Adaptive PSO based gain optimization of sliding mode control for position tracking control of magnetic levitation systems

• Published in: 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA) pp. 157 - 162
• Main Authors: Bosera, Adisu Safo, Olana, Frew Dereje, Merga, Chala, Gutole, Siraj Tura
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.11.2022
• Subjects: Adaptation models; adaptive particle swarm optimization; Adaptive systems; arctangent function; Gaussian disturbances; Loading; Magnetic Levitations; model uncertainty; Particle swarm optimization; Position control; Sliding mode control; Sliding Mode Controller; Uncertainty
• DOI: 10.1109/ICT4DA56482.2022.9971197

In this paper, nonlinear adaptive particle swarm optimization-based gain optimization of sliding mode control systems is presented under matching model uncertainty and random Gaussian external disturbances for magnetic levitation position control systems. The main problems in magnetic levitation control systems are: lack of nonlinear control system; limited position control, i.e., up to 1mm only; convergence rate; accuracy of control; chattering problems in conventional SMC; and lack of considering the effects of system parameters and loading mass changes into account with external disturbance. In this paper, first, a third-order dynamic nonlinear model was created, which includes mechanical (ball position and velocity) and electrical (the current) subsystems with uncertainty in the system parameter and loading mass of 20% each. Secondly, a sliding mode control system for position control systems under both matched model uncertainty (internal) and external Gaussian disturbances is designed for magnetic levitation position tracking systems. Then, the gain of the sliding mode control system is allocated in such a way that the ITSE fitness function is minimal using the adaptive particle swarm optimization technique. The suggested control method, which is based on the combination of the proposed sliding mode control system and adaptive particle swarm optimization, offers control performance with a significant improvement in terms of chattering reduction using arc tangential function, high precision control accuracy, and fast convergence rate. Moreover, a robust optimal SMC controller designed for magnetic levitation systems under both internal and external disturbances is able to reject all disturbances. Finally, the algorithm's performance is demonstrated by model simulation and the proposed control, with simulation results indicating good convergence for given constant and constant plus sinusoidal reference positions.