Adaptive tracking control of a nonholonomic wheeled mobile robot with multiple disturbances and input constraints
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Zhonghao Zhang
Zhonghao Zhang is working toward an M.S. degree in Control Science and Engineering at the Institute For Future, School Of Automation, Qingdao University, Qingdao, China. His research interests include adaptive control and wheeled mobile robots.
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Xiaorui Liu
Xiaorui Liu is an Assistant Professor at the Institute For Future, School Of Automation, Qingdao University, Qingdao, China. Hisresearch interests include robotics, EMC measurement technology, human-machine interaction, and embedded systems.
Wanyue Jiang is an Assistant Professor at the Institute For Future, School Of Automation, Qingdao University, Qingdao, China. Herresearch interests include robotics, human-robot interaction, robot navigation and control, and control theory and applications.
Abstract
In this paper, an adaptive trajectory-tracking control scheme is proposed for a nonholonomic wheeled mobile robot (NMR) subjected to skidding, slipping, unknown external disturbance, model uncertainties, and input saturation constraints. The changeable unknown external disturbances of the system which are hard to get, and input saturation constraints should be taken into account for safety in reality. An auxiliary system is presented for analyzing the influence of input saturation constraints, and the state of the auxiliary system is integrated into the adaptive controller. In addition, to handle the uncertainty of parameters, the radial basis function neural network (RBFNN) is proposed to deal with the unknown external disturbances of the robot dynamics. Considering slippery conditions, the skidding of the wheels and the slipping of the robots may lead to system instability. The influences of skidding, slipping, and model uncertainties are considered to be a special type of disturbance. Subsequently, a nonlinear disturbance observer is presented and integrated into the adaptive controller. Afterward based on the proposed control techniques, the stability of the robot system is proved through Lyapunov synthesis. Lastly, simulation experiments are carried on to testify to the effectiveness of the adaptive control scheme.
Zusammenfassung
In diesem Beitrag wird eine adaptive Bahnverfolgungssteuerung für einen nichtholonomischen mobilen Roboter auf Rädern (NMR) vorgeschlagen, der dem Schleudern, Rutschen, unbekannten externen Störungen, Modellunsicherheiten und Eingangssättigungsbeschränkungen ausgesetzt ist. Die veränderlichen unbekannten externen Störungen des Systems, die schwer zu erfassen sind, und die Eingangssättigungsbeschränkungen sollten für die Sicherheit in der Realität berücksichtigt werden. Ein Hilfssystem wird vorgestellt, um den Einfluss der Eingangssättigungsbeschränkungen zu analysieren, und der Zustand des Hilfssystems wird in den adaptiven Regler integriert. Darüber hinaus wird ein neuronales Netz mit radialer Basisfunktion (RBFNN) vorgeschlagen, um mit den unbekannten externen Störungen der Roboterdynamik umzugehen und die Unsicherheit der Parameter zu bewältigen. Bei rutschigen Bedingungen können das Schleudern der Räder und das Rutschen des Roboters zu einer Instabilität des Systems führen. Die Einflüsse von Schleudern, Rutschen und Modellunsicherheiten werden als eine besondere Art von Störung betrachtet. Anschließend wird ein nichtlinearer Störungsbeobachter vorgestellt und in den adaptiven Regler integriert. Anschließend wird auf der Grundlage der vorgeschlagenen Steuerungstechniken die Stabilität des Robotersystems durch die Lyapunov-Synthese nachgewiesen. Schließlich werden Simulationsexperimente durchgeführt, um die Effektivität des adaptiven Regelungsschemas zu belegen.
About the authors
Zhonghao Zhang is working toward an M.S. degree in Control Science and Engineering at the Institute For Future, School Of Automation, Qingdao University, Qingdao, China. His research interests include adaptive control and wheeled mobile robots.
Xiaorui Liu is an Assistant Professor at the Institute For Future, School Of Automation, Qingdao University, Qingdao, China. Hisresearch interests include robotics, EMC measurement technology, human-machine interaction, and embedded systems.
Wanyue Jiang is an Assistant Professor at the Institute For Future, School Of Automation, Qingdao University, Qingdao, China. Herresearch interests include robotics, human-robot interaction, robot navigation and control, and control theory and applications.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Competing interests: The authors declare that there are no conflicts of interest regarding the publication of this article.
