Particle Swarm Optimized Command Filtered Backstepping Control for an Active Magnetic Bearing System*
This paper discusses the position control problem for the active magnetic bearing (AMB) suspension system under external disturbance. The command filtered approach is applied for the controller design to reduce the complexity for repeatedly calculating the analytic derivatives of the virtual control with less computation effort compared to standard backstepping. The adaptive law and robust terms are obtained through Lyapunov theory and asymptotic stability can be guaranteed for the filtered tracking errors. The particle swarm optimization (PSO) algorithm is adopted to adjust the control parameters with guaranteed dynamic performance. Finally, the simulation results illustrate the tracking performance can be made arbitrarily close to the solution of standard backstepping.
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Particle Swarm Optimized Command Filtered Backstepping Control for an Active Magnetic Bearing System*
Semantic Scholar · Engineering · 2018
Abstract
This paper discusses the position control problem for the active magnetic bearing (AMB) suspension system under external disturbance. The command filtered approach is applied for the controller design to reduce the complexity for repeatedly calculating the analytic derivatives of the virtual control with less computation effort compared to standard backstepping. The adaptive law and robust terms are obtained through Lyapunov theory and asymptotic stability can be guaranteed for the filtered tracking errors. The particle swarm optimization (PSO) algorithm is adopted to adjust the control parameters with guaranteed dynamic performance. Finally, the simulation results illustrate the tracking performance can be made arbitrarily close to the solution of standard backstepping.