Design of Genetic Algorithm Based Robust LQG Controller for Active Magnetic Bearing System

Abstract

AbstractThe Active Magnetic Bearing system (AMB) is a mechatronic device which is used to suspend rotating parts of a machine so that they rotate without contact to the stationary part of the machine. AMBs are highly nonlinear, non-minimum phase and inherently unstable. This paper has aimed to obtain a robust optimal state-feedback control system for the stabilization of the AMB System, with the help of Genetic algorithm (GA) as an optimization tool which will solve the tedious manual tuning of the weighting matrices in the design of linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG).The system’s mathematical model has been developed and also the properties of the uncontrolled system have been analyzed. Since AMB is a MIMO system, the interaction of the inputs with the outputs has been analyzed using relative gain array analysis and frequency domain analysis of the system transfer functions. Then, the optimal state feedback controllers have been developed. Here, LQR and LQG controllers are developed.Finally, Comparative analysis between the controllers and between the design methods was carried out. The proposed GA based design methodology has resulted good Performance. In addition, the GA based design has also resulted improvements in robustness of the control systems. As far as gain margin (GM) and phase margin (PM) are concerned GA has resulted increase of 8.0*10–4 db and 6.02*10–3° in GM and PM respectively for LQR. Whereas, in LQG GA has resulted an increase of 3.8*10–5 db and 2.54*10–4°. KeywordsActive magnetic bearingGenetic algorithmLinear quadratic GaussianKalman filter