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Research funding: This work was supported by the National Key Research and Development Project under Grant 2020YFB1313604.
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Data availability: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
References
[1] C. Ding, J. Zhang, Y. Zhang, and Z. Zhang, “Adaptive neural network control of second-order underactuated systems with prescribed performance constraints,” Int. J. Nonlinear Sci. Numer. Simul., vol. 24, no. 1, pp. 81–93, 2023, https://doi.org/10.1515/ijnsns-2020-0141.Search in Google Scholar
[2] A. Roennau, J. Mangler, P. Keller, B. G. Marvin, N. Huegel, and R. Dillmann, “Grasping and retrieving unknown hazardous objects with a mobile manipulator,” at - Automatisierungstechnik, vol. 70, no. 10, pp. 838–849, 2022, https://doi.org/10.1515/auto-2022-0061.Search in Google Scholar
[3] M. Li, R. Jiang, S. S. Ge, and T. H. Lee, “Role playing learning for socially concomitant mobile robot navigation,” CAAI Trans. Intell. Technol., vol. 3, no. 1, pp. 49–58, 2018. https://doi.org/10.1049/trit.2018.0008.Search in Google Scholar
[4] P. Welz, M. Fischer, and J. Lunze, “Experimentelle Erprobung einer kollisionsfreien Abstandsregelung für mobile Roboter,” at - Automatisierungstechnik, vol. 68, no. 1, pp. 32–43, 2020, https://doi.org/10.1515/auto-2019-0068.Search in Google Scholar
[5] X. Liu, X. Li, H. Su, Y. Zhao, and S. S. Ge, “The opening workspace control strategy of a novel manipulator-driven emission source microscopy system,” ISA Trans., vol. 134, pp. 573–587, 2023, https://doi.org/10.1016/j.isatra.2022.09.002.Search in Google Scholar PubMed
[6] X. Liu, W. Jiang, H. Su, W. Qi, and S. S. Ge, “A control strategy of robot eye-head coordinated gaze behavior achieved for minimized neural transmission noise,” IEEE/ASME Trans. Mechatron., vol. 28, no. 2, pp. 956–966, 2023, https://doi.org/10.1109/tmech.2022.3210592.Search in Google Scholar
[7] Y. Wang, M. Shan, Y. Yue, and D. Wang, “Vision-based flexible leader–follower formation tracking of multiple nonholonomic mobile robots in unknown obstacle environments,” IEEE Trans. Control Syst. Technol., vol. 28, no. 3, pp. 1025–1033, 2020, https://doi.org/10.1109/tcst.2019.2892031.Search in Google Scholar
[8] J. Qin, S. Wang, Y. Kang, and Q. Liu, “Circular Formation algorithms for multiple nonholonomic mobile robots: an optimization-based approach,” IEEE Trans. Ind. Electron., vol. 66, no. 5, pp. 3693–3701, 2019, https://doi.org/10.1109/tie.2018.2864721.Search in Google Scholar
[9] R. Dhaouadi and A. A. Hatab, “Dynamic modelling of differential-drive mobile robots using Lagrange and Newton-euler methodologies: a unified framework,” Adv. Robot. Autom., vol. 2, no. 2, pp. 1–7, 2013. https://doi.org/10.4172/2168-9695.1000107.Search in Google Scholar
[10] V. Sankaranarayanan and A. D. Mahindrakar, “Configuration constrained stabilization of a wheeled mobile robot— theory and experiment,” IEEE Trans. Control Syst. Technol., vol. 21, no. 1, pp. 275–280, 2013, https://doi.org/10.1109/tcst.2011.2181378.Search in Google Scholar
[11] Y. Zhang, W. Li, and Z. Zhang, “Physical-limits-constrained minimum velocity norm coordinating scheme for wheeled mobile redundant manipulators,” Robotica, vol. 33, no. 6, pp. 1325–1350, 2015, https://doi.org/10.1017/s0263574714000757.Search in Google Scholar
[12] W. Li, C. Yang, Y. Jiang, X. Liu, and C. Su, “Motion planning for omnidirectional wheeled mobile robot by potential field method,” J. Adv. Transport., vol. 3, pp. 1–11, 2017, https://doi.org/10.1155/2017/4961383.Search in Google Scholar
