Robust MPC-based Abstraction for Motion Planning of a Mobile Robot

Firouzmand, Elnaz; Talebi, Heidar Ali; Abdollahi, Farzaneh

doi:10.1109/icrom54204.2021.9663460

Cited by 1 publication

(2 citation statements)

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“…In this regard, diverse approaches are used to design a robust MPC. Accordingly, all the methods that have been utilized to robust the MPC, namely, min-max approach [15,16], output feedback robust MPC [17,18], and tube-based MPC [14,19], are costly to compute, although many measures have been adopted to decrease their burden. For instance, the scholars in [20] utilized adaptive tubes and computed new invariant sets online, whereas they decreased the conservativeness, and the online computation burden increased significantly.…”

Section: Introductionmentioning

confidence: 99%

“…Robust MPC strategies can be used to provide robustness against external disturbances and uncertainties. For instance, the robust MPC was exploited in [14] as a novel robust abstraction strategy to solve the motion planning problem of a constrained mobile robot against external disturbances while ensuring planning goals formulated with linear temporal logic and avoiding obstacles. It is worth noting that the main challenge for these strategies is to ensure their stability and feasibility.…”

Section: Introductionmentioning

confidence: 99%

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Robust Laguerre‐based model predictive control for trajectory tracking of a mobile robot using an linear matrix inequality (LMI)‐based approach

Jamalabadi,

Firouzmand,

Sharifi

et al. 2023

Asian Journal of Control

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This paper studies the trajectory tracking of a constrained mobile robot under slippery conditions. The goal is to propose a controller for real‐time operations of time‐varying dynamics with insignificant execution time. Therefore, a Laguerre‐based model predictive control (LMPC) is designed, and robustness is provided with an linear matrix inequality (LMI)‐feedback controller. Moreover, a recursive least square (RLS) algorithm with a forgetting factor is utilized to identify the required parameters of the LMI‐based controller. LMPC and LMI‐based controller stability is achieved with suboptimal theory and Lyapunov function, respectively. This algorithm separates the nominal system and introduces new dynamics containing uncertainties. Furthermore, LMPC is removed from the online calculation, which dramatically reduces computation burden and time; consequently, online computation is dedicated to determining the LMI feedback. Simulations are provided to compare the proposed robust controller to its not robust counterpart. Finally, it is demonstrated that this controller diminishes the execution time considerably, making it incomparable to previous robust MPC strategies.

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