Design and implement of the recurrent radial basis function neural network control for brushless DC motor

Chen, Seng-Chi; Kuo, Chun-Yi

doi:10.1109/icasi.2017.7988483

Cited by 10 publications

(3 citation statements)

References 3 publications

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“…Recently, the application of surrogates has been considered to reduce computational efforts in different engineering design and control problems. 45,46 Such main surrogates used in deterministic and stochastic PID tuning are polynomial regression (also called response surface methodology), [47][48][49] Radial Basis Function (RBF), [50][51][52] and Kriging surrogate. 4,[53][54][55] To the best of our knowledge, there is a lack of studies that consider the effect of uncertainty in physical load parameters on the optimal design of controllers with the lower computational cost (lower number of simulation experiments).…”

Section: Related Workmentioning

confidence: 99%

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Parnianifard

Fakhfakh

Kotti

et al. 2020

Int J Numerical Modelling

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This paper aims to make a trade‐off between performance and robustness in stochastic control systems with probabilistic uncertainties. For this purpose, we develop a surrogate‐based robust simulation‐optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers. Kriging surrogate is combined with robust design optimization to construct a robust simulation‐optimization model in the class of dual response surfaces. Randomness in simulation experiments due to uncertainty is analyzed through bootstrapping technique by computing confidence regions for the estimation of Pareto frontier. Results confirmed a proper trade‐off between the model's performance with the measure of expected Integral Squared Error (ISE) and robustness against uncertainty in the plant's physical parameters. Finally, the proposed method is evaluated in terms of accuracy, computational cost, and simplicity particularly in comparison with some common existed techniques in the tuning of the Proportional‐Integral‐Derivative (PID) and Fractional‐Order PID (FOPID) controllers.

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Section: Related Workmentioning

confidence: 99%

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Parnianifard

Fakhfakh

Kotti

et al. 2020

Int J Numerical Modelling

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show abstract

“…So, it is difficult to adjust the parameters, and it takes a long time and also the control accuracy is not good [5]. In recent years, researchers have used many artificial intelligent methods to optimize the parameters of DC motors, such as particle swarm optimization algorithm [6]− [8], Harris Hawks optimization [9], [10], genetic algorithm [11]− [13], firefly algorithm [14]− [16], flower pollination algorithm [17]− [19] and neural network [20]− [21].…”

Section: Introductionmentioning

confidence: 99%

Optimization of PID parameters for controlling DC motor based on the aquila optimizer algorithm

Aribowo

Supari

Suprianto

2022

IJPEDS

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This study presents the application of the aquila optimizer (AO) algorithm to determine the parameters of the proportional integral derivative (PID) controller to control the speed of a dc motor. The AO method is inspired by the most popular bird of prey in the northern hemisphere named Aquila. Initially, the proposed AO algorithm is applied to unimodal and multimodal benchmark optimization problems. To get the performance of the AO method, the controller is compared with other methods, namely Seagull optimization algorithm (SOA), marine predators algorithm, giza pyramids construction (GPC), and chimp optimization algorithm (ChOA). The results represent that the AO is promising and shows the effectiveness. Determination of PID parameters using the AO method for dc motor speed control system shows superior performance.

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“…With such assumptions (of the analyzed control structure), fast responses to disturbances are possible. Moreover, the direct identification of the object is not necessary, which is very beneficial in industrial conditions [13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Nature-Inspired Algorithm Implemented for Stable Radial Basis Function Neural Controller of Electric Drive with Induction Motor

Kaminski

2020

Energies

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The main point of this paper was to perform the design process for and verify the properties of an adaptive neural controller implemented for a real nonlinear object—an electric drive with an Induction Motor (IM). The controller was composed as a parallel combination of the classical Proportional-Integral (PI) structure, and the second part was based on Radial Basis Function Neural Networks (RBFNNs) with the on-line recalculation of the weight layer. The algorithm for the adaptive element of the speed controller contained two parts in parallel. The first of them was dedicated for the main path of the neural network calculations. The second realized the equations of the adaptation law. The stability of the control system was provided according to the Lyapunov theorem. However, one of the main issues described in this work is the optimization of the constant part of the analyzed parallel speed controller. For this purpose, the Grey Wolf Optimizer (GWO) was applied. A deep analysis of the data processing during the calculations of this technique is shown. The implemented controller, based on the theory of neural networks, is an adaptive system that allows precise motor control. It ensures the precise and dynamic response of the electric drive. The theoretical considerations were firstly verified during the simulations. Then, experimental tests were performed (using a dSPACE1103 card and an induction machine with a rated power of 1.1 kW).

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Design and implement of the recurrent radial basis function neural network control for brushless DC motor

Cited by 10 publications

References 3 publications

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Optimization of PID parameters for controlling DC motor based on the aquila optimizer algorithm

Nature-Inspired Algorithm Implemented for Stable Radial Basis Function Neural Controller of Electric Drive with Induction Motor

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