Trajectory Tracking based on Sliding Mode Controller
International Journal of Electronics and Communication Engineering |
© 2023 by SSRG - IJECE Journal |
Volume 10 Issue 1 |
Year of Publication : 2023 |
Authors : Fakhur-un-nisa Alias Fizza Syed, Shakeel Ahmed Shaikh, Saifullah Samo , Qamar un nisaKamal |
How to Cite?
Fakhur-un-nisa Alias Fizza Syed, Shakeel Ahmed Shaikh, Saifullah Samo , Qamar un nisaKamal, "Trajectory Tracking based on Sliding Mode Controller," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 1, pp. 1-5, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I1P101
Abstract:
This paper presents trajectory tracking based on a sliding mode controller for the Puma robot manipulator. Puma is most commonly used in industries; it has great flexibility compared to other manipulators like SCARA, which decreases its precision. In order to increase the precision Sliding mode controller based on the Lyapunov stability approach is used in this work. The first motion control block for the sliding mode Controller is designed and link it with the Puma Robot manipulator in MATLAB Simulink. The Experiment Results for the effectiveness of this method are verified.
Keywords:
Trajectory Tracking, Sliding mode, PUMA560 Robot, Trajectory generation.
References:
[1] Joseph Davidson, “Robotic Manipulation for Specialty Crop Harvesting: A Review of Manipulator and End-Effector Technologies,” Global Journal of Agricultural and Allied Sciences, vol. 2, no. 1, pp. 25–41, 2020. Crossref, https://doi.org/10.35251/gjaas.2020.004
[2] Bin Wei, “A Tutorial on Robust Control, Adaptive Control and Robust Adaptive Control—Application to Robotic Manipulators,” Inventions, vol. 4, no. 3, pp. 1-13, 2019. Crossref, https://doi.org/10.3390/inventions4030049
[3] Ethelbert Ezemobi et al., “Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine,” Energies, vol. 14, no. 8, pp. 1-15, 2021. Crossref, https://doi.org/10.3390/en14082243
[4] Long Chen et al., “Extreme-Learning-Machine-Based Robust Integral Terminal Sliding Mode Control of Bicycle Robot,” Control Engineering Practice, vol. 121, 2022. Crossref, https://doi.org/10.1016/j.conengprac.2022.105064
[5] Yuxiang Wu et al., “Adaptive Neural Network Control of Uncertain Robotic Manipulators with External Disturbance and Time-Varying Output Constraints,” Neurocomputing, vol. 323, pp. 108–116, 2019. Crossref, https://doi.org/10.1016/j.neucom.2018.09.072S.
[6] Chouraqui, and Habiba Benzater, “A Multiobjective Genetic Algorithm Applied to Control Optimization,” Science International, vol. 3, no. 1, pp. 7-17, 2015.
[7] Qingyun Zhang et al., “Adaptive Sliding Mode Neural Network Control and Flexible Vibration Suppression of a Flexible Spatial Parallel Robot,” Electronics, vol. 10, no. 2, pp. 1-22, 2021. Crossref, https://doi.org/10.3390/electronics10020212
[8] Anh Tuan Vo, Mr. Truong Thanh Nguyen, and Dr. Hee-Jun Kang, “A Novel Prescribed-Performance-Tracking Control System with Finite-Time Convergence Stability for Uncertain Robotic Manipulators,” Sensors, vol. 22, no. 7, pp. 1-18, 2022. Crossref, https://doi.org/10.3390/s22072615
[9] Peiyu Wang et al., “Prescribed Performance Control with Sliding-Mode Dynamic Surface for a Glue Pump Motor Based on Extended State Observers,” Actuators, vol. 10, no. 11, pp. 1-28, 2021. Crossref, https://doi.org/10.3390/act10110282
[10] Qiang Chen, “Adaptive Robust Finite-Time Neural Control of Uncertain PMSM Servo System with Nonlinear Dead Zone,” Neural Computing and Applications, vol. 28, pp. 3725–3736, 2017. Crossref, https://doi.org/10.1007/s00521-016-2260-5
[11] Ngoc Phi Nguyen et al., “Finite-Time Attitude Fault Tolerant Control of Quadcopter System via Neural Networks,” Mathematics, vol. 8, no. 9, 2020. Crossref, https://doi.org/10.3390/math8091541
[12] Saeed Benjamin Niku, Introduction to Robotics: Analysis, Control, Applications, John Wiley & Sons: Hoboken, NJ, USA, p. 480, 2020
[13] Haoyan Zhang et al., “Adaptive Fuzzy Hierarchical Sliding Mode Control of Uncertain Under-Actuated Switched Nonlinear Systems With Actuator Faults,” International Journal of Systems Science, vol. 52, no. 8, pp. 1499–1514, 2021. Crossref, https://doi.org/10.1080/00207721.2020.1831645
