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Sterowanie trybem swobodnego przesuwania dla manipulatora robota szeregowego: robo ze sztywnym i elastycznym przegubem
Języki publikacji
Abstrakty
The control of robotic manipulators with flexible joints is still a challenging problem for researchers. In this paper, a comprehensive investigation into the dynamic modeling and control of a two-degree-of-freedom (2-DOF) robot, both with and without flexible joints, is presented. Firstly, the mathematical modeling of the considered robot is presented to understand its dynamical behavior. Then, two types of controllers, namely the intelligent Proportional-Derivative (iPD) and the intelligent Proportional-Derivative Sliding Mode (iPDSM), are applied to the considered robot and purposefully compared in terms of robustness, setting time, and overshoot while tracking various trajectories. Two cases are considered in the simulation tests: In the first case, trajectory tracking is performed with no elasticity at the joints. However, in the second case, elasticity and damping are added. To verify the effectiveness of the proposed controllers, simulation analyses through MATLAB are carried out. Based on the obtained results, iPDSM provides satisfying results compared to iPD. Namely, iPDSM accurately generates the angular motion of the robot’s flexible joints, allowing the robot to properly track the prescribed trajectory independently of any information derived from the mathematical model, even in the presence of Stribeck friction and elasticity.
Sterowanie robotami manipulacyjnymi wyposażonymi w elastyczne przeguby nadal stanowi wyzwanie dla badaczy. W artykule przedstawiono kompleksowe badanie dynamicznego modelowania i sterowania robotem o dwóch stopniach swobody (2-DOF), zarówno z elastycznymi przegubami, jak i bez nich. W pierwszej kolejności zaprezentowano model matematyczny rozpatrywanego robota, pozwalający zrozumieć jego zachowanie dynamiczne. Następnie do rozpatrywanego robota stosowane są dwa typy sterowników, a mianowicie inteligentny tryb proporcjonalno-różniczkujący (iPD) i inteligentny tryb proporcjonalno-różniczkujący (iPDSM), które są stosowane do rozpatrywanego robota i celowo porównywane pod względem wytrzymałości, czasu ustawiania i przeregulowania podczas śledzenia różnych trajektorie. W badaniach symulacyjnych uwzględniane są dwa przypadki: W pierwszym przypadku śledzenie trajektorii odbywa się bez elastyczności w stawach. Jednak w drugim przypadku dodaje się elastyczność i tłumienie. W celu sprawdzenia efektywności proponowanych sterowników przeprowadzane są analizy symulacyjne w programie MATLAB. Na podstawie uzyskanych wyników iPDSM zapewnia satysfakcjonujące wyniki w porównaniu do iPD. Mianowicie iPDSM dokładnie generuje ruch kątowy elastycznych przegubów robota, umożliwiając robotowi prawidłowe śledzenie zadanej trajektorii niezależnie od jakichkolwiek informacji pochodzących z modelu matematycznego, nawet w obecności tarcia i elastyczności Stribecka.
Wydawca
Czasopismo
Rocznik
Tom
Strony
139--144
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
autor
- Laboratory of Automation and Systems Analysis LAAS, National polytechnic school of Oran, Algeria
autor
- Laboratory of Automation and Systems Analysis LAAS, National polytechnic school of Oran, Algeria
autor
- Laboratory of Automation and Systems Analysis LAAS, National polytechnic school of Oran, Algeria
autor
- Laboratory of Automation and Systems Analysis LAAS, National polytechnic school of Oran, Algeria
Bibliografia
- [1] Farzaneh Abdollahi, Heidar A Talebi, and Rajnikant V Patel. A stable neural network-based observer with application to flexible-joint manipulators. IEEE Transactions on Neural Net- works, 17(1):118–129, 2006.
- [2] Hassane Abouaïssa and Samira Chouraqui. On the control of robot manipulator: A model-free approach. Journal of Compu- tational Science, 31:6–16, 2019.
- [3] John T Agee, Selcuk Kizir, and Zafer Bingul. Intelligent proportional-integral (ipi) control of a single link flexible joint ma- nipulator. Journal of Vibration and Control, 21(11):2273–2288, 2015.
- [4] A Ailon and R Ortega. An observer-based set-point controller for robot manipulators with flexible joints. Systems & Control Letters, 21(4):329–335, 1993.
- [5] Bernard Brogliato, Romeo Ortega, and Rogelio Lozano. Global tracking controllers for flexible-joint manipulators: a compara- tive study. Automatica, 31(7):941–956, 1995.
- [6] Francisco Javier Carrillo and Frédéric Rotella. Some contribu- tions to estimation for model-free control. IFAC-PapersOnLine, 48(28):150–155, 2015.
- [7] Aneesh N Chand, Michihiro Kawanishi, and Tatsuo Narikiyo.Non-linear model-free control of flapping wing flying robot using ipid. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 2930–2937. IEEE, 2016.
- [8] Ming-Chih Chien and An-Chyau Huang. Adaptive control for flexible-joint electrically driven robot with time-varying un- certainties. IEEE Transactions on Industrial Electronics, 54(2):1032–1038, 2007.
- [9] Brigitte d’Andréa Novel, Lghani Menhour, Michel Fliess, and Hugues Mounier. Some remarks on wheeled autonomous vehicles and the evolution of their control design. IFAC- PapersOnLine, 49(15):199–204, 2016.
- [10] Burak Ergöçmen and Umut Tilki. ˙Iki serbestlik dereceli robotun sdre algoritması ile kontrolü two degree of freedom manipulator control by sdre algorithm.
