Phase-Locked Loop Methods and PID Control

Crowe, J.; Johnson, Michael

doi:10.1007/1-84628-148-2_7

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…The proposed time synchronization protocol has first been implemented in Python for simulation and optimization purposes. Thereafter, in order to test the proposed protocol in an actual setup and integrate it into the ReMoni cloud solution for remote monitoring 4 , it has been implemented in C language. To enable comparison, the naive synchronization protocol has also been implemented in Python.…”

Section: Implementation and Experimental Resultsmentioning

confidence: 99%

“…This will reduce drastically the number of packets to communicate between sensors and master node, which means longer Standby phase and higher energy savings for the sensors. Following such a behavior, the sensor can be seen as a phaselocked PI controller [4]. Figure 5 depicts the overall behavior of the sensor with respect to drifting estimation and correction.…”

Section: Time Synchronization Dynamicsmentioning

confidence: 99%

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Intelligent Time Synchronization Protocol for Energy Efficient Sensor Systems

Boudjadar

Beck²

2021

Lecture Notes in Networks and Systems

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The ubiquity of sensors has offered great opportunities to monitoring infrastructures to track the real-time state of observed environments. Monitoring systems rely on synchronous massive data to calculate the environment state. However, collecting and communicating massive data usually leads to expensive energy consumption due to high sampling frequency and communication cost for the sensors operation. Clamp-on wireless sensors are a flexible plug-and-play technology easy to deploy where sensors operate on integrated batteries. This paper introduces a highly accurate time synchronization protocol to coordinate the operation of individual clamp-on sensors deployed as an energyconstrained monitoring solution. While maintaining sensors highly synchronized, the proposed protocol reduces drastically data communication and energy consumption of the sensor network. To minimize the energy consumption further, we optimize the protocol behavior using a genetic algorithm. The protocol has been modeled in VDM for formal analysis, implemented and compared to a state of the art protocol.

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Section: Implementation and Experimental Resultsmentioning

confidence: 99%

Section: Time Synchronization Dynamicsmentioning

confidence: 99%

Intelligent Time Synchronization Protocol for Energy Efficient Sensor Systems

Boudjadar

Beck²

2021

Lecture Notes in Networks and Systems

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“…A PID controller cannot do this. The PID controller has proportional, integral and derivative elements as in its name [12]. Fig.…”

Section: Controlmentioning

confidence: 99%

Exploiting Machine Learning for Vision and Motion Planning of Autonomous Vehicles Navigation

Rutendo

Akkad

2021

Intellekt. Sist. Proizv.

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The object of this paper is to create a system that can control any vehicle in any gaming environment to simulate, study, experiment and improve how self-driving vehicles operate. It is to be taken as the bases for future work on autonomous vehicles with real hardware devices. The long-term goal is to eliminate human error. Perception, localisation, planning and control subsystems were developed. LiDAR and RADAR sensors were used in addition to a normal web Camera. After getting information from the perception module, the system will be able to localise where the vehicle is, then the planning module is used to plan to which location the vehicle will move, using localisation module data to draw up the best path to use. After knowing the best path, the system will control the vehicle to move autonomously without human help. As a controller a Proportional Integral Derivative PID controller was used. Python programming language, computer vision, and machine learning were used in developing the system, where the only hardware required is a computer with a GPU and powerful graphical card that can run a game which has a vehicle, roads with lane lines and a map of the road. The developed system is intended to be a good tool in conducting experiments for achieving reliable autonomous vehicle navigation.

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“…The PID control scheme is generally preferred in many industrial and academic applications [ 13 , 14 ]. However, it is difficult to obtain satisfactory control performances in highly nonlinear and time-varying systems for the conventional PID controller due to its linear structure, constant control gains and limited degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

Precision Position Control of a Voice Coil Motor Using Self-Tuning Fractional Order Proportional-Integral-Derivative Control

Chen

Chia

2016

Micromachines

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The object of this study is to develop a self-tuning fractional order proportional-integral-derivative (SFOPID) controller for controlling the mover position of a direct drive linear voice coil motor (VCM) accurately under different operational conditions. The fractional order proportional-integral-derivative (FOPID) controller can improve the control performances of the conventional integer order PID controller with respect to the additional fractional differential and integral orders; however, choosing five interdependent control parameters including proportional, integral, and derivative gains, as well as fractional differential and integral orders appropriately is arduous in practical applications. In this regard, the SFOPID controller is proposed in which the five control parameters are optimized dynamically and concurrently according to an adaptive differential evolution algorithm with a high efficiency adaptive selection mechanism. Experimental results reveal that the SFOPID controller outperforms PID and FOPID controllers with regard to the nonlinear VCM control system under both nominal and payload conditions.

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Phase-Locked Loop Methods and PID Control

Cited by 6 publications

References 9 publications

Intelligent Time Synchronization Protocol for Energy Efficient Sensor Systems

Intelligent Time Synchronization Protocol for Energy Efficient Sensor Systems

Exploiting Machine Learning for Vision and Motion Planning of Autonomous Vehicles Navigation

Precision Position Control of a Voice Coil Motor Using Self-Tuning Fractional Order Proportional-Integral-Derivative Control

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