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This paper presents the robust linear quadratic regulator (LQR) control for the uncertain modular multilevel converters (MMCs). The classical LQR control prioritizes optimizing the performance index but lacks the ability to address converter uncertainties. This paper takes into account this limitation by developing a model of the MMC that considers parameter uncertainties. To enable LQR control in uncertain MMC systems, an optimization approach for the performance index employing linear matrix inequality (LMI) technique is proposed. Consequently, a novel robust control strategy for MMCs is established. The real-time performance of this LMI-based LQR control is evaluated against classical LQR control under both nominal and non-nominal conditions and single phase-to-ground fault using Opal-RT system. For this purpose, as the switching function model sufficiently captures all aspects of the MMC’s behavior, the switching function model of the MMC is implemented on the Opal-RT OP4610XG system for real-time emulation of MMC. The results from real-time simulation of the system indicate that the proposed LMI-LQR method offers an advantage in robustness over the classical LQR method in presence of parameters uncertainty.

This paper presents a supervisory control strategy to intelligently split power within a hybrid energy storage system (HESS) of battery and supercapacitor (SC) in electric vehicles (EVs). The intelligent energy management strategy (IEMS) is based on linear parameter-varying model predictive control (LPV-MPC) with the main aim of mitigating battery degradation. The battery RC model is considered in the control-oriented model, where the increment of the battery current is selected as the control variable, resulting in a linear state-space model dependent on the HESS parameters, namely, the battery state-of-charge (SoC) and SC state-of-voltage (SoV). The cost function minimizes the power loss in the battery and squared error of the SoV across the prediction horizon. A new SoV control strategy is proposed based on the upcoming acceleration, providing opportunities for the efficient utilization of the SC and extending the battery lifespan. Constrained optimization is transformed into a quadratic programming problem, which can be easily solved in real time. The superiority of the proposed method in assessing battery degradation was verified by comparing different strategies using five evaluation factors under two drive cycles. Compared with the LPV-MPC, where SoV remained fixed, the proposed method demonstrates reductions of up to 18.82% in the battery current root-mean-square, 30.26% and 25.85% in the discharge and charge peak current, 9.71% in the ampere-hour throughput, 4.78% in the capacity loss, and 29.06% in the energy loss. To validate the real time performance of the proposed method, it is implemented in the real-time digital simulator (RTDS).

This paper presents a control strategy to optimize both the vehicle speed and gearbox position on curved roads with the aim of maximizing fuel economy. Previous studies have focused only on the vehicle speed optimization during cornering. Combining vehicle speed and gearbox position optimization can significantly maximize the energy-saving for an Internal combustion engine (ICE) vehicle. The problem is formulated as two successive optimization problems. In the first one, based on the road map and traffic data, the vehicle’s optimal speed profile is calculated by minimizing the energy spent for the entire curve. In the second one, the gearbox position is optimized for fuel consumption minimization using realistic engine map. The presented control structure’s effectiveness was evaluated through co-simulation of Matlab/Simulink and CarSim in various driving scenarios. The results show that approximately 5.25% to 11.44 % of fuel savings can be achieved compared with a typical driver model.

This paper deals with the quadrotor craft trajectory tracking problem subject to unknown disturbance and actuator constraints. A new adaptive sliding mode control (ASMC) with finite-time convergence characteristics is proposed to guarantee quadrotor hovering in spite of parametric uncertainties and external disturbances. Compared with conventional sliding mode controller (SMC), the proposed adaptive algorithm has been developed for the vehicle altitude and attitude control, with unknown bounded of lumped uncertainties. This approach is based on a dynamical adaptive control law to avoid the overestimation and to ensure the convergence in a finite time. In addition, to solve the actuator saturation problem an auxiliary system is used. Stability analysis is demonstrated via Lyapunov theory, exhibiting that the proposed control strategy ensures that all signals of the closed-loop system are bounded and that the tracking errors are bounded in finite time. Numerical simulations and experimental results are given to illustrate the effectiveness of the proposed method, in which a comparative study with conventional SMC found in literature has been made. Experimental validation of the control strategy is carried-out using the parrot mambo mini-UAV.

Abstract This paper presents an enhanced control strategy for Wind Energy Conversion System (WECS) using Doubly-Fed Induction Generator (DFIG). A robust Super-Twisting (STW) sliding mode control for variable speed wind turbine is developed to produce the optimal aerodynamic torque and improve the dynamic performance of the WECS. The electromagnetic torque of the DFIG is directly tracked using the proposed control to achieve maximum power extraction. The performance and the effectiveness of the STW control strategy are compared to conventional Sliding Mode (SM) and Proportional-Integral (PI) controllers. The proposed STW algorithm shows interesting features in terms of chattering reduction, finite convergence time and robustness against parameters variations and system disturbances.

In this article, an advanced multiphase modular power drive prototype is developed for More Electric Aircraft (MEA). The proposed drive is designed to supply a multi-phase permanent magnet (PM) motor rating 120 kW with 24 slots and 11 pole pairs. The power converter of the drive system is based on Silicon Carbide Metal Oxide Semiconductor Field-Effect Transistor (SiC MOSFET) technology to operate at high voltage, high frequency and low reverse recovery current. Firstly, an experimental characterization test is performed for the selected SiC power module in harsh conditions to evaluate the switching energy losses. Secondly, a finite element thermal analysis based on Ansys-Icepak is accomplished to validate the selected cooling system for the power converter. Thirdly, a co-simulation model is developed using Matlab-Simulink and LTspice® to evaluate the SiC power module impact on the performance of a multiphase drive system at different operating conditions. The results obtained show that the dynamic performance and efficiency of the power drive are significantly improved, which makes the proposed system an excellent candidate for future aircraft applications.