A robust nonlinear predictive controller for permanent-magnet synchronous motors is proposed. The nonlinear predictive control law is formulated by optimizing a novel cost function. A key feature of the proposed control is that it does not require the knowledge of the external perturbation and parameter uncertainties to enhance the robustness. A zero steady-state error is guaranteed by an integral action of the controller. The stability of the closed-loop system is ensured by convergence of the output-tracking error to the origin. The proposed control strategy is verified via simulation and experiment. High performance with respect to speed tracking and current control of the motor has been demonstrated.

A nonlinear predictive control (NPC) scheme in a cascaded structure for a permanent magnet synchronous motor drive is proposed. Taylor series expansion is used to predict the system response over a finite horizon. As NPC cannot remove completely the steady-state error in the presence of mismatched parameters and external perturbation, a disturbance observer is used to estimate the offset caused by parametric uncertainties and the load torque variation. In addition, input constraints (restrictions on the magnitude) are considered in the synthesis of the disturbance observer, resulting in an equivalent cascaded proportional integral action with an antiwindup compensator. The validity of the proposed controller was tested via simulation and experiment. Excellent results were obtained with respect to the speed trajectory tracking, stability, and disturbance rejection.

The main function of the DC-DC converter in a grid-connected photovoltaic (PV) system is to regulate the terminal voltage of the PV arrays to ensure delivering the maximum power to the grid. The purpose of this paper is to design and practically implement a robust continuous-time model predictive control (CTMPC) for a dc-dc boost converter, feeding a three-phase inverter of a grid-connected PV system to regulate the PV output voltage. In CTMPC, the system behavior is predicted based on Taylor series expansion, raising concerns about the prediction accuracy in the presence of parametric uncertainty and unknown external disturbances. To overcome this drawback, a disturbance observer is designed and combined with CTMPC to enhance the steady-state performance in the presence of model uncertainty and unknown disturbance such as the PV current, which varies nonlinearly with the operating point. An interesting feature is that the composite controller reduces to a conventional PI controller plus a predictive term that allows further improvement of the dynamic performance over the whole operating range. The effectiveness of the proposed controller was tested numerically and validated experimentally with the consideration of the grid-connected PV inverter system and its controller.

This paper presents a robust continuous-time model predictive direct power control for doubly fed induction generator (DFIG). The proposed approach uses Taylor series expansion to predict the stator current in the synchronous reference frame over a finite time horizon. The predicted stator current is directly used to compute the required rotor voltage in order to minimize the difference between the actual stator currents and their references over the predictive time. However, as the proposed strategy is sensitive to parameter variations and external disturbances, a disturbance observer is embedded into the control loop to remove the steady-state error of the stator current. It turns out that the steady-state and the transient performances can be identified by simple design parameters. In this paper, the reference of the stator current is directly calculated from the desired stator active and reactive powers without encompassing the parameters of the machine itself. Hence, no extra power control loop is required in the control structure to ensure smooth operation of the DFIG. The feasibility of the proposed strategy is verified by the experimental results of the grid-connected DFIG and satisfactory performances are obtained.

This paper presents a novel design process of decoupled PI current controller for permanent magnet synchronous generator (PMSG)-based wind turbines feeding a grid-tied inverter through a back-to-back converter. Specifically, the design methodology consists of combining disturbance observer-based control (DOBC) with feedback linearization (FBL) technique to ensure nominal transient performance recovery under model uncertainty. By simplifying the DOBC under the feedback linearizing control, it is shown that the composite controller reduces to a decoupled PI current controller plus an additional term that has the main role of recovering the nominal transient performance of the FBL, especially under step changes in the reference. Additionally, an antiwindup compensator arises naturally into the controller when considering the control input saturation to design the DOBC. This permits removal of the effect of the saturation blocks required to limit the control input. The proposed control scheme is implemented and validated through experimentation conducted on 22-pole, 5 kW PMSG. The results revealed that the proposed technique can successfully achieve nominal performance recovery under model uncertainty as well as improved transient performances under control saturation.

This paper presents a robust continuous nonlinear model predictive control (CNMPC) for a grid-connected photovoltaic (PV) inverter system. The objective of the proposed approach is to control the power exchange between the grid and a PV system, while achieving unity power factor operation. As the continuous nonlinear MPC cannot completely remove the steady-state error in the presence of disturbances, the nonlinear disturbance observer-based control is adopted to estimate the offset caused by parametric uncertainties and external perturbation. The stability of the closed-loop system under both nonlinear predictive control and disturbance observer is ensured by convergence of the output-tracking error to the origin. The proposed control strategy is verified using a complete laboratory-scale PV test-bed system consisting of a PV emulator, a boost converter, and a grid-tied inverter. High performance with respect to dc-link voltage tracking, grid current control, disturbance rejection, and unity power factor operation has been demonstrated.