Projects per year
Personal profile
Personal profile
Dr Matteo Rubagotti obtained his PhD in Electronics, Computer Science and Electrical Engineering (track in Automatic Control) at the University of Pavia, Pavia, Italy, in 2010. Previously to his post at Nazarbayev University, he was lecturer at the University of Leicester, Leicester, UK, and postdoctoral fellow at the University of Trento, Trento, Italy, and at IMT Institute for Advanced Studies, Lucca, Italy. He also spent visiting periods at the Center for Automotive Research of The Ohio State University, OH, USA, and at the Automatic Control Lab, ETH Zurich, Zurich Switzerland.
Research interests
His research interests include model predictive control, sliding mode control, and the application of control and optimization algorithms to solve research problems in robotics (variable-impedance-actuated robots, parallel manipulators, tensegrity robots, and physical human-robot interaction).
External positions
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Projects
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Stochastic and learning-based predictive control methods for physical human-robot interaction.
Rubagotti, M., Sandygulova, A., Shintemirov, A., Yessenbayev, Z. & Summers, D.
1/1/20 → 12/31/22
Project: Monitored by Research Administration
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Motion Planning and Control of Tensegrity Robots
1/1/20 → 12/31/22
Project: Monitored by Research Administration
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Hardware and Software Based Methods for Safe Human-Robot Interaction with Variable Impedance Robots
Varol, H. A., Rubagotti, M., Zhakatayev, A., Saudabayev, A., Massalin, Y., Adiyatov, O. & Moldagaliyeva, A.
3/20/18 → 12/31/20
Project: Monitored by Research Administration
Research output
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An open-source 7-DOF wireless human arm motion-tracking system for use in robotics research
Shintemirov, A., Taunyazov, T., Omarali, B., Nurbayeva, A., Kim, A., Bukeyev, A. & Rubagotti, M., Jun 1 2020, In: Sensors (Switzerland). 20, 11, 3082.Research output: Contribution to journal › Article › peer-review
Open Access -
Constrained Nonlinear Discrete-Time Sliding Mode Control Based on a Receding Horizon Approach
Rubagotti, M., Incremona, G. P., Raimondo, D. M. & Ferrara, A., 2020, (Accepted/In press) In: IEEE Transactions on Automatic Control.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus) -
Deep learning-based approximate optimal control of a reaction-wheel-actuated spherical inverted pendulum
Baimukashev, D., Sandibay, N., Rakhim, B., Varol, H. A. & Rubagotti, M., Jul 2020, 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020. Institute of Electrical and Electronics Engineers Inc., p. 1322-1328 7 p. 9158920. (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM; vol. 2020-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
1 Citation (Scopus) -
Neural Network Augmented Sensor Fusion for Pose Estimation of Tensegrity Manipulators
Kuzdeuov, A., Rubagotti, M. & Varol, H. A., Apr 1 2020, In: IEEE Sensors Journal. 20, 7, p. 3655-3666 12 p., 8932581.Research output: Contribution to journal › Article › peer-review
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A Discrete-Time Optimization-Based Sliding Mode Control Law for Linear Systems with Input and State Constraints
Rubagotti, M., Paolo Incremona, G. & Ferrara, A., Jan 18 2019, 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., p. 5940-5945 6 p. 8619503. (Proceedings of the IEEE Conference on Decision and Control; vol. 2018-December).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution