Rachid Errouissi

Robust Nonlinear Predictive Controller for Permanent-Magnet Synchronous Motors With an Optimized Cost Function

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.

Robust Cascaded Nonlinear Predictive Control of a Permanent Magnet Synchronous Motor With Antiwindup Compensator

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.

Experimental Validation of a Robust Continuous Nonlinear Model Predictive Control Based Grid-Interlinked Photovoltaic Inverter

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.

Aggregated domestic electric water heater control - building on smart grid infrastructure

A pilot project is underway in New Brunswick, Canada, to demonstrate direct control of thermostatically controlled loads as a means of providing ancillary services. This paper will focus on one of the load classes, aggregated control of domestic electric water heaters (DEWHs) as a means of shifting load profiles. The approach not only strives to satisfy utility level objectives by curtailing individual DEWHs in a strategic manner but also takes account of the thermal dynamics of individual heaters and estimates of hot water usage in an attempt to maintain customer comfort and satisfaction. The aggregated load controller is unique in its topology and operation, consisting of an individual load extractor, a water demand model, and a temperature estimator for each DEWH, together with a reserve capacity forecast unit and aggregated load control logic. Details of the aggregated load controller are developed, the relationship to the system operator is described and a summary of preliminary results is provided.

A Novel Predictive Direct Torque Controller for Induction Motor Drives

A predictive direct torque controller (PDTC) for induction motors (IM) is proposed. It combines the direct torque control (DTC) and the predictive control (PC), and uses a predictive switching table to enhance the overall performance of the motor. A new type of PC is adopted for speed regulation with the use of a load torque observer, the torque being considered an unknown perturbation. A Kalman filter (KF) is used for reliable flux estimation. The validity of the proposed controller was experimentally confirmed on a rapid control prototyping station. The obtained results have proven superiority of the proposed control with respect to the speed trajectory tracking, torque and flux dynamic responses, and disturbance rejection. Also, a lower current distortion was observed with PDTC, in comparison with the regular DTC, due to increased mean inverter switching frequency.

Identification and Estimation for Electric Water Heaters in Direct Load Control Programs

PowerShift-Atlantic (PSA) is a pilot project lead by Canadian Maritime utilities that demonstrates direct load control strategies for up to 20 MW of commercial and residential loads for the purpose of balancing the intermittency of renewable generation and supporting demand-side management programs. On the residential side, domestic electric water heaters (DEWHs) form a significant end use class. The ability to accurately estimate and predict the state of individual end use devices allows aggregated control systems to better ensure end-use performance and comfort levels. This paper presents a methodology for estimating and predicting the state of individual DEWHs from models of their thermodynamics and water consumption that are derived under two scenarios: 1) when measurements of both power consumption and water temperature are available; and 2) when only measurements of power consumption are available. The proposed methodology was implemented as part of the PSA pilot project for the DEWH load class to simulate the behavior of the load in presence of the controller and evaluate the performance of the controller. Experimental results show that the model and water usage profile mimic the actual behavior of DEWHs, and can predict the future power consumption when the thermostatic control of a DEWH is interrupted as part of a load control strategy.

Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control

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.

Robust nonlinear generalised predictive control for a class of uncertain nonlinear systems via an integral sliding mode approach

ABSTRACT In this paper, a robust nonlinear generalised predictive control (GPC) method is proposed by combining an integral sliding mode approach. The composite controller can guarantee zero steady-state error for a class of uncertain nonlinear systems in the presence of both matched and unmatched disturbances. Indeed, it is well known that the traditional GPC based on Taylor series expansion cannot completely reject unknown disturbance and achieve offset-free tracking performance. To deal with this problem, the existing approaches are enhanced by avoiding the use of the disturbance observer and modifying the gain function of the nonlinear integral sliding surface. This modified strategy appears to be more capable of achieving both the disturbance rejection and the nominal prescribed specifications for matched disturbance. Simulation results demonstrate the effectiveness of the proposed approach.

A Robust Continuous-Time MPC of a DC–DC Boost Converter Interfaced With a Grid-Connected Photovoltaic System

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.

Aggregate Load Forecast with Payback Model of the Electric Water Heaters for a Direct Load Control Program

Domestic electric water heaters (DEWH) hold a large share of residential load in North America. The aggregated load profile of electric water heaters follows a similar pattern to the total household load profile, which means that changing the profile of DEWH load can significantly change the shape of the aggregated load profile. To change the load profile, the controller requires an estimation of future load profile and the payback effect of the control action on the forecasted load. This paper presents a load forecast module that uses a Kalman filtered neural network to forecast the aggregated controllable load combined with a statistical payback model to identify the impact of the control action on the load forecast. The proposed method was used by the University of New Brunswick as part of a pilot project named Power Shift Atlantic that aims to provide more than 11MW of ancillary services by controlling more than 1200 controllable loads. The experimental results on the real pilot project shows that the forecast method can be adapted with the dynamic behaviour of the customers. The payback model was also verified by applying various control signals on the pilot project.