Cascaded Linear–Nonlinear Active Disturbance Rejection Control and Parameter Tuning of Magnetic Levitation Ball System

Due to the significant nonlinear characteristics of the magnetic bearing, it is difficult to establish an accurate mathematical model, and it is susceptible to external disturbances. Traditional control methods struggle to meet the control requirements. Active disturbance rejection control (ADRC) does not rely on accurate models and has outstanding anti-interference ability. In order to improve the anti-disturbance ability and control stability of the system, a cascaded linear–nonlinear active disturbance rejection control method (CL-NLADRC) based on the improved artificial jellyfish algorithm is proposed and applied to the magnetic levitation ball system. Firstly, the mathematical model of the magnetic levitation ball system is established, and based on this model, a cascaded linear–nonlinear extended state observer is constructed to estimate and compensate for the system state, thereby enhancing the dynamic response capability of the system. Subsequently, the tangent spiral motion and the lens reversal learning strategy are introduced to improve the artificial jellyfish algorithm to further improve the global optimization performance of the algorithm. Finally, the improved artificial jellyfish algorithm is used to optimize the CL-NLADRC controller parameters. The simulation and experimental results show that compared with the traditional LADRC and PID controllers, the proposed CL-NLADRC has a significant improvement in the steady-state error, response speed, and anti-disturbance performance of the system. Among them, the root mean square error decreased by 14% and 47%, respectively, which verified the effectiveness and stability of the method in the magnetic levitation ball system.