Successive linearization based model predictive control of variable stiffness actuated robots

Altay Zhakatayev, Bexultan Rakhim, Olzhas Adiyatov, Almaskhan Baimyshev, Huseyin Atakan Varol

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Variable stiffness actuation is a new design paradigm for high performance and energy efficient robots with inherent safety features. Nonlinear model predictive control (NMPC) was employed to control these robots due to its ability to cope with constrained and nonlinear systems. Even though the results for NMPC are promising, one major weakness is the computational cost of this algorithm. It restricts the use of NMPC to low degree of freedom robots with relatively slow dynamics. This problem can be alleviated by finding an approximate linear representation of the system and using less computation hungry traditional model predictive control (MPC). In this work, we present our successive linearization based MPC (SLMPC) framework for variable stiffness actuated (VSA) robots. The system is linearized and discretized at every sampling instant and a quadratic problem is formulated using this discrete-Time linear model. Solution of this quadratic problem provides the control inputs for the control horizon. In order to compare our scheme to NMPC, we conducted experiments with a reaction wheel augmented VSA system. For a 16 s trajectory tracking experiment, the root-mean-square errors were 0.54 and 0.64 degrees for NMPC and SLMPC methods, respectively, whereas the average computation time of the control rule was 2.17 ms for NMPC and 1.25 ms for SLMPC. Halving the computation time without compromising tracking performance shows the potential of our approach as a viable control alternative for VSA robots.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1774-1779
Number of pages6
ISBN (Electronic)9781509059980
DOIs
Publication statusPublished - Aug 21 2017
Event2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017 - Munich, Germany
Duration: Jul 3 2017Jul 7 2017

Conference

Conference2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017
CountryGermany
CityMunich
Period7/3/177/7/17

Fingerprint

Model predictive control
Linearization
Stiffness
Robots
Degrees of freedom (mechanics)
Mean square error
Nonlinear systems
Wheels
Experiments
Trajectories
Sampling

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

Cite this

Zhakatayev, A., Rakhim, B., Adiyatov, O., Baimyshev, A., & Varol, H. A. (2017). Successive linearization based model predictive control of variable stiffness actuated robots. In 2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017 (pp. 1774-1779). [8014275] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2017.8014275

Successive linearization based model predictive control of variable stiffness actuated robots. / Zhakatayev, Altay; Rakhim, Bexultan; Adiyatov, Olzhas; Baimyshev, Almaskhan; Varol, Huseyin Atakan.

2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1774-1779 8014275.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhakatayev, A, Rakhim, B, Adiyatov, O, Baimyshev, A & Varol, HA 2017, Successive linearization based model predictive control of variable stiffness actuated robots. in 2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017., 8014275, Institute of Electrical and Electronics Engineers Inc., pp. 1774-1779, 2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017, Munich, Germany, 7/3/17. https://doi.org/10.1109/AIM.2017.8014275
Zhakatayev A, Rakhim B, Adiyatov O, Baimyshev A, Varol HA. Successive linearization based model predictive control of variable stiffness actuated robots. In 2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1774-1779. 8014275 https://doi.org/10.1109/AIM.2017.8014275
Zhakatayev, Altay ; Rakhim, Bexultan ; Adiyatov, Olzhas ; Baimyshev, Almaskhan ; Varol, Huseyin Atakan. / Successive linearization based model predictive control of variable stiffness actuated robots. 2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1774-1779
@inproceedings{86c39eff3a784206a71c5ed66b5caaf8,
title = "Successive linearization based model predictive control of variable stiffness actuated robots",
abstract = "Variable stiffness actuation is a new design paradigm for high performance and energy efficient robots with inherent safety features. Nonlinear model predictive control (NMPC) was employed to control these robots due to its ability to cope with constrained and nonlinear systems. Even though the results for NMPC are promising, one major weakness is the computational cost of this algorithm. It restricts the use of NMPC to low degree of freedom robots with relatively slow dynamics. This problem can be alleviated by finding an approximate linear representation of the system and using less computation hungry traditional model predictive control (MPC). In this work, we present our successive linearization based MPC (SLMPC) framework for variable stiffness actuated (VSA) robots. The system is linearized and discretized at every sampling instant and a quadratic problem is formulated using this discrete-Time linear model. Solution of this quadratic problem provides the control inputs for the control horizon. In order to compare our scheme to NMPC, we conducted experiments with a reaction wheel augmented VSA system. For a 16 s trajectory tracking experiment, the root-mean-square errors were 0.54 and 0.64 degrees for NMPC and SLMPC methods, respectively, whereas the average computation time of the control rule was 2.17 ms for NMPC and 1.25 ms for SLMPC. Halving the computation time without compromising tracking performance shows the potential of our approach as a viable control alternative for VSA robots.",
author = "Altay Zhakatayev and Bexultan Rakhim and Olzhas Adiyatov and Almaskhan Baimyshev and Varol, {Huseyin Atakan}",
year = "2017",
month = "8",
day = "21",
doi = "10.1109/AIM.2017.8014275",
language = "English",
pages = "1774--1779",
booktitle = "2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Successive linearization based model predictive control of variable stiffness actuated robots

