Abstract

In this study, forward kinematics, inverse kinematics, dynamic simulation and control of a three-link robotic manipulator for the pouring of molten metal using a small crucible are described. A 3D Computer Added Design (CAD) model of the robotic manipulator is designed in SolidWorks software. Simulation and control of this robotic manipulator were conducted in MATLAB 2016 b. The robotic manipulator SolidWorks CAD file is first converted into an Extensible Markup Language (XML) file after and then imported into MATLAB/Sim-Mechanics environment. A study on forward and inverse dynamics with a sinusoidal wave and cycloid trajectory as input signal was conducted and the joint actuator is controlled by Proportional-Integral-Derivative (PID) controller having a derivative filter. Trajectory tracking is optimized by Integral of Time multiplied by Absolute Error (ITAE) criteria using a pattern search algorithm. Variation in joint torque with a vertical load on the end-effector is described. It was found that the optimized controller reduced the ITAE error by 93.05%, 94.44%, and 97.6% in join1, joint2, and joint3 respectively.

Keyword

Forward kinematics, Inverse kinematics, Robotic manipulator, PID, Trajectory tracking optimization.

Cite this article

Jangid MK, Kumar S, Singh J

Refference

[1][1]Danthala SW, Rao SE, Mannepalli KA, Shilpa D. Robotic manipulator control by using machine learning algorithms: a review. International Journal of Mechanical and Production Engineering Research and Development. 2018; 8(5):305-10.

[2][2]Annisa J, Darus IM, Tokhi MO, Mohamaddan S. Implementation of PID based controller tuned by evolutionary algorithm for double link flexible robotic manipulator. In international conference on computational approach in smart systems design and applications 2018 (pp. 1-5). IEEE.

[3][3]Chen C, Zhang C, Hu T, Ni H, Luo W. Model-assisted extended state observer-based computed torque control for trajectory tracking of uncertain robotic manipulator systems. International Journal of Advanced Robotic Systems. 2018; 15(5):1-12.

[4][4]Ahmed S, Wang H, Tian Y. Adaptive fractional high‐order terminal sliding mode control for nonlinear robotic manipulator under alternating loads. Asian Journal of Control. 2021; 23(4):1900-10.

[5][5]Chang W, Li Y, Tong S. Adaptive fuzzy backstepping tracking control for flexible robotic manipulator. IEEE/CAA Journal of Automatica Sinica. 2018; 8(12):1923-30.

[6][6]Guo Q, Zhang Y, Celler BG, Su SW. Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint. IEEE Transactions on Neural Networks and Learning Systems. 2018; 30(12):3572-83.

[7][7]González-garcía S, Rodríguez-grce J, Loreto-gómez G, Montaño-serrano VM. Teaching forward kinematics in a robotics course using simulations: transfer to a real-world context using LEGO mindstorms™. International Journal on Interactive Design and Manufacturing. 2020; 14(3):773-87.

[8][8]Concha SA, Figueroa-rodríguez JF, Fuentes-covarrubias AG, Fuentes-covarrubias R, Gadi SK. Recycling and updating an educational robot manipulator with open-hardware-architecture. Sensors. 2020; 20(6):1-22.

[9][9]Tang Y, Li X. Simulation research of manipulator control system based on solidworks and simmechanics. Academic Journal of Computing & Information Science. 2018; 1(1):19-27.

[10][10]Karastoyanov D, Karastanev S. Reuse of Industrial Robots. IFAC-PapersOnLine. 2018; 51(30):44-7.

[11][11]Elsisi M, Mahmoud K, Lehtonen M, Darwish MM. An improved neural network algorithm to efficiently track various trajectories of robot manipulator arms. IEEE Access. 2021; 9:11911-20.

[12][12]Mustafa A, Dhar NK, Verma NK. Event-triggered sliding mode control for trajectory tracking of nonlinear systems. IEEE/CAA Journal of Automatica Sinica. 2019; 7(1):307-14.

[13][13]Pan L, Bao G, Xu F, Zhang L. Adaptive robust sliding mode trajectory tracking control for 6 degree-of-freedom industrial assembly robot with disturbances. Assembly Automation. 2018.

[14][14]Rahmani M, Komijani H, Rahman MH. New sliding mode control of 2-DOF robot manipulator based on extended grey wolf optimizer. International Journal of Control, Automation and Systems. 2020;18:1572-80.

[15][15]Sharma R, Rana KP, Kumar V. Comparative study of controller optimization techniques for a robotic manipulator. In proceedings of the third international conference on soft computing for problem solving 2014 (pp. 379-93). Springer, New Delhi.

[16][16]Ilgen S, Durdu A, Gulbahce E, Cakan A. Optimal tuning of the SMC parameters for a two-link manipulator co-simulation control. Elektronika Ir Elektrotechnika. 2021.

[17][17]Soleimani AM, Ramli R. Intelligent trajectory tracking behavior of a multi-joint robotic arm via genetic–swarm optimization for the inverse kinematic solution. Sensors. 2021; 21(9):1-18.

[18][18]Sherrin MR, Manoharan PS, Anand JV. Optimizing PID parameters for non-linear system using social spider optimization algorithm. In international conference on intelligent computing and control systems 2021 (pp. 706-12). IEEE.

[19][19]Kebria PM, Al-Wais S, Abdi H, Nahavandi S. Kinematic and dynamic modelling of UR5 manipulator. In international conference on systems, man, and cybernetics 2016 (pp. 4229-34). IEEE.

[20][20]Xiao J, Han W, Wang A. Simulation research of a six degrees of freedom manipulator kinematics based On MATLAB toolbox. In international conference on advanced mechatronic systems 2017 (pp. 376-80). IEEE.

[21][21]Misra A, Singh G. Kinematic and dynamic analysis of an industrial six-axis robotic manipulator. In international conference on robotics, automation and non-destructive evaluation 2019 (pp.1-14).

[22][22]Zarrin A, Azizi S, Aliasghary M. A novel inverse kinematics scheme for the design and fabrication of a five degree of freedom arm robot. International Journal of Dynamics and Control. 2020; 8(2):604-14.

[23][23]West C, Montazeri A, Monk SD, Taylor CJ. A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator. IFAC-PapersOnLine. 2016; 49(12):1261-6.

[24][24]OMahony T, Downing CJ. Klaudiusz Fatla (Wroclaw University of Technology, Poland). Genetic algorithms for PID parameter optimization, minimizing error criteria.

[25][25]Özdemi̇r MT, Öztürk D. Comparative performance analysis of optimal PID parameters tuning based on the optics inspired optimization methods for automatic generation control. Energies. 2017; 10(12):1-19.

[26][26]Şen Ma, Kalyoncu M. Optimal tuning of PID controller using grey wolf optimizer algorithm for quadruped robot. Balkan Journal of Electrical and Computer Engineering. 2018; 6(1):29-35.

[27][27]Kumar S, Rastogi V, Gupta P. Trajectory control of single arm underwater flexible welding robot using bond graphs. In the proceedings of the international conference on bond graph modelling and simulation, 2014 (pp. 79-84).

[28][28]Kumar S, Kumar S. Trajectory control of underwater flexible robot manipulator using overwhelming controller using bond graph approach. International Journal for Science and Advance Research in Technology. 2015; 1(8):50-5.