Control of an active magnetic bearing system using swarm intelligence-based optimization techniques

In this manuscript, to control and operate an open loop unstable active magnetic bearing (AMB) system, first a closed loop active magnetic bearing system is proposed. This proposed close-loop contains an algorithm-driven intelligently optimized PID controller, a power amplifier, a position sensor and the fabricated AMB system. Development of a hardware model of proposed AMB system is carried out in laboratory. Using its constructional parameters and after performing some experimental observations, a linearized open loop transfer function is determined by implementing mathematical linearization technique. For a nominal point of operation, the gain variables of PID controller are calculated on the basis of four different evaluation indexes using, firefly algorithm, grasshopper optimization algorithm and artificial bee colony optimization algorithm. These evaluation indexes are: integral of absolute error, integral of squared error, integral of time multiplied absolute error and integral of time multiplied squared error. Further, a comparison among these optimization techniques is realized on three scale: transient state performance, observed statistical data and time taken in execution of algorithm. The efficacy and utility of the optimization techniques are demonstrated by computational calculations and a thorough study of the acquired data.