Design and Development of a Terrain Adaptive Mobile Robot

Shit, Sahadeb; Das, Dibyendu; Ray, Dip Narayan

doi:10.1109/icccnt51525.2021.9580075

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…However, these control strategies effectively handle self-propelled robots in ideal working conditions without external disturbances. Some studies [12]- [15] have introduced uncertainty parameters into the dynamic equations of autonomous robots, leading to the utilization of hybrid Bulletin of Electr Eng & Inf ISSN: 2302-9285  adaptive control methods that merge adaptive control with neural network approximation of unknown components [16]. Another research direction combines adaptive management with fuzzy logic control [17].…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

Ha,

Thi Thuong,

Ngoc Truc

2024

Bulletin EEI

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This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.

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