Adaptive composite learning dynamic surface control for chaotic fractional-order permanent magnet synchronous motors - Wang, Chenhui (Frontiers in Applied Mathematics and Statistics, 2022)
This paper aims to address the tracking problem of fractional-order permanent magnet synchronous motors based on adaptive backstepping composite learning neural control scheme. Firstly, the functional uncertainties of the considered system are handled by utilizing radial-basis-function neural networks in the backstepping design process. An adaptive neural controller is presented to ensure the convergence of tracking error. Besides, a composite learning algorithm is introduced based on prediction errors which are induced by online data. In every subsystem, the tracking error along with the prediction error are exploited to update the adaptation parameter of the radial-basisfunction neural network so that the higher precision of parametric estimation and the ideal systematic adaptation under interval excitation condition are guaranteed. Furthermore, the boundedness of all closed-loop signals is demonstrated via stability analysis based on Lyapunov stability theory. Finally, in order to verify the efficiency of the proposed control strategy, a numerical simulation is performed, illustrating that the given scheme is endowed with satisfactory control performance.