A Modified Computed Torque Control Approach for a Master-Slave Robot Manipulator System

Obadina, Ololade O.; Thaha, Mohamed A.; Althoefer, Kaspar; Shaheed, M. H.

doi:10.1007/978-3-319-96728-8_3

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…The experimental MSRM system presented in Figure 1 (Obadina et al, 2018) is used in this study. The MSRM system comprises the master and slave manipulators and a control system where both manipulators have four degrees-of-freedom (4-DOFs) each.…”

Section: The Msrm Systemmentioning

confidence: 99%

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

Obadina

Thaha

Althoefer

et al. 2021

Journal of Vibration and Control

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This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

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Section: The Msrm Systemmentioning

confidence: 99%

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

Obadina

Thaha

Althoefer

et al. 2021

Journal of Vibration and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…Wijesoma and Richards [2] suggested a method for robust accurate trajectory tracking of manipulators based on the computed torque method and variable-structure systems (VSS) theory. Researchers also use computed torque control for parallel manipulators [3], and master-slave robot manipulator systems [4]. Artificial intelligence methods compared to analytical methods are widely used for motion control of manipulators.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Trajectory Tracking Control of 2 DoFs SCARA Robot under External Force Acting to the Tip along the Trajectory

KARA,

YİĞİD,

ŞEN

et al. 2023

DÜMF MD

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The robot arms often follow a certain trajectory depending on the type of end effector with functions of spray-painting, arc welding, bonding or machining etc. Therefore, trajectory-tracking control is a very important issue in robot arm applications. Also, the robot must be able to follow the determined trajectory stably under the influence of external forces or machining forces it encounters in its operations. In this study, a Model Predictive Control (MPC) for trajectory tracking control of a 2 Degrees of Freedom (DoFs) Selective Compliant Assembly Robot Arm (SCARA) under an external force acting to the tip of the robot along the trajectory was performed. The effectiveness of the MPC method used has been demonstrated by simulation applications. According to simulation studies, successful results were obtained.

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“…The grey wolf optimization (GWO) technique [32] improved the optimization performance when compared to the traditional techniques of ACO and PSO that have been utilized for a few applications associated with robotic systems [33][34][35]. In this study, we proposed a new hybrid GWO and weighted whale optimization algorithm (WWOA) technique that demonstrated a better optimization performance than those of the conventional GWO and the whale optimization algorithm (WOA) [36,37].…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf and Weighted Whale Algorithm Optimized IT2 Fuzzy Sliding Mode Backstepping Control with Fractional-Order Command Filter for a Nonlinear Dynamic System

Han

2021

Applied Sciences

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In this study, a fractional-order sliding mode backstepping control method was proposed, which involved the use of a fractional-order command filter, an interval type-2 fuzzy logic system approximation method, and a grey wolf and weighted whale optimization algorithm for multi-input multi-output nonlinear dynamic systems. For designing the stabilizing controls of the backstepping control, a novel fractional-order sliding mode surface was suggested. Further, the transformed errors that occurred during the recursive design steps were easily compensated by the controllers constructed using a new fractional-order command filter. Thus, the differentiation issue of the virtual control in the conventional backstepping control design could be bypassed with a simpler controller structure. Subsequently, the unknown plant dynamics were approximated by an interval type-2 fuzzy logic system. The uncertainties, such as the approximation error and the external disturbance, were compensated by the fractional-order sliding mode control that was added in the backstepping controller. Furthermore, the controller parameters and the fuzzy logic system were optimized via a grey wolf and weighted whale optimization algorithm to obtain a faster tuning process and an improved control performance. Simulation results demonstrated that the fractional-order sliding mode backstepping control scheme provides enhanced control performance over the conventional backstepping control system. Thus, in this paper, a fractional-order sliding mode surface and fractional-order backstepping control are studied, which provide more rapid convergence and enhanced robustness. Furthermore, a hybrid grey wolf and weighted whale optimization algorithm are proposed to provide an improved learning performance than those of conventional grey wolf optimization and weighted whale optimization methods.

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A Modified Computed Torque Control Approach for a Master-Slave Robot Manipulator System

Cited by 8 publications

References 14 publications

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

Model Predictive Trajectory Tracking Control of 2 DoFs SCARA Robot under External Force Acting to the Tip along the Trajectory

Grey Wolf and Weighted Whale Algorithm Optimized IT2 Fuzzy Sliding Mode Backstepping Control with Fractional-Order Command Filter for a Nonlinear Dynamic System

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