Particle Swarm Optimization-based Optimal PID Control of an Agricultural Mobile Robot

Gökçe, Barış; Koca, Yavuz Bahadır; Aslan, Yılmaz; GÖKÇE, Celal Onur

doi:10.7546/crabs.2021.04.12

Cited by 4 publications

(1 citation statement)

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“…In the research of agricultural wheeled robot path tracking, Li et al [76] introduced a radial basis function (RBF) neural network to estimate the uncertainty disturbance in real-time and then designed an adaptive sliding mode control method, which can improve the system robustness while reducing chattering. Gökçe et al [77] used a PID control strategy to control the agricultural robot and optimized the controller parameters through the PSO algorithm. In order to adapt to changes in speed and road, Sun et al [78] proposed an improved path-tracking control algorithm based on the fuzzy Stanley model (FSM) and PSO.…”

Section: Agricultural Robots and Facility Agricultural Machinerymentioning

confidence: 99%

Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges

Wang

et al. 2023

Applied Sciences

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This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle.

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Section: Agricultural Robots and Facility Agricultural Machinerymentioning

confidence: 99%

Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges

Wang

et al. 2023

Applied Sciences

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Swarm Intelligence in Action: Particle Swarm Optimization and Rendezvous Algorithms for Swarm Robotics

Ganduri,

Pathri

2024

Journal of Field Robotics

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Swarm technology is evolving quickly in the new era in the domains of autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs). Swarm technology takes its cues from nature, particularly from the behaviors of ants, bees, schools of fish, and birds. The rapid growth of swarm technologies can be attributed to this organic inspiration. It is utilized in a variety of applications, such as pick‐and‐place robotics and drone swarms for laser shows. To mimic the behavior of swarm robots or microbots, this research work explores the use of swarm algorithms, namely particle swarm optimization (PSO) and rendezvous algorithms (RA). The emphasis will be on applying these algorithms to situations that require swarm robots to navigate around obstacles while planning their paths and forming patterns. This study shows how successful these algorithms are by using MATLAB as a useful tool. The outcomes demonstrate that the algorithms are capable of producing intricate patterns and facilitating effective path planning for swarms of robots. The robots' ability to avoid obstacles and travel around them at a high speed is noteworthy. This study covers a wide range of industries, such as space exploration, military/defense applications, storage, surveillance, and search and rescue. The application of swarm technology demonstrates how it may transform practical situations and provides information about how flexible and efficient swarm algorithms are in a variety of settings. By offering useful insights for researchers, engineers, and practitioners in utilizing the combined potential of swarm intelligence (SI) for a variety of applications, this investigation advances the field of swarm robotics.

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Optimized linear regression control of DC motor under various disturbances

GÖKÇE

2022

Open Chemistry

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In this study, an optimized linear regression controller is proposed for velocity control of DC motor. System is tested under disturbances of different types. Step, sinusoidal and trapezoidal functions are used as reference input. A linear single layer network of weights is used to calculate the necessary armature voltage by giving past measured velocity, past reference and certain amount of future reference as inputs. Training data are generated using Proportional Integral (PI) controller parameters which are optimized using particle swarm optimization (PSO). In this first phase of training, pseudo-inverse solution is used to find the coarse parameters of the network. These parameters give suboptimal results with low performance even lower than that of PSO-optimized PI controller. In the second phase of training, parameters of the network are fine-tuned using PSO algorithm again, this time for optimization of network parameters. Quite encouraging results including up to more than 1,500% increase in performance are obtained and reported.

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Particle Swarm Optimization-based Optimal PID Control of an Agricultural Mobile Robot

Cited by 4 publications

References 0 publications

Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges

Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges

Swarm Intelligence in Action: Particle Swarm Optimization and Rendezvous Algorithms for Swarm Robotics

Optimized linear regression control of DC motor under various disturbances

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