AU - Zhakatayev, Altay

AU - Rakhim, Bexultan

AU - Adiyatov, Olzhas

AU - Baimyshev, Almaskhan

AU - Varol, Huseyin Atakan

PY - 2017/8/21

Y1 - 2017/8/21

N2 - Variable stiffness actuation is a new design paradigm for high performance and energy efficient robots with inherent safety features. Nonlinear model predictive control (NMPC) was employed to control these robots due to its ability to cope with constrained and nonlinear systems. Even though the results for NMPC are promising, one major weakness is the computational cost of this algorithm. It restricts the use of NMPC to low degree of freedom robots with relatively slow dynamics. This problem can be alleviated by finding an approximate linear representation of the system and using less computation hungry traditional model predictive control (MPC). In this work, we present our successive linearization based MPC (SLMPC) framework for variable stiffness actuated (VSA) robots. The system is linearized and discretized at every sampling instant and a quadratic problem is formulated using this discrete-Time linear model. Solution of this quadratic problem provides the control inputs for the control horizon. In order to compare our scheme to NMPC, we conducted experiments with a reaction wheel augmented VSA system. For a 16 s trajectory tracking experiment, the root-mean-square errors were 0.54 and 0.64 degrees for NMPC and SLMPC methods, respectively, whereas the average computation time of the control rule was 2.17 ms for NMPC and 1.25 ms for SLMPC. Halving the computation time without compromising tracking performance shows the potential of our approach as a viable control alternative for VSA robots.

AB - Variable stiffness actuation is a new design paradigm for high performance and energy efficient robots with inherent safety features. Nonlinear model predictive control (NMPC) was employed to control these robots due to its ability to cope with constrained and nonlinear systems. Even though the results for NMPC are promising, one major weakness is the computational cost of this algorithm. It restricts the use of NMPC to low degree of freedom robots with relatively slow dynamics. This problem can be alleviated by finding an approximate linear representation of the system and using less computation hungry traditional model predictive control (MPC). In this work, we present our successive linearization based MPC (SLMPC) framework for variable stiffness actuated (VSA) robots. The system is linearized and discretized at every sampling instant and a quadratic problem is formulated using this discrete-Time linear model. Solution of this quadratic problem provides the control inputs for the control horizon. In order to compare our scheme to NMPC, we conducted experiments with a reaction wheel augmented VSA system. For a 16 s trajectory tracking experiment, the root-mean-square errors were 0.54 and 0.64 degrees for NMPC and SLMPC methods, respectively, whereas the average computation time of the control rule was 2.17 ms for NMPC and 1.25 ms for SLMPC. Halving the computation time without compromising tracking performance shows the potential of our approach as a viable control alternative for VSA robots.

UR - http://www.scopus.com/inward/record.url?scp=85028767155&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028767155&partnerID=8YFLogxK

U2 - 10.1109/AIM.2017.8014275

DO - 10.1109/AIM.2017.8014275

M3 - Conference contribution

AN - SCOPUS:85028767155

SP - 1774

EP - 1779

BT - 2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -