-
- AIMS Journals
- AIMS Press Math Journals
-
{{journal.titleEn}}
{{journal.titleEn}}
- Book Series
- {{book.nameEn}}
-
- AIMS Journals
-
{{journal.titleEn}}
{{journal.titleEn}}
- AIMS Press Math Journals
-
{{journal.titleEn}}
{{journal.titleEn}}
- Book Series
- {{book.nameEn}}
Motion control of photovoltaic module dust cleaning robotic arm based on model predictive control
-
Abstract
Carbon neutralization has become a global consensus for green development, and solar photovoltaic power generation has become one of the key technologies for carbon reduction. The presence of dust on a photovoltaic module affects power generation, so the trajectory tracking control of dust removal robotic arm for photovoltaic modules is of great significance for improving power generation efficiency. In this study, a composite trajectory tracking strategy based on model predictive control is designed to track the desired angle of each joint, which is the control objective for the trajectory tracking of the photovoltaic module's dust cleaning robotic arm. The control strategy consists of a model predictive controller and a disturbance observer. Firstly, when there is no external disturbance acting on the system, and the robotic arm model is accurate, the trajectory tracking prediction optimization problem is constructed, and an error feedback correction mechanism is introduced so that the dust cleaning robotic arm tracks the desired trajectory asymptotically. Secondly, when there are model parameter deviations, system time variation, external disturbances, or other uncertain factors, a composite control strategy is established by combining the disturbance observer and model predictive control to compensate for the effects of disturbances through feedback, thus improving the stability and accuracy of the robotic arm control system. Finally, the feasibility of the composite control tracking strategy is verified by numerical simulation. The results show that the designed predictive controller has high error control accuracy and fast solution speed, and it can realize real-time and robust trajectory tracking of the robotic arm with a constrained dust cleaning assembly.
-
Keywords:
- Model predictive control,
- photovoltaic power generation,
- dust cleaning component robotic arm,
- disturbance observer,
- track tracking.
Mathematics Subject Classification: Primary: 93-10.Citation: 
-
-
Figure 1. Schematic diagram of working position of dust cleaning assembly
Figure 2. Principle of robotic arm of three degree of freedom dust cleaning assembly
Figure 3. Model predictive control principle
Figure 4. Control scheme
Figure 5. Joint position and control torque of three degree of freedom robotic arm
Figure 6. Joint position and control torque of three degree of freedom robotic arm
Figure 7. Model predictive control based on disturbance observer
Figure 8. Model predictive control based on disturbance observer
Table 1. Main parameters
Parameter Value First linkage mass/($ {\rm kg} $) 1 Second linkage mass/($ {\rm kg} $) 1 First linkage length/($ {\rm {m}} $) 0.5 Second linkage length/($ {\rm {m}} $) 0.5 Gravitational acceleration/($ {\rm {m/{s^2}}} $) 9.8 | Show Table
DownLoad: CSV
-
References
[1] F. Alambeigi, M. Bakhtiarinejad and S. Sefati, On the use of a continuum manipulator and a bendable medical screw for minimally invasive interventions in orthopedic surgery, IEEE Transactions on Medical Robotics and Bionics, 1 (2019), 14-21. doi: 10.1109/TMRB.2019.2895780. 
[2] J. Baek, M. Jin and S. Han, A new adaptive sliding-mode control scheme for application to robot manipulators, IEEE Transactions on Industrial Electronics, 63 (2016), 3628-3637.
[3] A. Banerjee, B. Bhattacharya and A. K. Mallik, Large deflection of cantilever beams with geometric non-linearity: Analytical and numerical approaches, International Journal of Non-Linear Mechanics, 43 (2008), 366-376. doi: 10.1016/j.ijnonlinmec.2007.12.020.
[4] V. M. Becerra, C. N. J. Cage and W. S. Harwin, Hardware retrofit and computed torque control of a Puma 560 Robot updating an industrial manipulator, IEEE Control Systems Magazine, 24 (2004), 78-82. doi: 10.1109/MCS.2004.1337867.
[5] M. G. Forbes, R. S. Patwardhan and H. Hamadah, Model predictive control in industry: Challenges and opportunities, IFAC-PapersOnLine, 48 (2015), 531-538. doi: 10.1016/j.ifacol.2015.09.022.