[13]] W. Jiang, D. Li, and S. S. Ge, “Micro flapping-wing vehicles formation control with attitude estimation,” IEEE Trans. Cybern., vol. 52, no. 2, pp. 1035–1047, 2022, https://doi.org/10.1109/tcyb.2020.2988911.Search in Google Scholar
[14] D. Li, H. Yu, K. P. Tee, Y. Wu, S. S. Ge, and T. H. Lee, “On time-synchronized stability and control,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 4, pp. 2450–2463, 2022, https://doi.org/10.1109/tsmc.2021.3050183.Search in Google Scholar
[15] S. Wang, “Asymptotic tracking control for nonaffine systems with disturbances,” IEEE Trans. Circuits Syst. II: Express Br., vol. 69, no. 2, pp. 479–483, 2022, https://doi.org/10.1109/tcsii.2021.3080524.Search in Google Scholar
[16] W. Jiang, S. S. Ge, and D. Li, “Fixed-time-synchronized control: a system-dimension-categorized approach,” Sci. China Inf. Sci., vol. 66, no. 7, p. 172203, 2023, https://doi.org/10.1007/s11432-022-3649-1.Search in Google Scholar
[17] Y. Zhang, J. Sun, H. Liang, and H. Li, “Event-triggered adaptive tracking control for multiagent systems with unknown disturbances,” IEEE Trans. Cybern., vol. 50, no. 3, pp. 890–901, 2020, https://doi.org/10.1109/tcyb.2018.2869084.Search in Google Scholar
[18] W. He, H. Huang, and S. S. Ge, “Adaptive neural network control of a robotic manipulator with time-varying output constraints,” IEEE Trans. Cybern., vol. 47, no. 10, pp. 3136–3147, 2017, https://doi.org/10.1109/tcyb.2017.2711961.Search in Google Scholar
[19] M. Chen, P. Shi, and C. Lim, “Robust constrained control for MIMO nonlinear systems based on disturbance observer,” IEEE Trans. Automat. Control, vol. 60, no. 12, pp. 3281–3286, 2015, https://doi.org/10.1109/tac.2015.2450891.Search in Google Scholar
[20] M. Boukattaya, M. Jallouli, and T. Damak, “On trajectory tracking control for nonholonomic mobile manipulators with dynamic uncertainties and external torque disturbances,” Robot. Autonom. Syst., vol. 60, no. 12, pp. 1640–1647, 2012, https://doi.org/10.1016/j.robot.2012.09.022.Search in Google Scholar
[21] W. Gao and R. R. Selmic, “Neural network control of a class of nonlinear systems with actuator saturation,” IEEE Trans. Neural Networks, vol. 17, no. 1, pp. 147–156, 2006, https://doi.org/10.1109/tnn.2005.863416.Search in Google Scholar
[22] J. Huang, C. Wen, W. Wang, and Z. Jiang, “Adaptive stabilization and tracking control of a nonholonomic mobile robot with input saturation and disturbance,” Syst. Control. Lett., vol. 62, no. 3, pp. 234–241, 2013, https://doi.org/10.1016/j.sysconle.2012.11.020.Search in Google Scholar
[23] J. Li, “Adaptive tracking and stabilization of nonholonomic mobile robots with input saturation,” IEEE Trans. Automat. Control, vol. 67, pp. 6173–6179, 2021, https://doi.org/10.1109/tac.2021.3129158.Search in Google Scholar
[24] Y. Chen, Z. Li, H. Kong, and F. Ke, “Model predictive tracking control of nonholonomic mobile robots with coupled input constraints and unknown dynamics,” IEEE Trans. Ind. Inf., vol. 15, no. 6, pp. 3196–3205, 2019, https://doi.org/10.1109/tii.2018.2874182.Search in Google Scholar
[25] Z. Chen, Y. Liu, W. He, H. Qiao, and H. Ji, “Adaptive-neural-network-based trajectory tracking control for a nonholonomic wheeled mobile robot with velocity constraints,” IEEE Trans. Ind. Electron., vol. 68, no. 6, pp. 5057–5067, 2021, https://doi.org/10.1109/tie.2020.2989711.Search in Google Scholar
[26] H. Kang, Y. Kim, C. Hyun, and M. Park, “Generalized extended state observer approach to robust tracking control for wheeled mobile robot with skidding and slipping,” Int. J. Adv. Rob. Syst., vol. 10, no. 3, p. 155, 2013, https://doi.org/10.5772/55738.Search in Google Scholar
