Adaptive Neural Control for Nonlinear Mechatronic Systems

This research investigates adaptive neural control methods for enhancing the precision and stability of nonlinear mechatronic systems. Nonlinear dynamics often pose challenges to traditional control techniques due to their sensitivity to modeling errors and external disturbances. The adaptive neural control approach proposed in this study uses neural networks for online learning and adjustment to dynamic environments. A model was developed and tested using MATLAB/Simulink for a representative mechatronic system, specifically a robotic manipulator with joint nonlinearities and external load variations. Simulation results revealed significant improvements in trajectory tracking accuracy and disturbance rejection, with a 35% average reduction in mean squared tracking error compared to conventional PID control. Regression and predictive analysis confirmed the statistical robustness of the neural controller. This study demonstrates the potential of adaptive neural strategies in complex control environments and provides a path forward for real-time intelligent control applications. Keywords: Adaptive Control, Neural Networks, Nonlinear Systems, Mechatronics, Intelligent Control, Predictive Modeling