(untitled) (Comprehensive Journal of Science, 2026)
The aim of this paper is to design a robust ,nonlinear control system to improve the position tracking accuracy of permanent Magnet Linear Synchronous Motors(PMLSMs) under unknown disturbances and parameter variations[1].the backstepping control serves as a recursive design methodology to achieve stability in complex nonlinear systems[2], employing a Radial Basis Function Neural Network (RBFNN) for approximation and cancellation of nonlinear dynamics such as (friction and external load ) online. The adaptive law for the neural network weights(W) is derived using Lyapunov Stability to guarantee Uniformly Ultimately Bounded (UUB)stability for the close-loop system. Superior tracking performance and high robustness achieved by the proposed approach Matlab simulation demonstrate,a comparative analysis and simulation results demonstrate the proposed approach achieves superior tracking performance and high robustness compared to conventional cascaded control methods.