[14] Zhi-Min Li et al., “Quantized Static Output Feedback Fuzzy Tracking Control for Discrete-Time Nonlinear Networked Systems with Asynchronous Event-Triggered Constraints,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, pp. 3820– 3831, 2021. Crossref, https://doi.org/10.1109/TSMC.2019.2931530
[15] Li Ma et al., “Small-Gain Technique-Based Adaptive Neural Output-Feedback Fault-Tolerant Control of Switched Nonlinear Systems with Unmodeled Dynamics,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 11, pp. 7051–7062, 2020. Crossref, https://doi.org/10.1109/TSMC.2020.2964822
[16] Yulin Li et al., “Command Filter-Based Adaptive Neural Finite-Time Control for Stochastic Nonlinear Systems with Time-Varying Full-State Constraints and Asymmetric Input Saturation,” International Journal of Systems Science, vol. 53, no. 1, pp. 199–221, 2022. Crossref, https://doi.org/10.1080/00207721.2021.1943562
[17] Somdavee Bhosinak, Dechrit Maneetham, and Tenzin Rabgyal, "Hybrid Fuzzy PID Controller for Intelligent Tractor Steering Control," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 359-369, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P235
[18] Wen-Kung Tseng, and Hou-Yu Chen, "The Study of Tracking Control for Autonomous Vehicle," SSRG International Journal of Mechanical Engineering, vol. 7, no. 11, pp. 57-62, 2020. Crossref, https://doi.org/10.14445/23488360/IJME-V7I11P108
[19] Yukai Zhu, Jianzhong Qiao, and Lei Guo, “Adaptive Sliding Mode Disturbance Observer-Based Composite Control with Prescribed Performance of Space Manipulators for Target Capturing,” IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 1973–1983, 2019. Crossref, https://doi.org/10.1109/TIE.2018.2838065
[20] Yang Liu, Xiaoping Liu, and Yuanwei Jing, “Adaptive Neural Networks Finite-Time Tracking Control for Non-Strict Feedback Systems via Prescribed Performance,” Information Sciences, vol. 468, pp. 29–46, 2018. Crossref, https://doi.org/10.1016/j.ins.2018.08.029
[21] Yuanwei Jing, Yang Liu, and Shaowei Zhou, “Prescribed Performance Finite-Time Tracking Control for Uncertain Nonlinear Systems,” Journal of Systems Science and Complexity, vol. 32, pp. 803–817, 2019. Crossref, https://doi.org/10.1007/s11424-018-7287-5
[22] Zhi-Gang Zhoua, “Prescribed Performance Fixed-Time Tracking Control for a Class of Second-Order Nonlinear Systems with Disturbances and Actuator Saturation,” International Journal of Control, vol. 94, no. 1, 223–234, 2020. Crossref, https://doi.org/10.1080/00207179.2019.1590644
[23] Runqi Chai et al., “Six-DOF Spacecraft Optimal Trajectory Planning and Real-Time Attitude Control: A Deep Neural Network-Based Approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 11, pp. 5005–5013, 2020. Crossref, https://doi.org/10.1109/TNNLS.2019.2955400
[24] Runqi Chai et al., “Dual-Loop Tube-Based Robust Model Predictive Attitude Tracking Control for Spacecraft with System Constraints and Additive Disturbances,” IEEE Transactions on Industrial Electronics, vol. 69, no. 4, pp. 4022 – 4033, 2022. Crossref, https://doi.org/10.1109/TIE.2021.3076729
[25] Xudong Cao, Jianjun Wang, and Wei Xiang, “Composite Adaptive Fuzzy Prescribed Performance Control of Nonlinear Systems,” Mathematical Problems in Engineering, vol. 2020, 2020. Crossref, https://doi.org/10.1155/2020/2948130
[26] J. Tao, and T. Zhang, “Novel Finite-Time Adaptive Neural Control of Flexible Spacecraft with Actuator Constraints and Prescribed Attitude Tracking Performance,” Acta Astronautica, vol. 179, pp. 646-658, 2021. Crossref, https://doi.org/10.1016/j.actaastro.2020.10.010
[27] Anh Tuan Vo, and Hee-Jun Kang, “An Adaptive Terminal Sliding Mode Control for Robot Manipulators with Non-Singular Terminal Sliding Surface Variables,” IEEE Access, vol. 7, pp. 8701–8712, 2018. Crossref, https://doi.org/10.1109/ACCESS.2018.2886222
[28] Thanh Nguyen Truong, Anh Tuan Vo, and Hee-Jun Kang, “A Backstepping Global Fast Terminal Sliding Mode Control for Trajectory Tracking Control of Industrial Robotic Manipulators,” IEEE Access, vol. 9, pp. 31921–31931, 2021. Crossref, https://doi.org/10.1109/ACCESS.2021.3060115