Artificial neural network based robust speed control of permanent magnet synchronous motors
Purpose - The aim of the paper is to find a simple structure of speed controller robust against drive parameters variations. Application of artificial neural network (ANN) in the controller of PI type creates proper non-linear characteristics, which ensures controller robustness. Design/methodology/approach - The robustness of the controller is based on its non-linear characteristic introduced by ANN. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the ANN to form the proper shape of the control surface, which represents the non-linear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behaviour of a laboratory speed control system is validated in the experimental set-up. The control algorithms of the system are performed by a microprocessor floating point DSP control system. Findings - The proposed controller structure with proper control surface created by ANN guarantees expected robustness. Originality/value - The original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.
control systems motion magnetic devices neural nets