Comparative study of various machine learning algorithms and Denavit–Hartenberg approach for the inverse kinematic solutions in a 3-<u>PP</u>SS parallel manipulator

Thomas, Mervin Joe; Sanjeev, Mithun M; Sudheer, A. P.; Joy, M. L.

doi:10.1108/ir-11-2019-0233

Cited by 16 publications

(11 citation statements)

References 22 publications

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“…Thomas et al 24 performed the mobility analysis for the 3-PPSS manipulator using the Grubler–Kutzbach criterion and derived its kinematic model using the Denavit–Hartenberg (DH) approach. The DH convention is applied to each leg and coupled using suitable holonomic constraints.…”

Section: Structural Description and Kinematic Model Of The 3-ppss Parallel Manipulatormentioning

confidence: 99%

“…According to the DH model for the 3-PPSS manipulator kinematics, each leg has eight joint variables collectively. 24 This is because each spherical joint is assumed as three mutually perpendicular revolute joints. This article borrows the same kinematic model to derive its dynamic model.…”

Section: Dynamic Modeling Of the 3-ppss Parallel Manipulatormentioning

confidence: 99%

“…The inverse kinematics module uses the Random Forest (RF) algorithm to determine the corresponding slider positions for a given input pose of the platform. 24 The simulation executes the RF algorithm in PYTHON by calling it as a function in Simulink. Figure 11(b) shows the inner part of the stepper motor dynamics module for one slider assembly.…”

Section: Intelligent Position Control Of the 3-ppss Parallel Manipulatormentioning

confidence: 99%

“…“ l ” is the length of the connecting link; “ b ” is the distance from the cent er to one corner of the equilateral triangle-shaped mobile platform; “ c ” is the normal distance between two consecutive sl iders measured perpendicular to its motion; “ X1 ” to “ X24 ” are the joint variables according to the DH model derived by Thomas et al 24…”

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confidence: 99%

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Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

Thomas

George

Sreedharan

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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The significant challenges seen with the mathematical modeling and control of spatial parallel manipulators are its difficulty in the kinematic formulation and the inability to real-time control. The analytical approaches for the determination of the kinematic solutions are computationally expensive. This is due to the passive joints, solvability issues with non-linear equations, and inherent kinematic constraints within the manipulator architecture. Therefore, this article concentrates on an artificial neural network–based system identification approach to resolve the complexities of mathematical formulations. Moreover, the low computation time with neural networks adds up to its advantage of real-time control. Besides, this article compares the performance of a constant gain proportional–integral–derivative (PID), variable gain proportional–integral–derivative, model predictive controller, and a cascade controller with combined variable proportional–integral–derivative and model predictive controller for real-time tracking of the end-effector. The control strategies are simulated on the Simulink model of a 6-degree-of-freedom 3-PPSS (P—prismatic; S—spherical) parallel manipulator. The simulation and real-time experiments performed on the fabricated manipulator prototype indicate that the proposed cascade controller with position and velocity compensation is an appropriate method for accurate tracking along the desired path. Also, training the network using the experimentally generated data set incorporates the mechanical joint approximations and link deformities present in the fabricated model into the predicted results. In addition, this article showcases the application of Euler–Lagrangian formalism on the 3-PPSS parallel manipulator for its dynamic model incorporating the system constraints. The Lagrangian multipliers include the influence of the constraint forces acting on the manipulator platform. For completeness, the analytical model results have been verified using ADAMS for a pre-defined end-effector trajectory.

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Section: Structural Description and Kinematic Model Of The 3-ppss Parallel Manipulatormentioning

confidence: 99%

Section: Dynamic Modeling Of the 3-ppss Parallel Manipulatormentioning

confidence: 99%

Section: Intelligent Position Control Of the 3-ppss Parallel Manipulatormentioning

confidence: 99%

mentioning

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Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

Thomas

George

Sreedharan

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The soil's capacity to hold moisture varies according to its texture. Sand has a larger pore size than clay, which results in a faster infiltration rate and a decreased capacity for water storage [2]. Additionally, runoff prediction is crucial for hydraulic structure building, as well as for water resource planning and management [3].…”

Section: Introductionmentioning

confidence: 99%

Soil Infiltration Rate Impact on Water Quality Modeled Using Random Forest Regression

Sopandi

2021

J. Appl. Data Sci.

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In this paper, Infiltration rate of the soil is investigated by using predictive models of Random forest regression and their performance were compared with Artificial neural network (ANN) and M5P model tree techniques. We utilized 132 field measurements comprising this dataset. 88 models were trained using observations, while the remaining 44 were used to validate it. The cumulative time (Tf), the impurity type (It), the impurity concentration (Ci), and the moisture content (Wc) were utilized as input variables, and the rate of infiltration was employed as the output. To evaluate the efficiency of the two modeling methodologies, correlation coefficients we estimated root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative square error are all terms that may be used to describe errors (RRSE). The random forest regression approach outperforms the other two models when compared to evolutionary data (ANN and M5P model tree). Using a random forest as a model, regression can properly estimate the infiltration rate within a 25% error range. According to the results of the sensitivity research, cumulative time plays an important influence in determining the soil's penetration rate.

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Modeling and Control of Robotic Manipulators Based on Artificial Neural Networks: A Review

Liu

Peng

Han

et al. 2023

Iran J Sci Technol Trans Mech Eng

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Comparative study of various machine learning algorithms and Denavit–Hartenberg approach for the inverse kinematic solutions in a 3-PPSS parallel manipulator

Cited by 16 publications

References 22 publications

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

Soil Infiltration Rate Impact on Water Quality Modeled Using Random Forest Regression

Modeling and Control of Robotic Manipulators Based on Artificial Neural Networks: A Review

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