots still face new challenges from uncertainties caused by changes in the environment and enhancement of interference. As a consequence, the neural network-based observer is utilized to reduce the influence of uncertainties and external disturbances. In this work, a new kind of nonlinear disturbance observer is designed which could well function with observed states. To improve the control efficiency and response characteristic, a kind of new integral sliding manifold is devised and the control input is generated via backstepping techniques. The stability is proved with Lyapunov theory, and the experimental results are given to demonstrate the effectiveness of the proposed controller.

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Trajectory Tracking Control of 3-RPS Parallel Mechanism Based on Udwadia-Kalaba Equation

Li,

Yin,

Wei

et al. 2023

Advances in Mechanism and Machine Science

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Experimental study on robust adaptive control with insufficient excitation of a 3-DOF spherical parallel robot for stabilization purposes

Cited by 11 publications

References 43 publications

Adaptive backstepping controller based on a novel framework for dynamic solution of an ankle rehabilitation spherical parallel robot

Adaptive backstepping controller based on a novel framework for dynamic solution of an ankle rehabilitation spherical parallel robot

Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators

Trajectory Tracking Control of 3-RPS Parallel Mechanism Based on Udwadia-Kalaba Equation

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