FFTSMC with Optimal Reference Trajectory Generated by MPC in Robust Robotino Motion Planning with Saturating Inputs

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

FFTSMC with Optimal Reference Trajectory Generated by MPC in Robust Robotino Motion Planning with Saturating Inputs. / Hedman, Max; Mercorelli, Paolo.

2021 American Control Conference (ACC). Piscataway : IEEE - Institute of Electrical and Electronics Engineers Inc., 2021. S. 1470-1477 9482876 (Proceedings of the American Control Conference).

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Hedman, M & Mercorelli, P 2021, FFTSMC with Optimal Reference Trajectory Generated by MPC in Robust Robotino Motion Planning with Saturating Inputs. in 2021 American Control Conference (ACC)., 9482876, Proceedings of the American Control Conference, IEEE - Institute of Electrical and Electronics Engineers Inc., Piscataway, S. 1470-1477, American Control Conference - ACC 2021, New Orleans, USA / Vereinigte Staaten, 25.05.21. https://doi.org/10.23919/ACC50511.2021.9482876

APA

Hedman, M., & Mercorelli, P. (2021). FFTSMC with Optimal Reference Trajectory Generated by MPC in Robust Robotino Motion Planning with Saturating Inputs. in 2021 American Control Conference (ACC) (S. 1470-1477). [9482876] (Proceedings of the American Control Conference). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC50511.2021.9482876

Vancouver

Hedman M, Mercorelli P. FFTSMC with Optimal Reference Trajectory Generated by MPC in Robust Robotino Motion Planning with Saturating Inputs. in 2021 American Control Conference (ACC). Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc. 2021. S. 1470-1477. 9482876. (Proceedings of the American Control Conference). https://doi.org/10.23919/ACC50511.2021.9482876

Bibtex

@inbook{1908e2ec44834cc38e2f3ed07898dc6c,
title = "FFTSMC with Optimal Reference Trajectory Generated by MPC in Robust Robotino Motion Planning with Saturating Inputs",
abstract = "Mobile robots are remarkable cases of highly developed technology and systems. The robot community has developed a complex analysis to meet the increased demands of the control challenges pertaining to the movement of robot. An approach using Explicit Model Predictive Control (MPC) in combination with Sliding Mode Control (SMC) in the context of a decoupling controller is proposed. The MPC works in the outer loop of the control and is used to generate the unique optimal reference trajectory. The generated reference resulting from the convex optimisation problem is to be tracked by the SMC. The SMC works in the inner loop of the proposed control strategy to compensate the nonlinearities. MPC is used over the more common PID strategy as it is able to handle saturation with better tracking and error. Implementation of three possible different SMC strategies such as classical SMC, Finite Time Sliding Mode Control (FTSMC), and Fast Finite Time Sliding Mode Control (FFTSMC) using Matlab/Simulink shows promising results even in the presence of external disturbances. In particular, in the case of FFTSMC, the paper exhibits a Proposition and a Theorem. In particular, the Theorem gives sufficient condition to avoid saturating inputs, while in the meantime preserving asymptotic stability. We were able to validate the approach using simulations to compare outcomes and tune to optimal results.",
keywords = "Holonomic mobile robots, Lyapunov approach, Model predictive control, Robotino, saturating inputs, Sliding mode control",
author = "Max Hedman and Paolo Mercorelli",
year = "2021",
month = may,
day = "25",
doi = "10.23919/ACC50511.2021.9482876",
language = "English",
isbn = "978-1-7281-9704-3",
series = "Proceedings of the American Control Conference",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "1470--1477",
booktitle = "2021 American Control Conference (ACC)",
address = "United States",
note = "American Control Conference - ACC 2021 ; Conference date: 25-05-2021 Through 28-05-2021",
url = "https://acc2021.a2c2.org/",

}

RIS

TY - CHAP

T1 - FFTSMC with Optimal Reference Trajectory Generated by MPC in Robust Robotino Motion Planning with Saturating Inputs

AU - Hedman, Max

AU - Mercorelli, Paolo

PY - 2021/5/25

Y1 - 2021/5/25

N2 - Mobile robots are remarkable cases of highly developed technology and systems. The robot community has developed a complex analysis to meet the increased demands of the control challenges pertaining to the movement of robot. An approach using Explicit Model Predictive Control (MPC) in combination with Sliding Mode Control (SMC) in the context of a decoupling controller is proposed. The MPC works in the outer loop of the control and is used to generate the unique optimal reference trajectory. The generated reference resulting from the convex optimisation problem is to be tracked by the SMC. The SMC works in the inner loop of the proposed control strategy to compensate the nonlinearities. MPC is used over the more common PID strategy as it is able to handle saturation with better tracking and error. Implementation of three possible different SMC strategies such as classical SMC, Finite Time Sliding Mode Control (FTSMC), and Fast Finite Time Sliding Mode Control (FFTSMC) using Matlab/Simulink shows promising results even in the presence of external disturbances. In particular, in the case of FFTSMC, the paper exhibits a Proposition and a Theorem. In particular, the Theorem gives sufficient condition to avoid saturating inputs, while in the meantime preserving asymptotic stability. We were able to validate the approach using simulations to compare outcomes and tune to optimal results.

AB - Mobile robots are remarkable cases of highly developed technology and systems. The robot community has developed a complex analysis to meet the increased demands of the control challenges pertaining to the movement of robot. An approach using Explicit Model Predictive Control (MPC) in combination with Sliding Mode Control (SMC) in the context of a decoupling controller is proposed. The MPC works in the outer loop of the control and is used to generate the unique optimal reference trajectory. The generated reference resulting from the convex optimisation problem is to be tracked by the SMC. The SMC works in the inner loop of the proposed control strategy to compensate the nonlinearities. MPC is used over the more common PID strategy as it is able to handle saturation with better tracking and error. Implementation of three possible different SMC strategies such as classical SMC, Finite Time Sliding Mode Control (FTSMC), and Fast Finite Time Sliding Mode Control (FFTSMC) using Matlab/Simulink shows promising results even in the presence of external disturbances. In particular, in the case of FFTSMC, the paper exhibits a Proposition and a Theorem. In particular, the Theorem gives sufficient condition to avoid saturating inputs, while in the meantime preserving asymptotic stability. We were able to validate the approach using simulations to compare outcomes and tune to optimal results.

KW - Holonomic mobile robots

KW - Lyapunov approach

KW - Model predictive control

KW - Robotino

KW - saturating inputs

KW - Sliding mode control

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U2 - 10.23919/ACC50511.2021.9482876

DO - 10.23919/ACC50511.2021.9482876

M3 - Article in conference proceedings

AN - SCOPUS:85111931672

SN - 978-1-7281-9704-3

T3 - Proceedings of the American Control Conference

SP - 1470

EP - 1477

BT - 2021 American Control Conference (ACC)

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

T2 - American Control Conference - ACC 2021

Y2 - 25 May 2021 through 28 May 2021

ER -

DOI