Disturbances Elimination with Fuzzy Sliding Mode Control for Mobile Robot Trajectory Tracking

Benaziza, Walid; Slimane, Noureddine; Mallem, Ali

doi:10.15598/aeee.v16i3.2767

Cited by 2 publications

(3 citation statements)

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“…The kinetic energy of CLMR can be described as: 𝐾 = 𝐾 𝑝 + 𝐾 𝑓𝑤 + 𝐾 𝑟𝑤 (10) where 𝐾 𝑝 , 𝐾 𝑓𝑤 , 𝐾 𝑟𝑤 denote the kinetic energy of the body, front wheels and rear wheels of CLMR. According to Lagrange mechanics, the following matrices are adopted for CLMR models [2].…”

Section: The Dynamic Model Of the Clmrmentioning

confidence: 99%

“…Peng and Shi [16] solved the problem of uncertainties and external disturbances in a non-holonomic wheeled mobile robot, where the uncertainties are approximated by a fuzzy logic system and an adaptive fuzzy integral terminal sliding mode con-troller is used for velocity controller. An adequate control law is presented in Benaziza et al [2]. It is based on Global Terminal Sliding Mode (GTSM) with fuzzy control.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Synergetic Control for Dynamic Car-Like Mobile Robot

Bouhamatou

Abedssemed

2022

Acta Mechanica Et Automatica

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This paper aims to present the dynamic control of a Car-like Mobile Robot (CLMR) using Synergetic Control (SC). The SC control is used to make the linear velocity and steering velocity converge to references. Lyapunov synthesis is adopted to assure controlled system stability. To find the optimised parameters of the SC, the grey wolf optimiser (GWO) algorithm is used. These parameters depend on the best-selected fitness function. Four fitness functions are selected for this purpose, which is based on the integral of the error square (ISE), the integral of the square of the time-weighted error (ITSE), the integral of the error absolute (IAE) and the integral of the absolute of the time-weighted error (TIAE) criterion. To go further in the investigation, fuzzy logic type 2 is used to get at each iteration the appropriate controller parameters that give the best performances and robustness. Simulations results are conducted to show the feasibility and efficiency of the proposed control methods.

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Section: The Dynamic Model Of the Clmrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Synergetic Control for Dynamic Car-Like Mobile Robot

Bouhamatou

Abedssemed

2022

Acta Mechanica Et Automatica

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“…5) Experiments and simulations are conducted to validate the theoretical developments. Both the simulations and experiments are conducted using different initial conditions which have not been done in previous works (see for example [10], [12], [13], [16], [17], [19]- [21], [23], [23], [25]- [33], [35], [36]). The rest of the paper is arranged as follows: In Section 2, the system description, modelling, and problem formulation are given.…”

Section: Introductionmentioning

confidence: 99%

LiDAR Based Trajectory-Tracking of an Autonomous Differential Drive Mobile Robot Using Fuzzy Sliding Mode Controller

Zaman

2022

IEEE Access

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This study aims to achieve the trajectory-tracking of an autonomous differential drive mobile robot (ADDMR) in the presence of friction torques using the proposed Fuzzy Sliding-Mode Control (FSMC). First, the complete model of the ADDMR is established including kinematic, dynamic and actuator models. Then, the desired trajectory is planned and generated via Bézier curve combined with cubic time mapping. The localization of the ADDMR is performed using the Monte Carlo Localization (MCL) technique and LiDAR point cloud data. Subsequently, the proposed FSMC utilizes nonlinear sliding surfaces inferred based on fuzzy logic to asymptotically attain the pose convergence. To investigate the feasibility and robustness of the proposed FSMC, simulations and experiments are conducted under different operation conditions. The results show that the proposed FSMC using LiDAR data in the feedback leads to excellent control performance even in the presence of friction torques. It was also shown through simulations and experiments that the established ADDMR model fits the actual ADDMR model very well.

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Disturbances Elimination with Fuzzy Sliding Mode Control for Mobile Robot Trajectory Tracking

Cited by 2 publications

References 0 publications

Fuzzy Synergetic Control for Dynamic Car-Like Mobile Robot

Fuzzy Synergetic Control for Dynamic Car-Like Mobile Robot

LiDAR Based Trajectory-Tracking of an Autonomous Differential Drive Mobile Robot Using Fuzzy Sliding Mode Controller

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