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An Adaptive LQG Combined With the MRAS-Based LFFC for Motion Control Systems

The aim of this paper is to develop advanced controllers for electromechanical motion systems. A new controller is proposed to take into account the inherent nonlinear disturbances, measurement noise, and variations and uncertainties in process behavior. It consists of a Linear Quadratic Gaussian (LQG) controller and a separate supplementary MRAS-based Learning Feed-Forward Controller (LFFC). Instead of design that is based on a fixed mathematical model of the process, the optimal steady-state filter gain in the Linear Quadratic Estimator (LQE) and the feedback gain in the Linear Quadratic Regulator (LQR) can be determined based on the parameters of the feed-forward part, which follows continuously the process at different load conditions. This will result in “an adaptive LQG combined with the MRAS-based LFFC”. Simulation results demonstrate the potential benefits of the proposed method.