Abstract An adaptive backstepping control (ABC) based on functional link radial basis function neural network (FLRBFNN) was proposed to improve the control performance of permanent magnet linear synchronous motor (PMLSM) servo system and solve the influence on the system of the uncertain factors such as the changes in system parameters, external disturbances, frictions and so on. Firstly, the dynamic model of PMLSM with the uncertain factors was established. Then, the adaptive law in ABC is used to estimate the lumped uncertainties of the system, but the “differential explosion” phenomenon is produced because of a large number of derivation operations during the design of ABC. Thus, in order to solve the problem and further improve the system performance, FLRBFNN is used to learn and adjust the controller parameters online. FLRBFNN is combined with radial basis function neural network (RBFNN) and functional link neural network (FLNN), FLNN is used to increase the searching space to improve the convergence speed and convergence accuracy of neural network so that it can improve the ability to estimate the uncertainties of RBFNN and reduce the influence of uncertain factors to the system. The experimental results show that the proposed method is effective. Compared with ABC, the system has stronger robust performance and tracking performance.
Wu Yongkang,Zhao Ximei. Adaptive Backstepping Control Based on Functional Link Radial Basis Function Neural Network for PMLSM[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 4044-4051.
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2018, Vol. 33 
