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Abstract

The paper presents a method for designing a neural speed controller with use of Reinforcement Learning method. The controlled object is an electric drive with a synchronous motor with permanent magnets, having a complex mechanical structure and changeable parameters. Several research cases of the control system with a neural controller are presented, focusing on the change of object parameters. Also, the influence of the system critic behaviour is researched, where the critic is a function of control error and energy cost. It ensures long term performance stability without the need of switching off the adaptation algorithm. Numerous simulation tests were carried out and confirmed on a real stand.

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Authors and Affiliations

T. Pajchrowski
P. Siwek
A. Wójcik
ORCID: ORCID
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Abstract

The paper deals with the application of the feed-forward and cascade-forward neural networks to mechanical state variable estimation of the drive system with elastic coupling. The learning procedure of neural estimators is described and the influence of the input vector size and neural network structure to the accuracy of state variable estimation is investigated. The quality of state estimation by neural estimators of different types is tested and compared. The simple optimisation procedure is proposed. Optimised neural estimators of the torsional torque and the load machine speed are tested in the open-loop and closed-loop control structure of the drive system with elastic joint, with additional feedbacks from the shaft torque and the difference between the motor and the load speeds. It is shown that torsional vibrations of the two-mass system are damped effectively using the closed-loop control structure with additional feedbacks obtained from the developed neural estimators. The simulation results are confirmed by laboratory experiments.

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Authors and Affiliations

T. Orłowska-Kowalska
M. Kamiński
K. Szabat
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Abstract

The paper describes a novel online identification algorithm for a two-mass drive system. The multi-layer extended Kalman Filter (MKF) is proposed in the paper. The proposed estimator has two layers. In the first one, three single extended Kalman filters (EKF) are placed. In the second layer, based on the incoming signals from the first layer, the final states and parameters of the two-mass system are calculated. In the considered drive system, the stiffness coefficient of the elastic shaft and the time constant of the load machine is estimated. To improve the quality of estimated states, an additional system based on II types of fuzzy sets is proposed. The application of fuzzy MKF allows for a shorter identification time, as well as improves the accuracy of estimated parameters. The identified parameters of the two-mass system are used to calculate the coefficients of the implemented control structure. Theoretical considerations are supported by simulations and experimental tests.
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Authors and Affiliations

Kacper Śleszycki
1
ORCID: ORCID
Karol Wróbel
1
ORCID: ORCID
Krzysztof Szabat
1
ORCID: ORCID
Seiichiro Katsura
2
ORCID: ORCID

  1. Wrocław University of Science and Technology, Institute of Electrical Machines, Drives and Measurements, Wrocław, Poland
  2. Keio University, Department of System Design Engineering, Tokyo, Japan

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