ALICE Connex: A volunteer computing platform for the Time-Of-Flight calibration of the ALICE experiment. An opportunistic use of CPU cycles on Android devices

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Author list: Jenviriyakul P., Chalumporn G., Achalakul T., Costa F., Akkarajitsakul K.

Publisher: Wiley

Publication year: 2019

Journal: Expert Systems: The Journal of Knowledge Engineering (0266-4720)

Volume number: 94

Start page: 510

End page: 523

Number of pages: 14

ISSN: 0266-4720

eISSN: 1468-0394

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075197160&doi=10.1111%2fexsy.12489&partnerID=40&md5=79a10029bb61eb0225abc8f34d305ba5

Languages: English-Great Britain (EN-GB)

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Abstract

A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems. © 2019 John Wiley & Sons, Ltd.

Keywords

Artificial Intelligence, Control System, Learning algorithm, Particle swarm, PID controller