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

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


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.

Titel2021 American Control Conference (ACC)
Anzahl der Seiten8
VerlagIEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)978-1-7281-9704-3
ISBN (elektronisch)978-1-6654-4197-1, 978-1-6654-4198-8
PublikationsstatusErschienen - 25.05.2021
VeranstaltungAmerican Control Conference - ACC 2021 - Virtual, New Orleans, USA / Vereinigte Staaten
Dauer: 25.05.202128.05.2021