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This paper aims to study advanced controls of two-mass wind turbine (WT) system based on a doubly fed induction generator (DFIG) to improve their performance. This work has two main objectives, which are the maximum aerodynamic power extraction from wind energy and indirect power control of DFIG. In the matter of two mass wind turbine control, the tip speed ratio (TSR) method based on maximum power point tracking algorithm (MPPT) is used to extract maximum power from kinetic energy of wind by implementing a high order sliding mode controller (HOSMC) to control the generator speed. Afterwards, the active and reactive power of DFIG is controlled by using HOSMC and artificial neural network (ANN) to evaluate their impact on the performance of DFIG wind turbine system. The dataset obtained from the SMC is used to train the proposed ANN architecture using a back-propagation algorithm. After training, the HOSMC is replaced with a constructed ANN design into DFIG power control loops. The obtained results illustrate that the two-mass WT system extracts maximum power from the wind energy by obtaining maximum coefficient power, and optimal TSR, additionally, the generator speed is well tracked the reference value. Furthermore, the ANN and HOSM controllers highlight effectiveness in the DFIG indirect power control by tracking the references values of the active and reactive power. However, ANN controller yielded superior results compared to the others by significantly reducing overshoot and improving time response, which can improve the performance of two-mass WT system.

Unmanned aerial vehicles (UAVs) have seen a rise in use during the last few years. Such aircrafts are now a convenient way to complete dangerous, dirty, and tedious tasks. Given that their operation involves a control problem which is non-linear and coupled, it is difficult to analyse. This paper presents the modeling and control of a fixed-wing unmanned aircraft as a contribution to this field. The system’s flight dynamics is derived using Newton’s second law of motion. The system is designed to have a non-linear Proportional Integral Derivative (NPID) controller and a higher-order sliding mode controller (HOSMC). When simulating the system using MATLAB Simulink software, an external disturbance was added to test the robustness of the controllers. Five performance indices which include mean square error (MSE), integral time square error (ITSE), integral absolute error (IAE), integral time absolute error (ITAE), and integral square error (ISE), were used to compare the controllers performance. These indices are used to provide a numerical assessment of the two controllers’ performance. The outcomes demonstrate that the roll, pitch, and yaw states performed better than the super-twisting sliding mode controller. On the airspeed control, the non-linear PID performed better than the super-twisting sliding mode controller.

Vertical Takeoff and Landing (VTOL) aircraft present significant control challenges due to their nonlinear, strongly coupled dynamics and sensitivity to disturbances, particularly during hover–forward flight transitions. This study focuses on developing a Higher-Order Sliding Mode Control (HOSMC) strategy, based on the Super-Twisting algorithm, to enhance trajectory tracking accuracy and robustness for small-scale VTOL platforms. The proposed method was designed with tailored sliding variables and refined gain tuning, and its performance was validated through extensive simulations under realistic urban flight conditions, including Dryden-model turbulence, thrust perturbations, and multi-phase trajectories. Results show that HOSMC achieved superior tracking performance, with a maximum error of 0.038 m and an RMS error of 0.017 m, representing a 61% improvement in RMS error over SMC and a 50% reduction in maximum error compared to ASMC, while also delivering smoother control inputs, a thrust-to-weight ratio of 0.17, and energy consumption as low as 0.1866 kWh/h. These findings confirm that HOSMC not only suppresses chattering but also improves energy efficiency by up to 20% compared to PID, underscoring its potential as a robust and efficient control solution for autonomous VTOL operations in urban air mobility (UAM) applications.

This paper introduces a robust proportional integral derivative higher-order sliding mode controller (PID-HOSMC) based on a double power reaching law (DPRL) to enhance large-signal stability in DC microgrids. The microgrid integrates a solar photovoltaic (SPV) system, an energy storage system (ESS), and DC loads. Efficient DC-DC converters, including bidirectional and boost converters, are employed to maintain a constant voltage level despite the lower SPV output power. An artificial neural network (ANN) generates the optimal reference voltage for the SPV system. The dynamical model, which incorporates external disturbances, is initially developed and based on this model, and the PID-HOSMC is designed to control output power by generating switching gate pulses. Afterwards, Lyapunov stability theory is used to demonstrate the model’s closed-loop stability, and theoretical analysis indicates that the controller can converge tracking errors to zero within a finite time frame. Finally, a comparative numerical simulation result is presented, demonstrating that the proposed controller exhibits a 58% improvement in settling time and an 82% improvement in overshoot compared to the existing controller. Experimental validation using processor-in-the-loop (PIL) confirms the proposed controller’s performance on a real-time platform.

