Adaptive neural model optimized by modified differential evolution for identifying 5-DOF robot manipulator dynamic system

Sơn, Nguyễn Ngọc; Ánh, Hồ Phạm Huy; Chau, Truong Dinh

doi:10.1007/s00500-016-2401-x

Cited by 13 publications

(3 citation statements)

References 29 publications

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“…(Differential Evolution, DE) do Storn và Price đề nghị năm 1997 5 . Việc cải tiến thuật toán DE thành thuật toán MDE đã được thực hiện bởi Sơn và đồng nghiệp 6,7 5,6,9,10 .…”

Section: Phương Pháp Thuật Toán Tiến Hóa VI Sai Cải Tiến (Modified Di...unclassified

“…Thuật toán MDE là một thuật toán tối ưu toàn cục đã áp dụng thành công trong các bài toán cơ kỹ thuật như tạo dáng đi cho robot 8,15 , tối ưu dầm trong xây dựng 10 , điều khiển robot 6,7 . Việc thử nghiệm thuật toán này cho bài toán nghịch đảo một chiều phương pháp MT đối với số liệu mô hình và số liệu thực tế cho thấy kết quả tốt.…”

Section: Kết Luậnunclassified

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One–dimensional magnetotelluric inversion by modified differential evolution algorithm

Luu

2023

Sci. Tech. Dev. J. - Nat. Sci.

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Traditional methods of solving geophysical inverse problems often used an initial structural model and an iterative process to find solutions. During each iteration, the model was linearized and updated, then the error between the model's response and the observed data were calculated and minimized. The inverse results solved by this method were often strongly dependent on the initial model choice, and the minimum of the objective function found could be the local value but not the global minimum. Global search methods such as genetic algorithm (GA) overcame these limitations of traditional solutions. This paper presented the application of modified differential evolution (MDE) algorithm which was developed from the genetic algorithm, to one-dimension (1D) magnetotelluric inversion. The algorithm had been used to invert two 3-layer geoelectrical models. The results showed that when using a 3-layer model for inversion, the obtained parameters of the subsurface almost coincided with those of the theoretical models. In the case the number of layers used for inversion had more than three layers (four layers and five layers), the obtained results showed that there was still a good agreement between the inverted structure and the theoretical model. Therefore, the algorithm was applied to one-dimensional (1D) inversion for data of one magnetotelluric station measured at Cu Chi district, Ho Chi Minh city. The results showed that the subsurface structure down to a depth of 16 km consisted of 3 layers: the top layer had a resistivity of 119 Ω.m with a thickness of about 760 m, the middle resistive layer with a resistivity of 4353 Ω.m with about 10 km thickness, and the lower half-space with a relatively low resistivity of 41.4 Ω.m. This result was consistent with the geoelectric structure information obtained from other magnetotelluric studies carried out in the studied area, demonstrating the practical applicability of the improved differential evolution algorithm.

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Section: Phương Pháp Thuật Toán Tiến Hóa VI Sai Cải Tiến (Modified Di...unclassified

Section: Kết Luậnunclassified

One–dimensional magnetotelluric inversion by modified differential evolution algorithm

Luu

2023

Sci. Tech. Dev. J. - Nat. Sci.

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“…Liang et al 43 introduced a SEDE which divided three different mutation operators (i.e., rand/1, current-to-rand/1, and current-to-best/1) into two overlapping groups including the exploration group (rand/1 and current-to-rand/1) and exploitation group (current-to-best/1 and current-to-rand/1). Nguyen et al 66 introduced a modified DE with a mutation scheme by combining rand/1 and best/1 operators. Li et al 67 introduced an improved DE with two mutation operators collaboration by using an elite guidance mechanism to modified the classical mutation operator including rand/2 and best/2.…”

Section: Related Workmentioning

confidence: 99%

Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator

2021

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This manuscript introduces a new adaptive inverse neural (AIN) control method applied to precisely track the piezoelectric (PZT) actuator displacement.First, a 3-layer neural network optimized by the enhanced differential evolution technique which modifies a mutation scheme and provides suggestions for selecting mutant coefficient F, crossover coefficient CR, and population size NP, is used to identify the inverse nonlinearity hysteresis structure of the PZT actuator. Second, a feed-forward control based on the identified model is proposed to compensate for the PZT hysteresis effect. Third, the Lyapunov stability principle is used to design and implement an adaptive law-based neural sliding mode model plus the feed-forward compensator to ensure that the whole PZT plant is operated in asymptotical stability. The experiment results demonstrate the proposed AIN controller proves superiority in comparison with other advanced control methods. K E Y W O R D S adaptive inverse neural controller, back-propagation, enhanced differential evolution, hybrid adaptive inverse neural control, Lyapunov stability concept

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Stability Identification of Power System Based Neural Network Training

Vinh²,

Huan³

2020

Advances in Intelligent Systems and Computing

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Adaptive neural model optimized by modified differential evolution for identifying 5-DOF robot manipulator dynamic system

Cited by 13 publications

References 29 publications

One–dimensional magnetotelluric inversion by modified differential evolution algorithm

One–dimensional magnetotelluric inversion by modified differential evolution algorithm

Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator

Stability Identification of Power System Based Neural Network Training

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