Backstepping Controller with Neural Network Supervisory for a Magnetic Levitation System

This article addresses the control challenges of magnetic levitation systems, which enable metal objects to move through magnetic fields without mechanical contact. Traditional control methods often fail due to these systems' inherent instability and strong nonlinear characteristics. We design a nonlinear control strategy combining a backstepping controller and a neural network structure to overcome these issues. The backstepping controller manages system nonlinearities, while the neural network adapts to un-desired conditions and disturbances, ensuring precise ball control in a magnetic suspension system. It delves into the mathematical structure and devises the neural network supervisory, covering adaptive control of a third-order system and providing simulation studies. The efficiency of the devised control approach is illustrated via numerical simulations, showcasing its effectiveness and robustness in handling uncertainties and disturbances.