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Predictive controllers and permanent magnet synchronous motors (PMSMs) got more attention over the past decades thanks to their applicable features. This paper aims to propose and verify a method to design a predictive current controller with consideration of motor characteristics obtained from finite element analysis (FEA).

Permanent magnet motor parameters and its maps can be calculated by means of FEA. The model takes into account magnetic saturation and thermal electro-magnetic properties. For each dq current vector and each position, self and mutual inductances are calculated. Based on co-energy method and fundamentals of coordinate transformation dynamic and static, dq inductances are obtained. These are used in classical and modified dead-beat current controller equations.

To sustain good features of a controller over higher current regions, it is necessary to adapt control law of a dead-beat controller. After its modification, control quality can be superior over classical solution in high saturation regions. The transient simulations of controller and motor give accurate results.

Common predictive current controllers use nominal motor parameters in their equations. The authors proposed a modified dead-beat current controller to improve the control quality. There is no need to apply self-tuning algorithms, and implementation of the controller is not much more complicated than that of the classical controller. Designer of a control system can obtain required data from motor designer; in design process of modern machines such data are often already available. The proposed methodology increases control quality of the presented dead-beat controller.

This paper aims to present a cascaded pseudo derivative feedback (PDF) plus pseudo derivative feedback plus pseudo derivative feedforward (PDFF) controller for a permanent magnet synchronous motor (PMSM) to improve the transient response of the system.

Proportional integral (PI) plus PI controller and the proposed PDF plus PDFF controller are designed, stability analysis is performed using the extended root locus method, and the effect of the damping coefficient is also extensively studied to validate the robustness of the proposed controller.

When compared to a cascaded PI plus PI controller, the proposed control approach has a much shorter settling time for the entire system and a 50% reduction in overshoot in stator current under extensive variations in speed with load disturbance.

The proposed controller is programmed into an FPGA Altera Cyclone II and applied to a 1.5 kW laboratory prototype PMSM drive. The effectiveness of the proposed methods has been demonstrated experimentally throughout a wide variable speed range, from 0 to 157 rad/s at different load conditions.

A new control method is proposed for grid integration of improved hybrid three quasi z source converter (IHTQZSC). The proposed controller provides a constant switching frequency with an improved dynamic response with fewer computations. The proposed constant switching frequency predictive controller (CSF-PC) does not need weighting factors and reduces the complexity of the control circuit.

A single PI controller is intended to control voltage across dc-link by generating the necessary shoot-through duty ratio. The predictive controller produces the modulating signals required to inject the desired grid current. The performance of the proposed controller is validated with MATLAB/Simulink software.

The discrete-time instantaneous model on the grid side in the proposed controller influences the inductor current with minimum ripples. Dynamic response and computational complexity of the converter with the PI controller, finite set model predictive controller (FS-MPC) and the proposed controller are discussed.

The converter belongs to impedance source converters (ISC) family, delivers higher voltage gain in a single-stage power conversion process, extract the energy from the intermittent nature of renewable energy conversion systems. Implementing CSF-PC for ISC is simple, as it has a single PI controller.

Grid integration of high voltage gain IHTQZSC is accomplished with PI, FS-MPC and CSF-PC. Though the FS-MPC exhibits superior dynamic response under input voltage disturbance and grid current variation, total harmonic distortion (THD) in the grid current is high. CSF-PC provides better THD with a good dynamic response with reduced inductor current ripples.

The active power filter with two-level inverter needs a high-rating coupling transformer for high-power applications. This complicates the control and system becomes bulky and expensive. The purpose of this paper is to motivate the use of multilevel inverter as harmonic filter, which eliminates the coupling transformer and allows direct control of the power circuit. The advancement in artificial intelligence (AI) for computation is explored for controller design.

The proposed scheme has a five-level cascaded H-bridge multilevel inverter (CHBMLI) as a harmonic filter. The control scheme includes one neural network controller and two fuzzy logic-based controllers for harmonic extraction, dc capacitor voltage balancing, and compensating current adjustment, respectively. The topology is modeled in MATLAB/SIMULINK and implemented using dSPACE DS1103 interface for experimentation.

The exhaustive simulation and experimental results demonstrate the robustness and effectiveness of the proposed topology and controllers for harmonic minimization for RL/RC load and change in load. The comparison between traditional PI controller and proposed AI-based controller is presented. It indicates that the AI-based controller is fast, dynamic, and adaptive to accommodate the changes in load. The total harmonic distortion obtained by applying AI-based controllers are well within the IEEE519 std. limits.

The simulation of high-power, medium-voltage system is presented and a downscaled prototype is designed and developed for implementation. The laboratory module of CHBMLI-based harmonic filter and AI-based controllers modeled in SIMULINK is executed using dSPACE DS1103 interface through real time workshop.

The dynamics of the PV microgrid (PVMG) system are highly nonlinear and uncertain in nature. It is encountered with parametric uncertainties and disturbances. This system cannot be controlled properly by conventional linear controllers. H⁻ controller and sliding mode controller (SMC) may capable of controlling it with ease. Due to its inherent dynamics, SMC introduces unwanted chattering into the system output waveforms. This paper aims to propose a controller to reduce this chattering.

