Abstract
A new strategy of model reference adaptive control (MRAC) system based on neural network generalized inverse (NNGI) algorithm, termed as MRAC-NNGI system, is proposed for the current and speed regulations of permanent magnet synchronous motor (PMSM) drives. Due to the fact that PMSM is a multivariable nonlinear system with strong couplings, this paper gives an analysis of generalized reversibility combined with NN. The developed scheme of NNGI is transformed into a pseudo-linear system from connecting the motor plant and achieved the purposes of decoupling and linearization with Levenberg-Marquardt algorithm off-line. Therefore, an adjustable gain of closed-loop adaptive controller is developed by introducing MRAC into this kind of pseudo-linear system. The self-adaptive law is given for the gain regulation of linear system. Comparison of simulation results from others widely used algorithms confirm that it incorporates the merits of model-free learning, high-precision tracking and strong anti-interference capability.
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Liu, G., Dong, B., Chen, L., Zhao, W. (2011). A New Model Reference Adaptive Control of PMSM Using Neural Network Generalized Inverse. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21111-9_7
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DOI: https://doi.org/10.1007/978-3-642-21111-9_7
Publisher Name: Springer, Berlin, Heidelberg
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