Tuning of Fractional PID Controllers Using Adaptive Genetic Algorithm for Active Magnetic Bearing System

This paper proposes a novel adaptive genetic algorithm (AGA) for the multi-objective optimization design of a fractional PID controller and applies it to the control of an active magnetic bearing (AMB) system. Different from PID controllers with three constants, the fractional PID controller’s parameters are composed of proportional constant, integral constant, derivative constant, derivative order and integral order. The fractional PID controller is more flexible and gives the possibility of adjusting more carefully the closed-loop system characteristics. However, its design becomes more complex than that of conventional integer order PID controller. An adaptive genetic algorithm is proposed to design the fractional PID controller. The five parameters of the fractional PID controller are selected as parameters to be determined. The dynamic model of an AMB system for axial motion is also presented. The simulation results of this AMB system show that a fractional PID controller designed via the proposed AGA has good performance. Key-Words: Active Magnetic Bearing, Adaptive Genetic Algorithms, Fractional PID Controller