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Application of MLP and RBF neural networks in the control structure of the drive system with elastic joint

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Purpose ‐ The purpose of this paper is to obtain an estimation of not measured mechanical state variables of the drive system with elastic coupling between the driven motor and a load machine, using neural networks (NN) of different type for the sensorless drive system. Design/methodology/approach ‐ The load-side speed and the torsional torque are estimated using multi-layer perceptron (MLP) and radial basis function (RBF) networks. The special forms of input vectors for neural state estimators were proposed and tested in open- and closed-loop control structure. The estimation quality as well as sensitivity of neural estimators to the changes of the inertia moment of the load machine were evaluated and compared. Findings ‐ It is shown that an application of RBF-based neural estimators can give better accuracy of the load speed and torsional torque estimation, especially for the proper choice of the input vector of NN, also in the case of a big change of the load machine time constant. Research limitations/implications ‐ The investigation and comparison is based on simulation tests and looked mainly at the quality of state variable estimation while the realisation cost in parallel processing devices (FPGA) still need to be addressed. Practical implications ‐ The proposed neural state variable estimators of two-mass system can be practically implemented in the control structure of two-mass drive with additional feedbacks from load machine speed and torsional torque, which results in the successive vibration damping. Originality/value ‐ The application of RBF neural state estimators for two-mass drive and their comparison with commonly used MLP-based estimators, as well as testing of both type of NN in the closed-loop control structure with additional feedbacks based on state variables estimated by neural estimators.
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Keywords: Control systems; Electric motors; Estimation; Neural nets; Vibration

Document Type: Research Article

Publication date: May 8, 2009

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