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Robust PID optimal tuning of a Delta parallel robot based on a hybrid optimization algorithm of particle swarm optimization and differential evolution

Published online by Cambridge University Press:  12 December 2022

Yong-Ju Pak ,
Yong-Su Kong Open the ORCID record for Yong-Su Kong [Opens in a new window]  and
Jin-Song Ri
Yong-Ju Pak
Affiliation:
Faculty of Electronics and Automation, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea
Yong-Su Kong*
Affiliation:
Faculty of Electronics and Automation, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea
Jin-Song Ri
Affiliation:
Faculty of Electronics and Automation, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea
*
*Corresponding author. E-mail: ys.kong0428@ryongnamsan.edu.kp
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Abstract

In this paper, we propose an approach to tune optimal parameters of a robust PID controller by means of computed torque control (CTC) strategy for trajectory tracking of a Delta parallel robot, using a hybrid optimization algorithm of Particle Swarm Optimization (PSO) and differential evolution (DE). It differs from previous works that they propose robust PID controller parameters tuning based on conventional gradient-based optimization algorithms and apply them to process control. First, we reduce the tuning problem of a robust PID controller with CTC strategy satisfying requirements including robustness and disturbance attenuation to an optimization problem with nonlinear constraints by considering the nonlinear dynamic model of a Delta parallel robot. Second, we set up the design characteristics for the trajectory tracking of a Delta parallel robot. Then, we propose a robust PID controller in a way of obtaining the global optimization solution of the nonlinear optimization problem by running a PSO-DE hybrid optimization algorithm of finding the global optimal solution by maintaining the diversity of swarm during evolution based on the evolution of cognitive experience. Simulation and experimental results demonstrate that the proposed controller outperforms previous works with respect to robust performance and active disturbance attenuation when it is applied to tracking control of a Delta parallel robot.

Keywords

Delta parallel robotparticle swarm optimizationdifferential evolutionrobust PID controlcomputed torque control