- [11] Tao Fan and Clarence W de Silva. Dynamic modelling and model predictive control of flexible-link manipulators. Interna- tional Journal of Robotics & Automation, 23(4):227, 2008.
- [12] Mohammad Mehdi Fateh. Nonlinear control of electrical flexible-joint robots. Nonlinear Dynamics, 67(4):2549–2559, 2012.
- [13] Maged Iskandar, Christiaan van Ommeren, Xuwei Wu, Alin Albu-Schaffer, and Alexander Dietrich. Model predictive control for flexible joint robots. arXiv preprint arXiv:2210.08084, 2022.
- [14] Maolin Jin, Jinoh Lee, and Nikolaos G Tsagarakis. Model-free robust adaptive control of humanoid robots with flexible joints. IEEE Transactions on Industrial Electronics, 64(2):1706–1715, 2016.
- [15] Tolgay Kara and Ali Hussien Mary. Adaptive pd-smc for nonlin- ear robotic manipulator tracking control. Studies in Informatics and Control, 26(1):49–58, 2017.
- [16] Joonyoung Kim and Elizabeth A Croft. Full-state tracking con- trol for flexible joint robots with singular perturbation techniques. IEEE Transactions on Control Systems Technology, 27(1):63–73, 2017.
- [17] Min Jun Kim, Fabian Beck, Christian Ott, and Alin Albu- Schäffer. Model-free friction observers for flexible joint robots with torque measurements. IEEE Transactions on Robotics, 35(6):15081515, 2019.
- [18] M Habibnejad Korayem and SR Nekoo. Finite-time state- dependent riccati equation for time-varying nonaffine systems: Rigid and flexible joint manipulator control. ISA transactions, 54:125–144, 2015.
- [19] Frédéric Lafont, Jean-François Balmat, Nathalie Pessel, and Michel Fliess. A model-free control strategy for an experimental greenhouse with an application to fault accommodation. Com- puters and Electronics in Agriculture, 110:139–149, 2015.
- [20] Jaeyoung Lee, Je Sung Yeon, Jong Hyeon Park, and Sanghun Lee. Robust back-stepping control for flexible-joint robot manip- ulators. In 2007 IEEE/RSJ International Conference on Intelli- gent Robots and Systems, pages 183–188. IEEE, 2007.
- [21] Christopher Lehnert and Gordon Wyeth. Locally weighted learning model predictive control for nonlinear and time varying dynamics. In 2013 IEEE International Conference on Robotics and Automation, pages 2619–2625. IEEE, 2013.
- [22] Hua-Shan Liu and Yong Huang. Bounded adaptive output feed- back tracking control for flexible-joint robot manipulators. Jour- nal of Zhejiang University-SCIENCE A, 19(7):557–578, 2018.
- [23] Huashan Liu, Kuangrong Hao, and Xiaobo Lai. Fuzzy saturated output feedback tracking control for robot manipulators: a sin- gular perturbation theory based approach. International Journal of Advanced Robotic Systems, 8(4):35, 2011.
- [24] Van-Anh Nguyen, Anh-Tu Nguyen, Antoine Dequidt, Laurent Vermeiren, and Michel Dambrine. Lmi-based 2-dof control design of a manipulator via ts descriptor approach. IFAC- PapersOnLine, 51(22):102–107, 2018.
- [25] Taghreed Mohammad Ridha and Claude H Moog. Model free control for type-1 diabetes: A fasting-phase study. IFAC- PapersOnLine, 48(20):76–81, 2015.
- [26] Raul-Cristian Roman, Mircea-Bogdan Radac, Radu-Emil Pre- cup, and Emil M Petriu. Data-driven optimal model-free control of twin rotor aerodynamic systems. In 2015 IEEE International Conference on Industrial Technology (ICIT), pages 161–166. IEEE, 2015.
- [27] MARKW Spong, Khashayar Khorasani, and Petar Kokotovic. An integral manifold approach to the feedback control of flex-ible joint robots. IEEE Journal on Robotics and Automation, 3(4):291–300, 1987.
- [28] Thi van Anh Nguyen. Commande de robots manipulateurs basée sur le modèle de Takagi-Sugeno: nouvelle approche pour le suivi de trajectoire. PhD thesis, Université Polytechnique Hauts-de-France, 2019.
- [29] Jorge Villagra, Blas Vinagre, and Inés Tejado. Data-driven fractional pid control: application to dc motors in flexible joints. IFAC Proceedings Volumes, 45(3):709–714, 2012.
- [30] Haoping Wang, Xuefei Ye, Yang Tian, and Nicolai Christov. Attitude control of a quadrotor using model free based sliding model controller. In 2015 20th international conference on control systems and computer science, pages 149–154. IEEE, 2015.
- [31] Xiaofeng Wang, Xing Li, Jianhui Wang, Xiaoke Fang, and Xue- feng Zhu. Data-driven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleton. Informa- tion Sciences, 327:246–257, 2016.
- [32] Cheng-Yang Yu and Jenq-Lang Wu. Intelligent pid control for two-wheeled inverted pendulums. In 2016 International Conference on System Science and Engineering (ICSSE), pages 1–4. IEEE, 2016.
- [33] Anlong Zhang, Zhiyun Lin, Bo Wang, and Zhimin Han. Non- linear model predictive control of single-link flexible-joint robot using recurrent neural network and differential evolution opti- mization. Electronics, 10(19):2426, 2021.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-94002b26-9e72-493b-a3c2-7754522e70a1
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