Lateral control of an autonomous vehicle based on salp swarm algorithm

Elias, Ghaidaa Hadi Salih; Al-Moadhen, Ahmed; Kamil, Haider

doi:10.1063/5.0120403

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…A modified particle swarm optimization is suggested in this research in order to prevent the early convergence phenomenon of PSO and enhance its performance on difficult tasks. One of the PSO parameters, inertia weight (𝜔), can provide particles with the capacity to dynamically adjust to various situations and achieve a balance between exploration and exploitation [16].…”

Section: Pid Related To the Mpso Methods (Pid-mpso)mentioning

confidence: 99%

Controlling a Longitudinal Autonomous Vehicle Using Modified Particle Swarm Optimization

Salih

2023

Preprint

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This paper presents two optimization algorithms for selecting the optimal Proportional Integral Derivative (PID) controller’s coefficients for a longitudinal dynamics vehicle system. The suggested optimization algorithms are the Particle Swarm Optimization (PSO) and Modified PSO (MPSO) algorithms, which use the Integral Absolute value Error (IAE) as a cost function to find the PID controller's coefficients. The optimal proposed controllers are compared in terms of optimized PID controller’s response specifications expressed in standards of maximum Overshoot (OS), Steady-State Error (SSE), Settling Time (ts) (2%), and Rise Time (tr). The obtained results show that a combined PID controller with the MPSO algorithm is more effective than an combined PID controller with the traditional PSO algorithm at controlling the vehicle's longitudinal dynamics system with an enhanced percentage of 2.5%.

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Section: Pid Related To the Mpso Methods (Pid-mpso)mentioning

confidence: 99%

Controlling a Longitudinal Autonomous Vehicle Using Modified Particle Swarm Optimization

Salih

2023

Preprint

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“…Stanley control [10,33,34] Simple computation; strong adaptability; fast response speed; strong path tracking ability; good robustness; real-time and easy-to-implement performance.…”

Section: Controller Type Advantages Disadvantagesmentioning

confidence: 99%

Research on Accurate Motion Trajectory Control Method of Four-Wheel Steering AGV Based on Stanley-PID Control

Fu,

Liu,

Zhang

2023

Sensors

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With the continuous progress and application of robotics technology, the importance of mobile robots capable of adapting to specialized work environments is gaining prominence. Among them, achieving precise and stable control of AGVs (Automated Guided Vehicles) stands as a paramount task propelling the advancement of mobile robotics. Consequently, this study devises a control system that enables AGVs to attain stable and accurate motion through equipment connection and debugging, kinematic modeling of the four-wheel steering AGV, and a selection and comparative analysis of motion control algorithms. The effectiveness of the Stanley-PID control algorithm in guiding the motion of a four-wheel steering AGV is validated through MATLAB 2021a simulation software. The simulation results illustrate the outstanding stability and precise control capabilities of the Stanley-PID algorithm.

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Lateral control of an autonomous vehicle based on salp swarm algorithm

Cited by 2 publications

References 30 publications

Controlling a Longitudinal Autonomous Vehicle Using Modified Particle Swarm Optimization

Controlling a Longitudinal Autonomous Vehicle Using Modified Particle Swarm Optimization

Research on Accurate Motion Trajectory Control Method of Four-Wheel Steering AGV Based on Stanley-PID Control

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