Frequency-Domain System Identification of an Unmanned Helicopter Based on an Adaptive Genetic Algorithm

Du, Yongle; Fang, Jiancheng; Miao, Cunxiao

doi:10.1109/tie.2013.2257135

Cited by 66 publications

(25 citation statements)

References 30 publications

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“…The most important factors in genetic algorithm are the coding, fitness function, initial population, population size, crossover rate, operator, mutation rate, and abort condition [18] [19]. Each course has a course number, teaching time, and teacher, and the combination of these three objects is considered a gene.…”

Section: Course Scheduling Through Genetic Algorithmmentioning

confidence: 99%

Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

Wang¹

2018

Int. J. Emerg. Technol. Learn.

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Abstract-Traditional artificial intelligence and computer-aided course scheduling schemes can no longer meet the increasing demands caused by the informatization of teaching management in colleges and universities. To address this problem, this study designed an improved adaptive genetic algorithm that is based on hard and soft constraints for course scheduling. First, the mathematical model of the genetic algorithm was established. The combination of time, teacher, and course number was regarded as the gene coding. The weekly course schedule of each class was a chromosome, and the course schedule of the entire school was the initial population. The fitness was designed according to the priority of each class, curriculum dispersion, and teacher satisfaction. Local columns between individuals were selected through the roulette principle for a variation of crossover and random columns. Iterative calculation was implemented on the basis of the default mutation and crossover rates to study the optimal course scheduling scheme. Experimental results demonstrate that the improved adaptive genetic algorithm is superior to the original genetic algorithm. When the number of iterations is 150, population evolution is optimal and the fitness does not increase. When the population size is 150 classes, the average scheduling time is the shortest. The basic, adaptive, and improved adaptive genetic algorithms are compared in terms of the number of average iterations required for convergence, maximum individual fitness, and average individual fitness. Comparison results show that the improved adaptive genetic algorithm is superior to the two other algorithms. This study provides references for the model building and evaluation of course scheduling in colleges and universities.

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Section: Course Scheduling Through Genetic Algorithmmentioning

confidence: 99%

Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

Wang¹

2018

Int. J. Emerg. Technol. Learn.

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“…Bacterial foraging optimization (BFO) algorithm based ANC schemes have also been designed in the recent past (GholamiBoroujeny andEshghi, 2010, 2012). A quantum behaved PSO (Fang et al, 2010a;Manju and Nigam, 2014) based online system identification scheme has been applied for controlling quality of service offered by a web server in (Fang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method is compared with conventional PSO, comprehensive An adaptive GA has been used in (Du et al, 2014) to successfully identify the attitude model of an unmanned helicopter. The authors have employed a frequency domain cost function and compared the results obtained with that using a simple GA. Parameter estimation of a permanent magnet synchronous machine under variable speed control has been attempted in (Liu and Zhu, 2014) using a quantum GA. Also, a few evolutionary computing based system identification methods have been presented lately for parameter estimation of bilinear (Wang and Gu, 2007;Modares et al, 2010b;Zorlu, 2011) andhysteretic systems (Kyprianou et al, 2001;Ye and Wang, 2007;Charalampakis and Koumousis, 2008;Ye and Wang, 2009;Worden 36 andManson, 2011, 2012;Worden and Barthorpe, 2012;Liu et al, 2012;Ortiz et al, 2013;Charalampakis and Dimou, 2013;Quaranta et al, 2014).…”

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confidence: 99%

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

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“…Some significant results have been reported in [1]- [8]. In [2], an image-based visual servoing control system was developed for automatic hovering of an autonomous airship in the outdoor environment.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Position/Attitude Tracking Control of Aerial Robot With Unknown Inertial Matrix Based on a New Robust Neural Identifier

Lai

Liu

Zhang

et al. 2016

IEEE Trans. Neural Netw. Learning Syst.

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This paper presents a novel adaptive controller for controlling an autonomous helicopter with unknown inertial matrix to asymptotically track the desired trajectory. To identify the unknown inertial matrix included in the attitude dynamic model, this paper proposes a new structural identifier that differs from those previously proposed in that it additionally contains a neural networks (NNs) mechanism and a robust adaptive mechanism, respectively. Using the NNs to compensate the unknown aerodynamic forces online and the robust adaptive mechanism to cancel the combination of the overlarge NNs compensation error and the external disturbances, the new robust neural identifier exhibits a better identification performance in the complex flight environment. Moreover, an optimized algorithm is included in the NNs mechanism to alleviate the burdensome online computation. By the strict Lyapunov argument, the asymptotic convergence of the inertial matrix identification error, position tracking error, and attitude tracking error to arbitrarily small neighborhood of the origin is proved. The simulation and implementation results are provided to evaluate the performance of the proposed controller.

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Frequency-Domain System Identification of an Unmanned Helicopter Based on an Adaptive Genetic Algorithm

Cited by 66 publications

References 30 publications

Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Adaptive Position/Attitude Tracking Control of Aerial Robot With Unknown Inertial Matrix Based on a New Robust Neural Identifier

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