journal article·2026-02-20Open access

Integrated Mathematical Modelling of a Robot Manipulator Control System Using ANSYS and MATLAB Simulink for Accurate Dynamic Response Prediction

Chenfei Wen, Maksim A. Grigorev, Victor Kushnarev, Siyuan Zhang, Ivan Kholodilin

Applied Sciences· Volume 16· Issue 4

Abstract

As robotic manipulators evolve toward lightweight and long-link structures, flexibility increasingly affects dynamic response and trajectory tracking accuracy. However, existing studies often lack a consistent coupling mechanism between finite element structural models and control models, and flexible effects are typically treated as disturbances, limiting the direct use of structural parameters for control prediction and optimization. This paper proposes a structure–control collaborative co-simulation framework for a six-degree-of-freedom (6-DOF) flexible-joint manipulator. ANSYS-based finite element analysis (FEA) is integrated with the MATLAB/Simulink control environment to extract joint-level equivalent stiffness, inertia, modal frequencies, and damping parameters, which are embedded into a rigid–flexible coupled dynamic model. A regression-based representation is introduced to capture unmodeled flexible residual dynamics, and a regression-compensated adaptive PID torque controller with σ-modification and a dead-zone mechanism is developed to ensure bounded adaptation and closed-loop stability. Simulation results under no-load and payload conditions demonstrate improved oscillation suppression and tracking accuracy. By establishing a unified coupling mechanism from structural parameters to the control model, the proposed method achieves consistent co-modeling of the structural and control domains and provides an engineering-feasible co-simulation approach for dynamic prediction and control optimization of multi-DOF flexible manipulators under varying operating conditions.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Dynamics and Control of Mechanical Systems · Control Systems in Engineering · Hydraulic and Pneumatic Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Electric Drive, Mechatronics and Electromechanics, South Ural State University, Chelyabinsk 454080, Russia

Funding

Russian Science Foundation

References (35)

