A robust control of a class of induction motors using rough type-2 fuzzy neural networks

Sabzalian, Mohammad Hosein; Mohammadzadeh, Ardashir; Lin, Shuyi; Zhang, W.

doi:10.1007/s00500-019-04493-3

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…The delta adaptation rule is used as the learning algorithm for the T2FNN controller. The consequent parameters of T2FNN are updated online by using this algorithm 35,36 . The consequent parameters of T2FNN are updated as follows:

b_{0}^{i} ((), k) = b_{0}^{i} ((), k) + Δ b_{0}^{i} ((), k) = b_{0}^{i} ((), k) + α \frac{\partial E}{\partial b_{0}^{i}}

\frac{\partial E}{\partial b_{0}^{i}} = δ \frac{\partial i_{q}^{*}}{\partial b_{0}^{j}}

…”

Section: Design Of Type‐2 Fuzzy Neural Network Controllermentioning

confidence: 99%

“…There is a need for controller structures that are less sensitive to motor parameter changes, capable of rejecting load disturbances and robustness to speed changes. 7,8 Nowadays, with the developments in microprocessor and power electronics, it is aimed to improve the performance of induction motor used in industrial applications. In parallel with these developments, intelligent controller structures are preferred in various applications instead of conventional controllers.…”

Section: Introductionmentioning

confidence: 99%

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Real‐time adaptive speed control of vector‐controlled induction motor drive based on online‐trained Type‐2 Fuzzy Neural Network Controller

Açıkgöz

2020

Int Trans Electr Energ Syst

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Background: The speed control of induction motor having superior features such as high efficiency, robust construction, low maintenance and low cost is carried out more effectively with the developments in control theory. With innovations in power electronics and microprocessor technology, it has been made possible to use the vector control method for applications requiring high performance in induction motor drives. Aim and Objective: In this study, Type-2 Fuzzy Neural Network (T2FNN) controller which is durable, adaptable and has fast dynamic response capabilities against parameter changes, is proposed to obtain a robust speed response from induction motor. Materials and Methods: Matlab/RTI model is developed through DS1103 controller card to experimentally test the speed performance of the proposed controller based induction motor. The proposed controller is trained on-line to improve the robustness of the induction motor against disturbances. After that, experimental studies are built to investigate the speed control behavior and effectiveness of the induction motor. Results and Discussion: The performance of T2FNN controller is compared with PI and Type-1 Fuzzy Neural Network (T1FNN) controllers. The experimental results clearly indicate that the proposed controller has a faster and more stable dynamic response capability. Conclusion: The proposed controller is significantly improved the dynamic response of the induction motor compared to T1FNN and PI controllers. In addition, the settling time, overshoot and recovery time promoting percentages of T1FNN and PI controllers by T2FNN controller are in satisfactory levels during all steady-state and the transient conditions.

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b_{0}^{i} ((), k) = b_{0}^{i} ((), k) + Δ b_{0}^{i} ((), k) = b_{0}^{i} ((), k) + α \frac{\partial E}{\partial b_{0}^{i}}

\frac{\partial E}{\partial b_{0}^{i}} = δ \frac{\partial i_{q}^{*}}{\partial b_{0}^{j}}

…”

Section: Design Of Type‐2 Fuzzy Neural Network Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real‐time adaptive speed control of vector‐controlled induction motor drive based on online‐trained Type‐2 Fuzzy Neural Network Controller

Açıkgöz

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

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“…Perception methods for vehicles can reduce the rate of accidents at an intersection. Meanwhile, improvements in the performance of control algorithms also need the support of real-time and accurate prediction data [ 14 , 15 ]. Based on perception methods, the trajectory prediction methods gradually became the focus of research.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Wang

Liu

et al. 2023

Sensors

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Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety.

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“…Many LPV techniques, such as switching LPV control, model predictive control based on the LPV model, data-driven strategies, and LPV control with scheduling uncertain parameters, have been applied to aerospace domain problems [ 24 , 25 , 26 , 27 , 28 ]. Fuzzy gain scheduling techniques have also been studied [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Self-Scheduled LPV Control of Asymmetric Variable-Span Morphing UAV

Lee

Kim

Lee

et al. 2023

Sensors

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In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA generic transport model. The left and right wing span variation ratios were decomposed into symmetric and asymmetric morphing parameters, which were then used as the scheduling parameter and the control input, respectively. LPV-based control augmentation systems were designed to track the normal acceleration, angle of sideslip, and roll rate commands. The span morphing strategy was investigated considering the effects of morphing on various factors to aid the intended maneuver. Autopilots were designed using LPV methods to track commands for airspeed, altitude, angle of sideslip, and roll angle. A nonlinear guidance law was coupled with the autopilots for three-dimensional trajectory tracking. A numerical simulation was performed to demonstrate the effectiveness of the proposed scheme.

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A robust control of a class of induction motors using rough type-2 fuzzy neural networks

Cited by 9 publications

References 27 publications

Real‐time adaptive speed control of vector‐controlled induction motor drive based on online‐trained Type‐2 Fuzzy Neural Network Controller

Real‐time adaptive speed control of vector‐controlled induction motor drive based on online‐trained Type‐2 Fuzzy Neural Network Controller

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Self-Scheduled LPV Control of Asymmetric Variable-Span Morphing UAV

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