Dynamic Obstacle Avoidance Using Nonlinear Model Predictive Control and Control Barrier Function for Ballbot Systems
Main Article Content
Abstract
This research presents a tracking control system for a ballbot designed to operate in complex environments filled with both static and dynamic obstacles. The Nonlinear Model Predictive Control (NMPC) framework is formulated to predict the future positions of the ballbot and all surrounding obstacles. This predictive capability is crucial for effective navigation, as it allows the ballbot to anticipate potential collisions in the prediction horizon. The NMPC is integrated with an optimization problem that is enhanced by Control Barrier Function (CBF) constraints. These constraints ensure that the ballbot maintains a safe and consistent distance from every obstacle, thus preventing collisions. Additionally, an Extended State Observer (ESO) is implemented to observe and compensate for uncertain disturbances in the ballbot’s movements, as well as to estimate immeasurable variables that might affect its performance. Various simulation scenarios are conducted to thoroughly test and validate the effectiveness of this approach in achieving precise tracking control and reliable collision avoidance in environments with a large number of obstacles.
Keywords
Ballbot, control barrier function, model predictive control, obstacles avoidance
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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[4] P. Fankhauser and C. Gwerder, Modeling and Control of a Ballbot, Bachelor Thesis, Swiss Federal Institute of Technology, Zürich, Switzerland, 2010.
[5] D. C. Vu, T. H. Nguyen Thi, D. D. Vu, T. L. Nguyen, and D. H. Nguyen, H-infinity full-state feedback control for a ball-balancing robot, in Hanoi Industrial University Journal of Science and Technology, vol. 59, no. 2A, pp. 96–100, Mar. 2023. https://doi.org/10.57001/huih5804.2023.047
[6] D. C. Vu, T. H. N. Thi, D. D. Vu, V. P. Pham, D. H. Nguyen, and T. L. Nguyen, H-infinity Optimal fullstate feedback control for a ball-balancing robot, in The International Conference on Intelligent Systems & Networks, Agu. 2023, pp. 326–334. https://doi.org/10.1007/978-981-99-4725-6_41
[7] D. B. Pham, H. Kim, J. Kim, and S.-G. Lee, Balancing and transferring control of a ball segway using a double-loop approach [applications of control], IEEE Control Systems Magazine, vol. 38, no. 2, pp. 15–37, Apr. 2018. https://doi.org/10.1109/MCS.2017.2786444
[8] M. D. Pham et al., Auto-balancing ballbot systems: A fractional-order sliding mode based radial-basis neural network approach, in Proceedings of the International Conference on Engineering Research and Applications, ICERA 2022, 2022, pp. 270–280. https://doi.org/10.1007/978-3-031-22200-9_29
[9] C.-W. Liao, C.-C. Tsai, Y. Y. Li, and C.-K. Chan, Dynamic modeling and sliding-mode control of a ball robot with inverse mouse-ball drive, in 2008 SICE Annual Conference, Chofu, Japan, Oct. 2008, pp. 2951–2955. https://doi.org/10.1109/SICE.2008.4655168
[10] M. D. Pham, D. C. Vu, T. T. H. Nguyen, T. V. A. Nguyen, D. D. Vu, and T. L. Nguyen, Nonlinear model predictive control for ballbot systems: A benchmark with hierarchical sliding mode and linear quadratic controls, in 2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, Ha Noi, Vietnam, Nov. 2023, pp. 411–416. https://doi.org/10.1109/ICCAIS59597.2023.10382349
[11] A. D. Ames, J. W. Grizzle, and P. Tabuada, Control barrier function based quadratic programs with application to adaptive cruise control, in 53rd IEEE Conference on Decision and Control, Los Angeles, CA, USA, Dec. 2014, pp. 6271-6278. https://doi.org/10.1109/CDC.2014.7040372
[12] A. D. Ames, K. Galloway, K. Sreenath, and J. W. Grizzle, Rapidly exponentially stabilizing control Lyapunov functions and hybrid zero dynamics, IEEE, Transactions on Automatic Control, vol. 59, iss. 4, Apr. 2014, pp. 876–891. https://doi.org/10.1109/TAC.2014.2299335
[13] A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, Control barrier functions: Theory and applications, in 2019 18th European Control Conference (ECC), Naples, Italy, Jun. 2019, pp. 3420-3431. https://doi.org/10.23919/ECC.2019.8796030
[14] J. Han, A class of extended state observers for uncertain systems, Control and Decision, vol. 10, no. 1, pp. 85-88, 1995.
[15] M. Ran, J. Li, and L. Xie, A new extended state observer for uncertain nonlinear systems, Automatica, vol. 131, pp. 109772, 2021. https://doi.org/10.1016/j.automatica.2021.109772