Modelling, analysis and control of an eddy current braking system using intelligent controllers

Singh, Arunesh Kumar; Nasiruddin, Ibraheem; Sharma, Amit Kumar; Saxena, Abhinav

doi:10.3233/jifs-169930

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Neural Networks are simply defined as massively parallel interconnected networks of simple (usually adaptive) elements. Their hierarchical organizations are intended to interact with objects of the real world in the same way as biological nervous systems do [19,20].…”

Section: Shlnnmentioning

confidence: 99%

Artificial Intelligence Based PID Controller for an Eddy Current Dynamometer

Uluocak¹,

Yavuz²

2022

Intelligent Automation &Amp; Soft Computing

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This paper presents a design and real-time application of an efficient Artificial Intelligence (AI) method assembled with PID controller of an eddy current dynamometer (ECD) for robustness due to highly nonlinear system by reason of some magnetism phenomena such as skin effect and dissipated heat of eddy currents. PID Control which is known as the most popular conventional control method in industry is inadequate for such nonlinear systems. On the other hand, Adaptive Neural Fuzzy Interference System (ANFIS), Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), and Radial Basis Neural Network (RBNN) are examples used as artificial intelligence-based techniques that can increase the performance of conventional control systems in particular. The proposed control system proves changeable K p (Proportional gain), K i, (Integral gain) and K d (Derivative gain) parameters in real-time to adapt and presents a good capacity to adapt nonlinearities and bring robustness using 4 different versatile soft computing methods of ANFIS, SHLNN, GRNN, and RBNN. The testing dataset is extracted from experimental studies and its robustness has also been verified with different Artificial Intelligence (AI) methods. The presented technique is observed to have a good performance in terms of response time (t) and accuracy of desired speed value (V) under different parameters such as non-linear dynamics (V, T) of the system elements and the varying load effects.

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Section: Shlnnmentioning

confidence: 99%

Artificial Intelligence Based PID Controller for an Eddy Current Dynamometer

Uluocak¹,

Yavuz²

2022

Intelligent Automation &Amp; Soft Computing

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“…Moreover, Zhao et al [14] designed an ANN (Artificial Neural Network) for MPC for damping DC voltage smaller steady-state error and a dynamic voltage overshoot on aircraft systems. Yan et al [15] handled the NMP [2] PD controller Air bearing speed 3 Fountaine [3] PID control Load of an ECD 4 Simeu et al [4] Two-Observer based nonlinear compensator Angular speed of an eddy current brake 5 Gosline et al [5] Time domain passivity Position of a haptic interface 6 Anwar [6] Sliding mode control Torque of and Eddy current dynamometer 7 Roozbehani et al [7] Fuzzy + PID Torque of and Eddy current dynamometer 8 Yang et al [8] Model-Based control Vehicle speed using Eddy current retarder 9 Xu et al [9] Indirect adaptive Fuzzy + H∞ Shaft speed of Eddy current brake 10 Lee et al [10] Sliding mode control Vehicle slip ratio 11 Bunker et al [11] Multivariable Controller Torque and speed of Eddy current brake 12 Singh et al [12] SHLNN, Fuzzy Logic Rotor speed of Eddy current brake problem by using ANN supervised MPC system. RBNN coupled with MPC was demonstrated to be effective in the paper of Huang et al [16] for clutch control, Han et al [17] for optimization of wind turbines, Mirzaeinejad [18] for controlling of wheel slip in antilock braking systems, Jamil et al [19] for controlling control of vibrations in tall structure.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Coupled with Artificial Intelligence for Eddy Current Dynamometers

Uluocak¹,

Yavuz²

2023

Computer Systems Science and Engineering

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The recent studies on Artificial Intelligence (AI) accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adaptiveness and fast responding features. The Model Predictive Control (MPC) technique is a widely used, safe and reliable control method based on constraints. On the other hand, the Eddy Current dynamometers are highly nonlinear braking systems whose performance parameters are related to many processes related variables. This study is based on an adaptive model predictive control that utilizes selected AI methods. The presented approach presents an updated the mathematical model of an Eddy Current Dynamometer based on experimentally obtained system operational data. Finally, the comparison of AI methods and related learning performances based on the assessment technique of mean absolute percentage error (MAPE) issues are discussed. The results indicate that Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), Radial Basis Network (RBNN), Neuro Fuzzy Network (ANFIS) coupled MPC have quite satisfying performances. The presented results indicate that, amongst them, GRNN appears to provide the best performance.

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“…The work in [24] analyzes the optimization of duty cycle using a hybrid standard multi-verse optimization (MVO) for efficient operation of maximum power point tracking (MPPT) controllers in order to minimize the inadequacies occurring in conventional controllers. Another research idea is addressed in [25] for the combination of conventional brakes with eddy current brake to achieve superior braking performance at high speed. The paper discusses the hardware model development, analysis and control of a multi-disc eddy current braking system using different intelligent controllers.…”

mentioning

confidence: 99%