[6] Y. Gao, R. Li and Y. Shi, Design of path planning and tracking control of quadrotor, J. Ind. Manag. Optim., 18 (2022), 2221-2235. doi: 10.3934/jimo.2021063.
[7] R. Grandia, A. J. Taylor and A. Singletary, Nonlinear model predictive control of robotic systems with control lyapunov functions, arXiv Preprint, (2020), 2006.01229. doi: 10.15607/RSS.2020.XVI.098.
[8] R. Hedjar, R. Toumi, P. Boucher and D. Dumur, Feedback nonlinear predictive control of rigid link robot manipulators, Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301), 5 (2002), 3594-3599. doi: 10.1109/ACC.2002.1024486.
[9] S. Heshmati-Alamdari, C. P. Bechlioulis and G. C. Karras, A robust interaction control approach for underwater vehicle manipulator systems, Annual Reviews in Control, 46 (2018), 315-325. doi: 10.1016/j.arcontrol.2018.10.003.
[10] E. Hill, S. A. Gadsden and M. Biglarbegian, Explicit nonlinear MPC for fault tolerance using interacting multiple models, IEEE Transactions on Aerospace and Electronic Systems, 57 (2021), 2784-2794. doi: 10.1109/TAES.2021.3065089.
[11] M. B. Hong, S. J. Kim and Y. S. Ihn, KULEX-hand: An underactuated wearable hand for grasping power assistance, IEEE Transactions on Robotics, 35 (2018), 420-432. doi: 10.1109/TRO.2018.2880121.
[12] Y. Hou, F. Fu and J. Chen, Analysis on dynamic feature of cross arm light weighting for photovoltaic panel cleaning device in power station based on power correlation, Open Physics, 18 (2020), 492-503. doi: 10.1515/phys-2020-0115.
[13] S. Jung, Improvement of tracking control of a sliding mode controller for robot manipulators by a neural network, International Journal of Control, Automation and Systems, 16 (2018), 937-943.
[14] E. Kang, H. Qiao and J. Gao, Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints, ISA Transactions, 109 (2021), 89-101. doi: 10.1016/j.isatra.2020.10.009.
[15] J. C. Kim, D. S. Pae and M. T. Lim, Obstacle avoidance path planning based on output constrained model predictive control, International Journal of Control, Automation and Systems, 17 (2019), 2850-2861. doi: 10.1007/s12555-019-9091-y.
[16] X. Kong and X. Liu, Nonlinear model predictive control for continuous systems based on input-output linearization, Control theory and Application, 29 (2012), 217-224.
[17] A. Kozáková, Discrete-time asymptotic reference tracking for an industrial robot, 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), (2014), 1-6. doi: 10.1109/RAAD.2014.7002279.
[18] A. Kumar and V. Kumar, Evolving an interval type-2 fuzzy PID controller for the redundant robotic manipulator, Expert Systems with Applications, 73 (2017), 161-177. doi: 10.1016/j.eswa.2016.12.029.
[19] J. Lee, P. H. Chang and M. Jin, Adaptive integral sliding mode control with time-delay estimation for robot manipulators, IEEE Transactions on Industrial Electronics, 64 (2017), 6796-6804. doi: 10.1109/TIE.2017.2698416.
[20] S. Li, J. Yang and W. H. Chen, Disturbance Observer-Based Control: Methods and Applications, CRC Press, 2014. doi: 10.1201/b16570.
[21] Y. Luo, Y. Xia and Z. Sun, Robust event-triggered model predictive control for constrained linear continuous system, International Journal of Robust and Nonlinear Control, 29 (2019), 1216-1229. doi: 10.1002/rnc.4432.
[22] J. Mattingley and S. Boyd, CVXGEN: A code generator for embedded convex optimization, Optimization and Engineering, 13 (2012), 1-27. doi: 10.1007/s11081-011-9176-9.
[23] W. Meng, T. Piao and D. Si, Study on the impact of dust on photovoltaic power generation and cleaning of modules, Solar Energy, 2 (2015), 22-27.
[24] N. Mimmo, A. Macchelli and R. Naldi, Robust motion control of aerial manipulators, Annual Reviews in Control, 49 (2020), 230-238. doi: 10.1016/j.arcontrol.2020.04.006.
