Model predictive control (MPC), manly based on a direct use of an explicit and identifiable model, has been widely used in controller design in different applications both by academia and industry. The reason for such popularity is due to its strong ability for providing high performance electric drive systems, as highly recognized as the most reliable control approach compared with field-oriented control (FOC) and direct torque control (DTC). In general, the MPC has numerous features and advantages, such as direct switching states to the converter without any modulation, online optimization with multivariable control, low current total harmonic distortion, low switching loss, etc. The aim of this paper is to provide a comprehensive review for major development and achievements of the recent progress on the MPC for electrical machines and drives. This review begins with the innovative topologies and operating principles of fundamental MPC, and ends to summary on different advanced MPC algorithms. Typical MPC techniques have been fully adopted to enhance the drive performance of the electrical drives, mainly including finite-set model predictive control (FS-MPC) based on tuning weighting factors, without weighting factors, maximum torque per ampere, low number of switching vectors, and multi voltage vectors in one sample period. Finally, great attention has been paid to the discussion of the new trends and future research topics.

This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications.

In this study, an adaptive nonsingular terminal sliding mode control technique according to the barrier function is designed for the performance improvement and robust stability of nonlinear systems with outdoor disturbances. For this reason, a novel nonlinear sliding surface is presented based on the states of the system. The nonlinear sliding surface forces the states of the system to converge from initial conditions to zero. Subsequently, a non-singular terminal sliding control scheme is advised for the purpose of finite-time stability of the nonlinear switching surface. Finite-time stabilization of the non-singular terminal sliding surface is verified by the Lyapunov theory. For improvement of the system performance against exterior perturbation, the barrier function adaptive technique is employed to estimate the unknown upper bounds of the exterior disturbance. Finally, the advantage and productivity of the recommended control method is investigated based on the simulation results. In the simulation part, the plasma torch jerk chaotic system is considered as a case study, such that the obtained results are given in different scenarios.

This article proposes a robust adaptive supertwisting sliding-mode controller (STSMC) for high-precision IM drive. First, the STSMC is developed for controlling and stabilizing the IM. However, the control performance may be influenced due to exterior disorders along with parameter variations since the IM model is not accurately known during operation. Therefore, it is essential to estimate the lumped disturbance to strength the robustness of the STSMC using system model-based disturbance estimator. Second, to optimize the control performance, a robust adaptive neural-network tracking controller is developed to evade the constraints on knowing the disorders and uncertainties upper bounds. The RANTC combines an STSMC and a recurrent radial basis function network (RRBFN) estimators. The RRBFNs are employed to approximate the IM dynamics along with estimating the optimum super-twisting control algorithms online. The RRBFN training is developed via adaptive particle swarm optimization (APSO) algorithm to attain enhanced performance in less learning iterations, which is the critical issue in real-time applications. Third, to imitate the STSMC algorithms for preserving the robust control characteristics without the necessity of the IM parameters, a direct adaptive intelligent controller (DAIC) based on Takagi–Sugeno–Kang-type recurrent Petri fuzzy-neural-network is developed. Furthermore, the online adaptive laws are derived according to Lyapunov theory, so that the system stability can be assured. The validity of control schemes is verified by simulation and experimentation. The simulation and experimental results confirm the superiority of the STSMC-SMDE, RANTC, and DAIC schemes compared with STSMC irrespective of exterior disorders along with indefinite model uncertainties.

This study proposes an adaptive barrier functions-based non-singular terminal sliding mode control approach for the trajectory tracking of a quadrotor unmanned aerial vehicle subject to bounded uncertainties and input constraints. First, the state-space equations of the six degrees-of-freedom quadrotor system is introduced in the presence of bounded uncertainty and constrained input. Then, a compensation system is designed with the aim of removing the constrained input and leading to high performance. Afterwards, a linear switching surface is defined using the tracking error and virtual control input to guarantee the convergence of the tracking error in the presence of parametric uncertainties and input saturation. Later, a non-singular terminal sliding surface is proposed for fast convergence of the linear switching surface. To eliminate the need for approximating the upper bounds of uncertainties and ensure the fast convergence of the non-singular terminal sliding surface to a pre-specified neighborhood of the origin, we considered an adaptive barrier function scheme. The fast convergence rate of the proposed approach is verified via the Lyapunov stability theory. The accuracy and performance of the proposed approach is assessed using MATLAB/Simulink simulations and robustness analysis using the random number noise.

This paper proposes an adaptive nonlinear disturbance observer (ANDO) for identification and control of a two-axis motion control system driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The proposed control scheme incorporates a feedback linearization controller (FLC), a new double loop self-organizing recurrent wavelet neural network (DLSORWNN) controller, a robust controller and an ${\mathcal H}_\infty$ controller. First, a FLC is designed to stabilize the X-Y table system. Then, a NDO is designed to estimate the nonlinear lumped parameters uncertainties that include the external disturbances, cross-coupled interference and frictional force. However, the X-Y table performance is degraded by the NDO error due to parameter uncertainties. To improve the robustness, the ANDO is designed to attain this purpose. In addition, the robust controller is designed to recover the approximation error of the DLSORWNN while the ${\mathcal H}_\infty$ controller is specified such that the quadratic cost function is minimized and the worst case effect of NDO error must be attenuated below a desired attenuation level. The online adaptive control laws are derived using the Lyapunov stability analysis and ${\mathcal H}_\infty$ control theory, so that the stability of the ANDO can be guaranteed. The experimental results show the improvements in disturbance suppression and parameter uncertainties, which illustrate the superiority of the ANDO control scheme.