This paper presents redesign of the SMC by modifying its sliding surface and tuning its parameters by employing water-evaporation-optimization (WEO) based metaheuristic algorithm.

By using this proposed water-evaporation-optimization algorithm-double integral sliding mode controller (WEOA-DISMC), the chattering magnitude is diminished greatly. Further, to examine which controller between H8 controller and proposed WEOA-DISMC performs better in both normal and uncertain situations, a comparative analysis has been made in this paper. The considered comparison parameters are reference tracking, disturbance rejection and robust stability.

WEO tuned DISMC for PVMG system is the contribution.

The paper aims to propose an adaptive and robust on‐line trained neuro‐fuzzy current controller based on indirect field oriented control (IFOC) for the current control of multilevel inverter fed induction motor (IM).

Torque current of IM is controlled with Sugeno type neuro‐fuzzy controller (NFC) which has the ability of self tuning against parameter variations and load disturbance. Input variables of the neuro‐fuzzy current controller are chosen error and integral of error in order to eliminate steady state error. The consequent parameters of neuro‐fuzzy current controller are trained on‐line through backpropagation learning algorithm.

The validity of proposed current control algorithm is shown with experimental results carried out under different speed commands, parameter variations and load disturbances. The experimental results show that control performance of NFC in the current control of IMs is satisfactory because of its adaptive and robust structure.

This paper presents the design of an on‐line trained neuro‐fuzzy current control to improve the current control performance. The performance of the current controller largely depends on using converter systems. In this study, a multilevel inverter is used to obtain less harmonic distortion and near sinusoidal form of output voltage and current waveforms of the converter.

In the paper an application of fuzzy logic controller (FLC) for control of thyristor DC drive is presented. During synthesis of the FLC a robustness against variation of structure of the current control plant was taken into account. Comparison between the fuzzy control system and an ordinary digital control system, carried out by simulation method, proved that FLC as a robust controller gives better performance in the range where non‐linearity and parameter variation is observed. The simulation results were confirmed by the laboratory experiment.

The purpose of this paper is to integrate the function of a speed controller for induction motor (IM) drive, such as the speed PI controller, the current vector controller, the slip speed estimator, the space vector pulse width modulation scheme, the quadrature encoder pulse, and analog to digital converter interface circuit, etc. into one field programmable gate array (FPGA).

First, the mathematical modeling of an IM drive, the field-oriented control algorithm, and PI controller are derived. Second, the very high speed IC hardware description language (VHDL) is adopted to describe the behavior of the algorithms above. Third, based on electronic design automation simulator link, a co-simulation work constructed by ModelSim and Simulink is applied to verify the proposed VHDL code for the speed controller intellectual properties (IP). Finally, the developed VHDL code will be downloaded to the FPGA for further control the IM drive.

In realization aspect, it only needs 5,590 LEs, 196,608 RAM bits, and 14 embedded 9-bit multipliers in FPGA to build up a speed control IP. In computational power aspect, the operation time to complete the computation of the PI controller, the slip speed estimator, the current vector controller are only 0.28 μs, 0.72 μs, and 0.96 μs, respectively.

Fast computation in FPGA can speed up the speed response of IM drive system to increase the running performance.

This is the first time to realize all the function of a speed controller for IM drive within one FPGA.

The principal intention behind the activity is to regulate the speed, current and commutation of the brushless DC (BLDC) motor. Thereby, the authors can control the torque.

In order to regulate the current and speed of the motor, the Multi-resolution PID (MRPID) controller is proposed. The altered Landsman converter is utilized in this proposed suppression circuit, and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor. The adaptive neuro-fuzzy inference system-elephant herding optimization (ANFIS-EHO) calculation mirrors the conduct of the procreant framework in families.

Brushless DC motor's dynamic properties are created, noticed and examined by MATLAB/Simulink model. The performance will be compared with existing genetic algorithms.

The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.

This paper aims to propose a robust cascaded controller based on proportional-integral (PI) and continuous sliding mode control.

Cascaded control structure is an attractive control scheme for DC-DC power converters. It has a two-loop structure where the outer loop contains PI controller and the inner loop uses sliding mode control (SMC). This structure thus combines the merits of both the control schemes. However, there are some issues that have prohibited its adoption in industry, the discontinuous nature of SMC which leads to variable switching frequency operation and is hard to realize practically. This paper attempts to overcome this issue by changing the discontinuous functionality of SMC to continuous by utilizing the concept of equivalent control.

The robustness of the controller designed is verified by considering various cases, namely, ideal case with no uncertainties, sudden variation of input supply voltage, load resistance, reference voltage, circuit-parameters and for noise disturbance. The controller effectiveness is validated by simulating the DC-DC boost and Cuk converters in SimPowerSystems toolbox of MATLAB/Simulink. It is shown that the performance of the proposed controller is satisfactory, and both reference output voltage and inductor current are tracked with little or no sensitivity to disturbances.

The results for various scenarios are interesting and show that the controller works quite satisfactorily for all the simulated uncertainties.