Implementation of PID Controller with PSO Tuning for Autonomous Vehicle

Azar, Ahmad Taher; Ammar, Hossam Hassan; Ibrahim, Zahra Fathy; Ibrahim, Habiba; Mohamed, Nada Ali; Taha, Mazen Ahmed

doi:10.1007/978-3-030-31129-2_27

Cited by 28 publications

(16 citation statements)

References 16 publications

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“…The tuning of A, B and C coefficients is effected automatically [46][47][48] or manually [49] during sea trials. They pertain, respectively, to the amplification of proportional, integral and derivative terms.…”

Section: Xte Parameter Of the Usvmentioning

confidence: 99%

Assessment of the Steering Precision of a Hydrographic USV along Sounding Profiles Using a High-Precision GNSS RTK Receiver Supported Autopilot

Marchel

Specht

2020

Energies

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Unmanned Surface Vehicles (USV) are increasingly used to perform numerous tasks connected with measurements in inland waters and seas. One of such target applications is hydrography, where traditional (manned) bathymetric measurements are increasingly often realized by unmanned surface vehicles. This pertains especially to restricted or hardly navigable waters, in which execution of hydrographic surveys with the use of USVs requires precise maneuvering. Bathymetric measurements should be realized in a way that makes it possible to determine the waterbody’s depth as precisely as possible, and this requires high-precision in navigating along planned sounding profiles. This paper presents research that aimed to determine the accuracy of unmanned surface vehicle steering in autonomous mode (with a Proportional-Integral-Derivative (PID) controller) along planned hydrographic profiles. During the measurements, a high-precision Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) positioning system based on a GNSS reference station network (positioning accuracy: 1–2 cm, p = 0.95) and a magnetic compass with the stability of course maintenance of 1°–3° Root Mean Square (RMS) were used. For the purpose of evaluating the accuracy of the vessel’s path following along sounding profiles, the cross track error (XTE) measure, i.e., the distance between an USV’s position and the hydrographic profile, calculated transversely to the course, was proposed. The tests were compared with earlier measurements taken by other unmanned surface vehicles, which followed the exact same profiles with the use of much simpler and low-cost multi-GNSS receiver (positioning accuracy: 2–2.5 m or better, p = 0.50), supported with a Fluxgate magnetic compass with a high course measurement accuracy of 0.3° (p = 0.50 at 30 m/s). The research has shown that despite the considerable difference in the positioning accuracy of both devices and incomparably different costs of both solutions, the authors proved that the use of the GNSS RTK positioning system, as opposed to a multi-GNSS system supported with a Fluxgate magnetic compass, influences the precision of USV following sounding profiles to an insignificant extent.

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Section: Xte Parameter Of the Usvmentioning

confidence: 99%

Assessment of the Steering Precision of a Hydrographic USV along Sounding Profiles Using a High-Precision GNSS RTK Receiver Supported Autopilot

Marchel

Specht

2020

Energies

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“…That being said, we would like to point out that some of the optimization methods that we cover in this paper are also utilized in security-related aspects in autonomous vehicle implementations. Some examples include: [84], where the authors implement Reinforcement Learning (RL) to maximize the robustness of UAV dynamics control to cyber-physical attacks, and [85], where the authors implement a Proportional-Integral Derivative (PID) controller using PSO as a tuning method to achieve high stability.…”

Section: High-speed Scenarios Based On Leo Satellites For Future Auto...mentioning

confidence: 99%

Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation

Çelikbilek

Saleem

Ferre

et al. 2022

Sensors

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Future autonomous transportation is one of the most demanding application areas in terms of connectivity, as it has to simultaneously meet stringent criteria that do not typically go hand in hand, such as high throughput, low latency, high coverage/availability, high positioning and sensing accuracies, high security and robustness to interferences, etc. In order to meet the future demands of challenging applications, such as applications relying on autonomous vehicles, terrestrial networks are no longer sufficient and are to be augmented in the future with satellite-based networks. Among the emerging satellite networks, Low Earth Orbit (LEO) networks are able to provide advantages over traditional Medium Earth Orbit (MEO) and Geo-Stationary Earth Orbit (GEO) networks in terms of signal latency, cost, and performance. Nevertheless, several challenges exist in LEO system design, which have not been fully addressed in the existing literature. In particular, the problem of LEO-system optimization of design parameters is a multi-dimensional problem with many aspects to be considered. This paper offers a comprehensive survey of the LEO-system design parameters, of the challenges in LEO system design process, and of the optimization methods for satellite communication, positioning, and sensing applications, as well as a summarizing discussion on the design considerations for LEO-based networks to support future autonomous transportation.

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“…been tuned using PSO in [110] which helps to control the linear as well as the angular motion of the car by selecting the most appropriate values of the PID controller. In [109], the authors have proposed a model-free derivative filter PID and tuned its control parameters using PSO.…”

Section: Kinematics and Dynamic Parameters Simulationmentioning

confidence: 99%

“…References [86][87][88][89][90] have utilized the genetic algorithms to optimize the kinematical and dynamic model of the steering controller of the vehicle. Besides, references [22,[103][104][105][106][107][108][109][110][111][112][113][114][115][116] have used swarm optimization techniques to optimize the parameters of the steering control methods. However, the genetic algorithm-based optimization techniques cannot be utilized in the realtime steering control system because of their complex structure which raises latency issues in the dynamic road conditions.…”

Section: Lack Of Validation Techniques For the Optimizationmentioning

confidence: 99%

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Rasib

Butt

Khalid

et al. 2021

Mathematical Problems in Engineering

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In the last couple of years, academia-industry collaboration has demonstrated rapid advancements in the development of self-driving vehicles. Since it is evident that self-driving vehicles are going to reshape the traditional transportation systems in near future through enhancement in safe and smart mobility, motion control in self-driving vehicles while performing driving tasks in a dynamic road environment is still a challenging task. In this regard, we present a comprehensive study considering the evolution of steering control methods for self-driving vehicles. Initially, we discussed an insight into the traditional steering systems of the vehicles. To the best of our knowledge, currently, there is no taxonomy available, which elaborates steering control methods for self-driving vehicles. In this paper, we present a novel taxonomy including different steering control methods which are categorized into deterministic and heuristic steering control methods. Concurrently, the abovementioned techniques are critically reviewed elaborating their strengths and limitations. Based on the analysis, key challenges/research gaps in existing steering control methods along with the possible solutions have been briefly discussed to improve the effectiveness of the steering system of self-driving vehicles.

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Implementation of PID Controller with PSO Tuning for Autonomous Vehicle

Cited by 28 publications

References 16 publications

Assessment of the Steering Precision of a Hydrographic USV along Sounding Profiles Using a High-Precision GNSS RTK Receiver Supported Autopilot

Assessment of the Steering Precision of a Hydrographic USV along Sounding Profiles Using a High-Precision GNSS RTK Receiver Supported Autopilot

Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

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