This paper proposes a non-cascade -single loop- Direct Speed Control algorithm for surface mounted Permanent Magnet Synchronous Motor (PMSM) fed by Matrix Converter. The proposed method uses Finite Control Set Model Predictive Control (FCS-MPC) to manipulate system speed and currents simultaneously. Also, for better performance of the predictive method, an observer designed to estimate mechanical torque and other uncertain parameters of the mechanical subsystem as a lumped disturbance. Simulation results using Matlab/Simulink demonstrate the performance of proposed algorithm.

Even though some wind turbine manufacturers are no longer active, and are not willing to disclose their intellectual property, their turbines are still in operation. Since the exact values of wind turbine parameters like the aerodynamic model, drive-train model and generator parameters are not always accessible, the design of a controller which does not require prior knowledge of the system parameters can be very useful and effective. As a result, this paper proposes a disturbance observer-based controller to harvest the maximum power from a wind turbine system with fully unknown parameters. To reduce control efforts, a disturbance observer is designed to estimate unknown nonlinear terms caused by unknown model parameters in the presence of unknown control coefficient that uses only the tracking error to estimate nonlinear disturbance. Compared with previously published works, in this paper, both aerodynamic model and drive-train model parameters are assumed to be fully unknown. Closed-loop stability of the proposed controller is analyzed by the Lyapunov stability theorem. To demonstrate the performance of the proposed controller, it is compared with some existing controllers. Comparative simulation results show its effectiveness. Furthermore, although the proposed controller does not require system parameters and includes fewer tuning parameters, it shows the same tracking performance as the other three controllers. The numerical comparative results are listed in a table, which shows that Mean Square Error (MSE) of the proposed controller is 75% less than minimum MSE of the other three controllers of previous works, while its control effort is 1.7% higher than the minimum control effort of the other three.