Show source-provided references
1. Global modes for the reduction of flexible multibody systems10.1007/s11044-021-09790-0 ↗
2. Model Order Reduction Using Modal Derivatives for Nonlinear Flexible Multibody Systems
3. Payload Parameter Identification of a Flexible Space Manipulator System via Complex Eigenvalue Estimation10.1155/2020/5142925 ↗
4. Jing, H., Ma, X., Chen, M., and Chen, J. (2025). Dynamic Parameter Identification Method for Space Manipulators Based on Hybrid Optimization Strategy. Actuators, 14.10.3390/act14100497 ↗
5. Cheng, J., Bi, S., and Yuan, C. (2022). Dynamic Parameters Identification Method of 6-DOF Industrial Robot Based on Quaternion. Mathematics, 10.10.3390/math10091513 ↗
6. Autonomous Modal Analysis Method for Industrial Robots Considering Dynamic Spatial Sensitivity and Excitation Randomness10.1038/s41598-025-95339-6 ↗
7. Seyed Hosseini, S.H., Hajzargarbashi, S., Côté, G., and Liu, Z. (2025). Transfer Learning Approach for Estimating Modal Parameters of Robot Manipulators Using Minimal Experimental Data. Vibration, 8.10.3390/vibration8040065 ↗
8. Seyed Hosseini, S.H., Hajzargarbashi, S., and Liu, Z. (2025). Enhancing robotic manipulator performance through analyzing vibration, identifying deep-learning-based modal parameters and estimating frequency response functions. Machines, 13.10.21203/rs.3.rs-4939775/v1 ↗
9. Subedi, D., Tyapin, I., and Hovland, G. (2021). Dynamic Modeling of Planar Multi-Link Flexible Manipulators. Robotics, 10.10.3390/robotics10020070 ↗
10. Dynamic Modeling of Flexible Robots Based on Different Discretization Methods
11. Salamina, L., Botto, D., Mauro, S., and Pastorelli, S. (2020). Modeling of Flexible Bodies for the Study of Control in the Simulink Environment. Appl. Sci., 10.10.3390/app10175861 ↗
12. Stavropoulos, P., Gerontas, C., Bikas, H., and Souflas, T. (July, January 29). Multi-body dynamic simulation of a machining robot driven by CAM. Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022, Lugano, Switzerland.10.1016/j.procir.2022.05.059 ↗
13. Motion Control Strategy for Robotic Arm Using Deep Cascaded Feature-Enhancement Bayesian Broad Learning System with Motion Constraints10.1016/j.isatra.2025.02.027 ↗
14. Motion Control Strategy for Robotic Arm Using Cascaded Feature-Enhancement ElasticNet Broad Learning System10.1016/j.conengprac.2025.106278 ↗
15. Dong, F., Yu, B., Zhao, X., Chen, S., and Liu, H. (2021). Constraint-Following Servo Control for Trajectory Tracking of Manipulator with Flexible Joints and Mismatched Disturbances. Machines, 9.10.3390/machines9090202 ↗
16. Xu, H., Yang, Q., Cai, J., Zhu, C., and Mei, C. (2025). Adaptive Prescribed Performance Control for Flexible-Joint Robotic Manipulators with Unknown Deadzone and Actuator Faults. Electronics, 14.10.3390/electronics14101917 ↗
17. Adaptive Neural Network Anti-Disturbance Control of a Cable-Driven Flexible-Joint-Based Underwater Vehicle Manipulator System with Dead-Zone Input Nonlinearities via Multiple Neural Observers10.1007/s44295-023-00014-z ↗
18. Adaptive Inverse Compensation Fault-Tolerant Control for a Flexible Manipulator with Unknown Dead-Zone and Actuator Faults10.1109/TIE.2023.3239926 ↗
19. An Input–Output Feedback Linearization Approach to the Motion Control of Flexible Joint Manipulators
20. From Simulation to Experimentable Digital Twins: Streamlining Simulation-Based Systems Engineering for Industry 4.010.1109/TII.2018.2804917 ↗
21. Review of Digital Twin about Concepts, Technologies, and Industrial Applications10.1016/j.jmsy.2020.06.017 ↗
22. Developing and Leveraging Digital Twins in Engineering Design10.1016/j.cirp.2025.05.002 ↗
23. Huang, Z., Shen, Y., Li, J., Fey, M., and Brecher, C. (2021). A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics. Sensors, 21.10.3390/s21196340 ↗
24. Kuts, V., Marvel, J.A., Aksu, M., Pizzagalli, S.L., Sarkans, M., Bondarenko, Y., and Otto, T. (2022). Digital Twin as Industrial Robots Manipulation Validation Tool: A VR User Interface. Robotics, 11.10.3390/robotics11050113 ↗
25. Digital Twin for Human–Robot Collaboration Enhancement in Manufacturing Systems10.1016/j.cie.2023.109764 ↗
26. Modelica Association (2014). Functional Mock-up Interface (FMI) Standard—Specification Version 2.0, Modelica Association.
27. An FMI-Based Co-Simulation Framework for a Hybrid Wave Energy Converter with Digital Twin Integration10.1016/j.enconman.2024.119220 ↗
28. Zhao, X., Wang, M., Liu, N., and Tang, Y. (2017, January 25–26). Trajectory Planning for 6-DOF Robotic Arm Based on Quintic Polynomial. Proceedings of the 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017), Sanya, China.10.2991/caai-17.2017.23 ↗
29. A 6-DOF Robot Time-Optimal Trajectory Planning Based on an Improved Genetic Algorithm10.1186/s40638-018-0085-7 ↗
30. Goubej, M., Königsmarková, J., Kampinga, R., Nieuwenkamp, J., and Paquay, S. (2021). Employing Finite Element Analysis and Robust Control Concepts in Mechatronic System Design-Flexible Manipulator Case Study. Appl. Sci., 11.10.3390/app11083689 ↗
31. Caiza, J., and Sanz, J. (2024). ISO 23247-Based Digital Twin Architecture for Manufacturing Systems. Appl. Sci., 14.10.3390/app14104204 ↗
32. Abir, G., Atal, A.K., Bassem, H., and Peter, P. (2022). Digital Twin for Human–Robot Interactions by Means of Industry 4.0 Enabling Technologies. Sensors, 22.10.3390/s22134950 ↗
33. Spong, M.W., Hutchinson, S., and Vidyasagar, M. (2006). Robot Modeling and Control, Wiley.
34. Ioannou, P., and Sun, J. (2012). Robust Adaptive Control, Dover Publications.
35. Digital Twin Framework for Human–Robot Collaboration10.3390/s21248266 ↗