In this paper, a comprehensive review of several strategies applied to improve the Low Voltage Ride-Through (LVRT) capability is presented for grid-connected wind-turbine-driven Doubly Fed Induction Generator (DFIG). Usually, themost proposed LVRT solutions in the literature based on: hardware solutions, which increase the system costs and software solutions, which increase the control system complexity. Therefore, the main objective of this study is to take into account grid requirements over LVRT performance under grid fault conditions using software solution based on Higher Order-Sliding Mode Control (HOSMC). Effectively, this control strategy is proposed to overcome the chattering problem and the injected stator current harmonics into the grid of the classical First Order Sliding Mode (FOSMC). Furthermore, the resultant HOSMC methodology is relatively simple; where, the online computational cost and time are considerably reduced. The LVRT capacity and effectiveness of the proposed control method, compared to the conventional FOSMC, are validated by time-domain simulation studies under Matlab on a 1.5MW wind-turbine-driven DFIG.

In this paper, a current controlled-hybrid power compensator (CC-HPC) is presented to reduce the effect of input current harmonics on battery chargers. Passive filters have significant power loss and degrade system frequency due to excessive harmonic attenuation. The proposed system integrates the Higher Order Sliding Mode Controller (HOSMC) with a generalized form of p-q power theory and a Time Series - Artificial Neural Network (TS-ANN) is used to produce compensating reference current for a three-phase system and generates DC link inductor current. Switching pulses to Current Controlled-Active Power Compensator (CC-APC) switches are generated using a reference compensated signal. The development of CC-HPC and its control approach helps to reduce the overall harmonic distortion of the supply current used in battery chargers are the main contributions of the proposed system. HOSMC is a robust and adaptable controller that tracks reference current without causing chattering is the significant advantage of the proposed method. The control algorithm is designed in MATLAB/SIMULINK software for various load conditions and the experimental setup has been developed for rectified fed RC load using TS-ANN. The filtering process of CC-HPC can maintain the harmonic distortion of supply current within the IEEE 519-2014 standard.

This paper presents direct power control (DPC) strategies using the super-twisting sliding mode control (STSMC) applied to active and reactive power control of a doubly-fed induction generator (DFIG) supplied by a space vector modulation inverter for wind turbine system. Then, a control STSMC-DPC and SVM strategies are applied. The active and reactive powers that are generated by the DFIG will be decoupled by the orientation of the stator flux and controlled by super-twisting sliding mode control. Its simulated performance is then compared with conventional sliding mode control. The test of robustness of the controllers against machine parameters uncertainty will be tackled, and the simulations will be presented. Simulation results of the proposed controller (SMC-DPC) and (STSMC-DPC) scheme are compared for various step changes in the active and reactive power. This approach super-twisting sliding mode control is validated using the Matlab/Simulink software and the results of the simulation can prove the excellent performance of this control in terms of improving the quality of the energy supplied to the electricity grid.

U ovom radu, prikazan je sveobuhvatan pregled strategija primjenjenih za poboljšanje sposobnosti rada tijekom prolaznih smetnji niskog napona mreže za vjetroagregat s dvostruko napajanim asinkronim generatorom (DFIG). Uobičajeno, većina predloženih LVRT rješenja u literaturi temelji se na: hardverskim rješenjima, što povećava troškove sustava i softverskih rješenja te složenost sustava upravljanja. Stoga je glavni cilj ovog istraživanja da se uključuje i zahtjevi mreže kroz ponašanje LVRTa u uvjetima mrežnih kvarova korištenjem softverskog rješenja zasnovanoga na kliznom režimu rada višeg reda (HOSMC). Efektivno, ova upravljačka strategija je predložena kako bi se prevladali oscilacije i ubacivanje harmonika struje statora u mrežu klasičnim metodama kliznog režima rada prvog reda (FOSMC). Nadalje, rezultantna metodologija HOSMC je relativno jednostavna; gdje su online računski zahtjevi i potrebno vrijeme značajno smanjeni. LVRT kapacitet i učinkovitost predložene metode upravljanja, u usporedbi s konvencionalnim FOSMC potvrđene su simulacijama u vremenskoj domeni u Matlabu na 1.5 MW vjetroagregatu s DFIG-om.

This chapter presents a comparative experimental study between nonlinear robust sensorless induction motor (IM) controllers, taking into account different operation conditions and under parametric uncertainties. The nonlinear controllers considered in the chapter are (a) an integral backstepping control (IBC) and (b) a high‐order sliding‐mode control (HOSMC). These control schemes are designed to improve the performance of the sensorless IM, at different operation conditions. The first section of the chapter is devoted to the description of the IM model and the problem formulation. The robust integral backstepping is developed in the second section. The third section presents the HOSMC. In the fourth section, to implement the proposed controller and to estimate the nonmeasured variables, an adaptive interconnected observer design is introduced. The experimental results are given and discussed related with the performance of the control schemes in the fifth section. The final section draws some conclusions.