[25] D. Nicolis, F. Allevi and P Rocco, Operational space model predictive sliding mode control for redundant manipulators, IEEE Transactions on Robotics, 36 (2020), 1348-1355. doi: 10.1109/TRO.2020.2974092.
[26] H. Ning, W. Wang and H. Min, Research on dynamics of mechanical arm of dust cleaning equipment in photovoltaic power station, Journal of Solar Energy, 41 (2020), 167-173.
[27] Y. P. Pane, S. P. Nageshrao and J. Kober, Reinforcement learning based compensation methods for robot manipulators, Engineering Applications of Artificial Intelligence, 78 (2019), 236-247. doi: 10.1016/j.engappai.2018.11.006.
[28] P. Poignet and M. Gautier, Nonlinear model predictive control of a robot manipulator, 6th International Workshop on Advanced Motion Control, Proceedings (Cat. No. 00TH8494), (2000), 401-406. doi: 10.1109/AMC.2000.862901.
[29] D. A. Spencer and R. D. Braun, Mars Pathfinder atmospheric entry-Trajectory design and dispersion analysis, Journal of Spacecraft and Rockets, 33 (1996), 670-676. doi: 10.2514/3.26819.
[30] L. Stella, A. Themelis and P. Sopasakis, A simple and efficient algorithm for nonlinear model predictive control, 2017 IEEE 56th Annual Conference on Decision and Control (CDC), (2017), 1939-1944. doi: 10.1109/CDC.2017.8263933.
[31] A. Stolfi, P. Gasbarri and M. Sabatini, A combined impedance-PD approach for controlling a dual-arm space manipulator in the capture of a non-cooperative target, Acta Astronautica, 139 (2017), 243-253. doi: 10.1016/j.actaastro.2017.07.014.
[32] Y. Tan, Q. Cao and L. Li, A chance-constrained stochastic model predictive control problem with disturbance feedback, J. Ind. Manag. Optim., 17 (2021), 67-79. doi: 10.3934/jimo.2019099.
[33] B. Varga, S. Meier and S. Schwab, Model predictive control and trajectory optimization of large vehicle-manipulators, 2019 IEEE International Conference on Mechatronics (ICM), 1 (2019), 60-66. doi: 10.1109/ICMECH.2019.8722886.
[34] W. Wang and K. Siau, Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda, Journal of Database Management (JDM), 30 (2019), 61-79. doi: 10.4018/JDM.2019010104.
[35] C. Wu, J. Yang and S. Li, Disturbance observer based model predictive control for accurate atmospheric entry of spacecraft, Advances in Space Research, 61 (2018), 2457-2471. doi: 10.1016/j.asr.2018.02.010.
[36] G. Wu, X. Guo and L. Zhang, Design of multi-objective robust control algorithm for auto adaptive cruise following, Journal of Harbin Institute of Technology, 48 (2016), 80-86.
[37] Y. Xi, X. Xu and Z. Zhang, Research status of predictive control and multilayer intelligent predictive control, Control theory and Application, 6 (1989), 1-7.
[38] S. Zaare, M. R. Soltanpour and M. Moattari, Voltage based sliding mode control of flexible joint robot manipulators in presence of uncertainties, Robotics and Autonomous Systems, 118 (2019), 204-219. doi: 10.1016/j.robot.2019.05.014.
[39] J. Zhang, Q. Li and D. Chen, Vehicle multi-mode multi-objective adaptive cruise control, Journal of University of Electronic Science and Technology, 47 (2018), 368-374.
[40] A. Zolotas, Disturbance observer-based control: methods and applications [bookshelf], IEEE Control Systems Magazine, 35 (2015), 55-57. doi: 10.1109/MCS.2015.2408011.
-
Access History
Figures(8)
Tables(1)
Export File
Citation
Format
Content
-
Figure 1.
Schematic diagram of working position of dust cleaning assembly
-
Figure 2.
Principle of robotic arm of three degree of freedom dust cleaning assembly
-
Figure 3.
Model predictive control principle
-
Figure 4.
Control scheme
-
Figure 5.
Joint position and control torque of three degree of freedom robotic arm
-
Figure 6.
Joint position and control torque of three degree of freedom robotic arm
-
Figure 7.
Model predictive control based on disturbance observer
-
Figure 8.
Model predictive control based on disturbance observer