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

• A MLP-based neurocontroller has been used as on-line trained speed controller in a drive system with elastic joint.
• The on-line adaptation mechanism of neurocontroller weights relies on Levenberg–Marquardt method with fuzzy model for adaptive changes of a learning rate.
• The developed speed neurocontroller uses only easy measurable motor speed (no sensors or estimators of other mechanical state variables are applied).
• Very good torsional vibration damping was obtained for this single-loop (from motor speed only) adaptive control structure of the two-mass system.

This paper describes the application of a neural model in a speed control loop of an electrical drive with an elastic mechanical coupling. Such mechanical construction makes precise speed control more difficult because of the oscillation tendency of state variables caused by a long shaft. The goal of the presented application was the replacement of a classical speed controller by an on-line trained neurocontroller, based on only one feedback from easily measurable driving motor speed. The proposed controller is based on the feedforwad neural network. Internal coefficients of neural model – weights – are adapted on-line according to the Levenberg–Marquardt algorithm. One of the problematic issues in such implementation is selection of a learning factor of the weight adaptation algorithm. In the proposed solution, a fuzzy model was implemented for calculation of this learning coefficient. The proposed solution was compared to the classical one with a PI speed controller. The designed control structure was tested in simulations and verified in experiments, using dSPACE1103 card.

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