An on-line trained neural controller with a fuzzy learning rate of the Levenberg–Marquardt algorithm for speed control of an electrical drive with an elastic joint

Kaminski, Marcin; Orłowska-Kowalska, Teresa

doi:10.1016/j.asoc.2015.04.013

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…The input was in the form of data processed from the hidden layer to get output in the form of a motion prediction sensor. The similar thing was also done by Marcin Kaminski and Teresa Orlowska-Kowalska [11] for trained controllers. This model was really helpful for mechanical model learning, which required a continuous training.…”

Section: Introductionmentioning

confidence: 55%

The Implementation of the Levenberg-Marquardt for Continuous Improvement of the Management System for BPK PENABUR Education Foundation in Jakarta

Kusuma¹,

Wibawa²,

Sitepu³

2018

IJET

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Improving the quality of the management system for schools that have several branches requires a big commitment from executor to managerial. By using the Levenberg-Marquardt education system concept that belongs to 15 branches, it is divided into 5 subsystems and 3 layers. The subsystems are in the form of learning dynamics, organizational transformation, human development, knowledge management and technology applications. Layer 1 is the managerial rank in each branch, the second layer is the leader as the first validator to be forwarded to the third layer, which is the head office with the final decision at the top of BPK Penabur organization. The improvement of management system starts from the gap between each subsystem that has been agreed as the standard. All components covering the secretariat (administration), Principal, Deputy Principal (student, curriculum, Facility SBI), teachers, librarians and laboratory assistants were the subjects responsible for the improvement of the management performance. While the president director and organization leader are the last layer that determines the better changes that is run. After being done gradually and continuously, the gap occurred is decreasing. By reducing the gap, the performance of the management at BPK PENABUR school can become better

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Section: Introductionmentioning

confidence: 55%

The Implementation of the Levenberg-Marquardt for Continuous Improvement of the Management System for BPK PENABUR Education Foundation in Jakarta

Kusuma¹,

Wibawa²,

Sitepu³

2018

IJET

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“…An intelligent adaptive neural network (ANN) controller for Ref. [112] Optimization of the parameters of a PI controller with real-time data and giving dynamic stability A direct torque controlled (DTC) electric vehicle (EV) propulsion system e stator reference flux voltage considered for synthesizing the space vector with width modulation is obtained for a DTC 8…”

Section: An Unmanned Helicoptermentioning

confidence: 99%

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller

Sabetahd

Ghasemi

Poursorkhabi

et al. 2022

Computational Intelligence and Neuroscience

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The present study aims to design a robust adaptive controller employed in the active tuned mass damper (ATMD) system to overcome undesirable vibrations in multistory buildings under seismic excitations. We propose a novel adaptive type-2 neural-fuzzy controller (AT2NF). All system parameters are taken as unknowns. The MLP neural network is used to extract the Jacobian and estimate the structural model; then, the estimated model is applied to the controller online. To tune the control force applied to the ATMD and achieve the control targets, the controller parameters are adaptively trained using the extended Kalman Filter (EKF) and the error back-propagation algorithm. A PID controller is also included in this method to increase the stability and robustness of the adaptive type-2 neural-fuzzy controller against seismic vibrations. An online simple adaptive controller (OSAC) is studied to demonstrate the suggested controller’s superiority. The OSAC is based on adaptive control of the implicit reference model. In this proposed method, the EKF is used to tune the controller parameters online as a novel feature. The uncertainty associated with identifying the mechanical properties of structures, such as mass and stiffness, is one of the primary challenges in the real-time control of structures. This paper investigates how both controllers cope with parametric uncertainties under far-field and near-field seismic excitation. According to numerical results, the AT2NF controller outperforms OSAC in minimizing the dynamic responses of the structure during an earthquake and accomplishing control objectives when the structure’s characteristics change.

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“…Then learning rate  [Kaminski and Orlowska-Kowalska (2015)] and smooth factor α [Wu and Moody (2014)] were set; k was set as 1. Some initial values were substituted to calculate the initial deviation of the output value and ideal value,…”

Section: The Procedures Of Pid Control Optimization With Bp Neural Nementioning

confidence: 99%

Distance Control Algorithm for Automobile Automatic Obstacle Avoidance and Cruise System

Zhao¹

2018

CMES

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With the improvement of automobile ownership in recent years, the incidence of traffic accidents constantly increases and requirements on the security of automobiles become increasingly higher. As science and technology develops constantly, the development of automobile automatic obstacle avoidance and cruise system accelerates gradually, and the requirement on distance control becomes stricter. Automobile automatic obstacle avoidance and cruise system can determine the conditions of automobiles and roads using sensing technology, automatically adopt measures to control automobile after discovering road safety hazards, thus to reduce the incidence of traffic accidents. To prevent accidental collision of automobile which are installed with automatic obstacle avoidance and cruise system, active brake should be controlled during driving. This study put forward a neural network based proportional-integral-derivative (PID) control algorithm. The active brake of automobiles was effectively controlled using the system to keep the distance between automobiles. Moreover the algorithm was tested using professional automobile simulation platform. The results demonstrated that neural network based PID control algorithm can precisely and efficiently control the distance between two cars. This work provides a reference for the development of automobile automatic obstacle avoidance and cruise system.

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An on-line trained neural controller with a fuzzy learning rate of the Levenberg–Marquardt algorithm for speed control of an electrical drive with an elastic joint

Cited by 11 publications

References 13 publications

The Implementation of the Levenberg-Marquardt for Continuous Improvement of the Management System for BPK PENABUR Education Foundation in Jakarta

The Implementation of the Levenberg-Marquardt for Continuous Improvement of the Management System for BPK PENABUR Education Foundation in Jakarta

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller

Distance Control Algorithm for Automobile Automatic Obstacle Avoidance and Cruise System

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