Intelligent H2/H∞ Robust Control of an Active Magnetic Bearings System


Robust controller design requires a proper definition of uncertainty bounds. These uncertainty bounds are commonly selected randomly and conservatively for certain stability, without regard for controller performance. This issue becomes critically important for multivariable systems with high nonlinearities, as in Active Magnetic Bearings (AMB) System. Flexibility and advanced learning abilities of intelligent techniques make them appealing for uncertainty estimation. The aim of this paper is to describe the development of robust H2/H∞ controller for AMB based on intelligent estimation of uncertainty bounds using Adaptive Neuro Fuzzy Inference System (ANFIS). Simulation results reveal that the robust controller design objectives of wide bandwidth and improved performance are satisfied for a wide range of frequency variations. It can be concluded that the intelligent uncertainty weighting functions can precisely compensate for the effects of modelling errors and nonlinearities in the system.