[27] M. Chen, “Disturbance attenuation tracking control for wheeled mobile robots with skidding and slipping,” IEEE Trans. Ind. Electron., vol. 64, no. 4, pp. 3359–3368, 2017, https://doi.org/10.1109/tie.2016.2613839.Search in Google Scholar
[28] S. S. Ge, T. H. Lee, and C. J. Harris, Adaptive Neural Network Control of Robotic Manipulators, vol. 19, River Edge, NJ, United States, World Scientific Publishing, 1998, p. 396.Search in Google Scholar
[29] R. Cui, C. Yang, Y. Li, and S. Sharma, “Adaptive neural network control of AUVs with control input nonlinearities using reinforcement learning,” IEEE Trans. Syst. Man Cybern. Syst., vol. 47, no. 6, pp. 1019–1029, 2017, https://doi.org/10.1109/tsmc.2016.2645699.Search in Google Scholar
[30] M. Chen, S. S. Ge, and B. Ren, “Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints,” Automatica, vol. 47, no. 2, pp. 452–465, 2011, https://doi.org/10.1016/j.automatica.2011.01.025.Search in Google Scholar
[31] S. S. Ge, C. C. Hang, T. H. Lee, and T. Zhang, Stable Adaptive Neural Network Control, vol. 13, Boston, MA, USA, Springer, 2002, pp. 139–182.Search in Google Scholar
[32] S. S. Ge and C. Wang, “Direct adaptive NN control of a class of nonlinear systems,” IEEE Trans. Neural Networks, vol. 13, no. 1, pp. 214–221, 2002, https://doi.org/10.1109/72.977306.Search in Google Scholar PubMed
[33] B. Xu, C. Yang, and Y. Pan, “Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle,” IEEE Transact. Neural Networks Learn. Syst., vol. 26, no. 10, pp. 2563–2575, 2015, https://doi.org/10.1109/tnnls.2015.2456972.Search in Google Scholar
[34] Y. Kanayama, Y. Kimura, F. Miyazaki, and T. Noguchi, “A stable tracking control method for a non-holonomic mobile robot,” in Proceedings IROS ’91:IEEE/RSJ International Workshop on Intelligent Robots and Systems ’91, vol. 3, 1991, pp. 1236–1241.10.1109/IROS.1991.174669Search in Google Scholar
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Articles in the same Issue
- Frontmatter
- Editorial
- Vorwort 2024
- Survey
- Unmanned vehicles on the rise: a review on projects of cooperating robot teams
- Methods
- Quantum computer-aided job scheduling for storage and retrieval systems
- On computation of Floquet-transformations and their applicability to controller synthesis for electrical machines
- Adaptive tracking control of a nonholonomic wheeled mobile robot with multiple disturbances and input constraints
- Fault detection in automated production systems based on a long short-term memory autoencoder
- Applications
- SysML’ – incorporating component properties in early design phases of automated production systems
- Persönliches
- Prof. Dr.-Ing. habil. Hartmut Janocha zum 80. Geburtstag
Articles in the same Issue
- Frontmatter
- Editorial
- Vorwort 2024
- Survey
- Unmanned vehicles on the rise: a review on projects of cooperating robot teams
- Methods
- Quantum computer-aided job scheduling for storage and retrieval systems
- On computation of Floquet-transformations and their applicability to controller synthesis for electrical machines
- Adaptive tracking control of a nonholonomic wheeled mobile robot with multiple disturbances and input constraints
- Fault detection in automated production systems based on a long short-term memory autoencoder
- Applications
- SysML’ – incorporating component properties in early design phases of automated production systems
- Persönliches
- Prof. Dr.-Ing. habil. Hartmut Janocha zum 80. Geburtstag
