IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i17p3126-d902993.html
   My bibliography  Save this article

Utilisation of Initialised Observation Scheme for Multi-Joint Robotic Arm in Lyapunov-Based Adaptive Control Strategy

Author

Listed:
  • Mohammad Soleimani Amiri

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Rizauddin Ramli

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

Abstract

In this paper, we present a modelling, dynamic analysis, and controller tuning comparison for a five-degree-of-freedom (DoF) multi-joint robotic arm based on the Lyapunov-based Adaptive Controller (LAC). In most pick-and-place applications of robotic arms, it is essential to control the end-effector trajectory to reach a precise target position. The kinematic solution of the 5-DoF robotic arm has been determined by the Lagrangian technique, and the mathematical model of each joint has been obtained in the range of motion condition. The Proportional-Integral-Derivative (PID) control parameters of the LAC have been determined by the Lyapunov stability approach and are initialised by four observation methods based on the obtained transfer function. The effectiveness of the initialised controller’s parameters is compared by a unit step response as the desired input of the controller system. As a result, the average error (AE) for Ziegler–Nichols is 6.6%, 83%, and 53% lower than for Pettit & Carr, Chau, and Bucz. The performance of LAC for the robotic arm model is validated in a virtual 3D model under a robot operating system environment. The results of root mean square error by LAC are 0.021 (rad) and 0.025 (rad) for joint 1 and joint 2, respectively, which indicate the efficiency of the proposed LAC strategy in reaching the predetermined trajectory and the potential of minimizing the controller tuning complexity.

Suggested Citation

  • Mohammad Soleimani Amiri & Rizauddin Ramli, 2022. "Utilisation of Initialised Observation Scheme for Multi-Joint Robotic Arm in Lyapunov-Based Adaptive Control Strategy," Mathematics, MDPI, vol. 10(17), pages 1-14, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3126-:d:902993
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/17/3126/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/17/3126/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haifeng Huang & Mohammadamin Shirkhani & Jafar Tavoosi & Omar Mahmoud, 2022. "A New Intelligent Dynamic Control Method for a Class of Stochastic Nonlinear Systems," Mathematics, MDPI, vol. 10(9), pages 1-15, April.
    2. Mohammad Soleimani Amiri & Rizauddin Ramli & Mohd Faisal Ibrahim & Dzuraidah Abd Wahab & Norazam Aliman, 2020. "Adaptive Particle Swarm Optimization of PID Gain Tuning for Lower-Limb Human Exoskeleton in Virtual Environment," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yingying Liao & Weiguo Zhao & Liying Wang, 2021. "Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers," Mathematics, MDPI, vol. 9(18), pages 1-38, September.
    2. Jiabao Gu & Hui Wang & Wuquan Li & Ben Niu, 2022. "Adaptive State-Feedback Stabilization for Stochastic Nonlinear Systems with Time-Varying Powers and Unknown Covariance," Mathematics, MDPI, vol. 10(16), pages 1-16, August.
    3. Yaqiong Ding & Hanguang Jia & Yunong Zhang & Binbin Qiu, 2023. "High-Order Modeling, Zeroing Dynamics Control, and Perturbations Rejection for Non-Linear Double-Holding Water Tank," Mathematics, MDPI, vol. 11(13), pages 1-18, July.
    4. Libo Yang & Mei Guo & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "Taylor Series-Based Fuzzy Model Predictive Control for Wheeled Robots," Mathematics, MDPI, vol. 10(14), pages 1-13, July.
    5. Saeed Danyali & Omid Aghaei & Mohammadamin Shirkhani & Rahmat Aazami & Jafar Tavoosi & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "A New Model Predictive Control Method for Buck-Boost Inverter-Based Photovoltaic Systems," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    6. Khanh Hieu Nguyen & Sung Hyun Kim, 2022. "Event-Triggered Non-PDC Filter Design of Fuzzy Markovian Jump Systems under Mismatch Phenomena," Mathematics, MDPI, vol. 10(16), pages 1-25, August.
    7. Qi Liu & Hong Lu & Heisei Yonezawa & Ansei Yonezawa & Itsuro Kajiwara & Ben Wang, 2023. "Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems," Mathematics, MDPI, vol. 11(10), pages 1-29, May.
    8. Ye Wang & Zhaiaibai Ma & Mostafa M. Salah & Ahmed Shaker, 2023. "An Evolutionarily Based Type-2 Fuzzy-PID for Multi-Machine Power System Stabilization," Mathematics, MDPI, vol. 11(11), pages 1-18, May.
    9. Ruitao Wang & Hui Wang & Wuquan Li & Ben Niu, 2022. "Output Tracking Control of Random Nonlinear Time-Varying Systems," Mathematics, MDPI, vol. 10(14), pages 1-13, July.
    10. Weijun Hu & Jiale Quan & Xianlong Ma & Mostafa M. Salah & Ahmed Shaker, 2023. "Analytical Design of Optimal Model Predictive Control and Its Application in Small-Scale Helicopters," Mathematics, MDPI, vol. 11(8), pages 1-15, April.
    11. Rahmat Aazami & Omid Heydari & Jafar Tavoosi & Mohammadamin Shirkhani & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "Optimal Control of an Energy-Storage System in a Microgrid for Reducing Wind-Power Fluctuations," Sustainability, MDPI, vol. 14(10), pages 1-14, May.
    12. Xinlan Guo & Mohammadamin Shirkhani & Emad M. Ahmed, 2022. "Machine-Learning-Based Improved Smith Predictive Control for MIMO Processes," Mathematics, MDPI, vol. 10(19), pages 1-19, October.
    13. Wei Xu & Dillip Kumar Das & Željko Stević & Marko Subotić & Adel F. Alrasheedi & Shiru Sun, 2023. "Trapezoidal Interval Type-2 Fuzzy PIPRECIA-MARCOS Model for Management Efficiency of Traffic Flow on Observed Road Sections," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    14. Chan Gu & Encheng Chi & Chujia Guo & Mostafa M. Salah & Ahmed Shaker, 2023. "A New Self-Tuning Deep Neuro-Sliding Mode Control for Multi-Machine Power System Stabilizer," Mathematics, MDPI, vol. 11(7), pages 1-18, March.
    15. Xin Xu & Ahmed Shaker & Marwa S. Salem, 2022. "Automatic Control of a Mobile Manipulator Robot Based on Type-2 Fuzzy Sliding Mode Technique," Mathematics, MDPI, vol. 10(20), pages 1-18, October.
    16. Dong Zhao & Shuyan Sun & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency," Sustainability, MDPI, vol. 14(18), pages 1-14, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3126-:d:902993. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.