Information

Type
Research Article
Information
Robotica , Volume 41 , Issue 4 , April 2023 , pp. 1159 - 1178
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Clavel, R., Device for the movement and positioning of an element in space, Google Patents (1990).Google Scholar
Angel, L. and Viola, J., “Fractional order PID for tracking control of a parallel robotic manipulator type delta,” ISA Trans. 79, 172–188 (2018).CrossRefGoogle ScholarPubMed
Liu, C., Cao, G.-H., Qu, Y.-Y. and Cheng, Y.-M., “An improved PSO algorithm for time-optimal trajectory planning of delta robot in intelligent packaging,” Int. J. Adv. Manuf. Technol. 107(3), 1091–1099 (2020).10.1007/s00170-019-04421-7CrossRefGoogle Scholar
Su, Y. X., Duan, B. Y., Zheng, C. H., Zhang, Y. F., Chen, G. D. and Mi, G. D., “Disturbance rejection high-precision motion control of a Stewart platform,” IEEE Trans. Control Syst. Technol. 12(3), 364–374 (2004).10.1109/TCST.2004.824315CrossRefGoogle Scholar
Taghirad, H. D. Parallel Robots: Mechanics and Control (CRC, Brighton, London, 2013).Google Scholar
Asier, Z., Marga, M., Itziar, C. and Charles, P., “A procedure to evaluate extended computed torque control configurations in the stewart? Gough platform,” Robot Auton. Syst. 59(10), 770–781 (2011).Google Scholar
Kong, Y. S., Zhao, D., Yang, B., Han, C. and Han, K., “Non-fragile multiobjective static output feedback control of vehicle active suspension with time-delay,” Veh. Syst. Dyn. Int. J. Veh. Mech. Mobility 52(7), 948–968 (2014).CrossRefGoogle Scholar
Kong, Y., Zhao, D., Yang, B., Han, C. and Han, K., “Robust non-fragile $H_\infty$ / $L_2-$ $L_\infty$ control of uncertain linear system with time-delay and application to vehicle active suspension,” Int. J. Robust Nonlinear Control 25(13), 2122–2141 (2015).CrossRefGoogle Scholar
Shang, W., Cong, S. and Kong, F., “Identification of dynamic and friction parameters of a parallel manipulator with actuation redundancy,” Mechatronics 20(2), 192–200 (2010).10.1016/j.mechatronics.2009.11.005CrossRefGoogle Scholar
Wu, J., Wang, J., Wang, L. and Li, T., “Dynamic formulation of redundant and nonredundant parallel manipulators for dynamic parameter identification,” Mechatronics 19(4), 586–590 (2009).10.1016/j.mechatronics.2009.01.003CrossRefGoogle Scholar
Luiz, R., Isabelle, Q., Germain, G., Francois, P. and Sophie, T., “Identification and vibration attenuation for the parallel robot Par2,” IEEE Trans. Control Syst. Technol. 22(1), 190–200 (2014).Google Scholar
Zhao, Q., Wang, P. and Mei, J., “Controller parameter tuning of delta robot based on servo identification,” Chin. J. Mech. Eng. 28(2), 267–275 (2015).10.3901/CJME.2014.1117.169CrossRefGoogle Scholar
Miguel, G. and Jaime, A., “Off-line PID control tuning for a planar parallel robot using DE variants,” Exp. Syst. Appl. 64, 444–454 (2016).Google Scholar
Monje, C. A., Chen, Y. Q., Xue, D., Vinagre, B. M. and Feliu, V., “Fractional-order Systems andControls,” In: Fundamentals and Applications, Advances in Industrial Control (Springer-Verlag, London, 2010).Google Scholar
Monje, C. A., Vinagre, B. M., Feliu, V. and Chen, Y., “Tuning and auto-tuning of fractional order controllers for industry applications,” Control Eng. Pract. 16(7), 798–812 (2008).CrossRefGoogle Scholar
Rubio, J. D. J., Ochoa, G., Balcazar, R. and Pacheco, J., “Disturbance rejection in two mechatronic systems,” IEEE Lat. Am. Trans. 14(2), 485–492 (2016).CrossRefGoogle Scholar
Viola, J. and Angel, L., “Factorial design for robustness evaluation of fractional PID controllers,” Lat. Am. Trans. IEEE (Revista IEEE Am. Lat. May) 13(5), 1286–1293 (2015).CrossRefGoogle Scholar
Liu, N. and Wu, J., “Kinematics and application of a hybrid industrial robot-Delta-RST,” Sens. Transducers 169(4), 186–192 (2014).Google Scholar
Noppeney, V., Boaventura, T. and Siqueira, A., “Task-space impedance control of a parallel delta robot using dual quaternions and a neural network,” J. Braz. Soc. Mech. Sci. 43(9), 440–447 (2021).CrossRefGoogle Scholar
Liu, C., Cao, G. and Qu, Y., “Safety analysis via forward kinematics of delta parallel robot using machine learning,” Safety Sci. 117, 243–249 (2019).CrossRefGoogle Scholar
Ayiz, C. and Kucuk, S., “The Kinematics of Industrial Robot Manipulators Based on the Exponential Rotational Matrices,” In: IEEE International Symposium on Industrial Electronics (2009) pp. 977–982.Google Scholar
Kucuk, S. and Gungor, B. D., “Inverse Kinematics Solution of a New Hybrid Robot Manipulator Proposed for Medical Purposes,” In: Medical Technologies National Congress, (Medical Technologies National Congress, 2016).Google Scholar
Fabrizio, P. and Antonio, V. Advances in Robust Fractional Control (Springer, 2015) pp. 34–35.Google Scholar
Chen, Y. Q. and Moore, K. L., “Relay feedback tuning of robust PID controllers with iso-damping property,” IEEE Trans. Syst. Man Cybern Part B 35(1), 23–31 (2005).CrossRefGoogle ScholarPubMed
Clerc, M. and Kennedy, J., “The particle swarm - explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput. 6(1), 58–73 (2002).CrossRefGoogle Scholar
Epitropakis, M. G., Plagianakos, V. P. and Vrahatis, M. N., “Evolving cognitive and social experience in particle swarm optimization through differential evolution: A hybrid approach,” Inf. Sci. 216, 50–92 (2012).CrossRefGoogle Scholar
Swagatam, D. and Ponnuthurai, N. S., “Differential evolution: A survey of the state-of-the-art,” IEEE Trans. Evol. Comput. 15(1), 4–31 (2011).Google Scholar
Afroun, M., Chettibi, T. and Hanchi, S., “Planning Optimal Motions for a DELTA Parallel Robot,” In: 14th Mediterranean Conference on Control and Automation (2006) pp. 28–30.Google Scholar
Xiuqing, H. and Lei, S., “Trajectory Planning and Simulation of a New Symmetric Parallel Mechanism with Three Translational DOF,” In: International Conference on Measuring Technology and Mechatronics Automation (2009) pp. 424–427.Google Scholar