dSPACE DS1104 Research Articles

Improving photovoltaic water pumping system performance with ANN-based direct torque control using real-time simulation

Photovoltaic Water Pumping Systems (PVWPS) have become increasingly important as a renewable energy solution in rural areas, providing energy independence, cost savings, and environmental friendliness. This system has two main controllers. The first controller is employed to maximize power extraction from the PV array by controlling the duty ratio of the DC-DC boost converter. The second controller is responsible for regulating the operation of the induction motor through the switching pulses of the Voltage Source Inverter (VSI). These two controllers play an essential role in the system, which increases efficiency and performance. Therefore, the innovative aspect of this work consists of introducing Artificial Neural Networks (ANNs) based on each PVWPS controller. On the one hand, ANN-based MPPT is implemented to ensure optimal performance of the PV array under varying irradiation levels. On the other hand, to overcome the defects and problems caused by Direct Torque Control (DTC), such as flux and torque ripples, high switching frequency, and challenges at low speeds, an ANN-based DTC is proposed in which each of the hysteresis comparators, switching table, and speed controller in the DTC are replaced by ANN controllers. The PVWPS based on the proposed controls is thoroughly modeled and simulated using MATLAB/Simulink software and validated using dSPACE DS1104 Board. The results demonstrate significant improvements, including a 75.51% reduction in flux ripples, a 77.5% reduction in torque ripples, a 44.79% improvement in response time, and an increase in the water quantity. Furthermore, the Real-Time simulation and visualization obtained are consistent with the simulation outcomes.

Experimental evaluation of DC-DC buck converter based on adaptive fuzzy fast terminal synergetic controller

This study suggests an enhanced version of the adaptive fuzzy fast terminal synergetic controller (AF-FTSC) for controlling the uncertain DC/DC buck converter based on the synergetic theory of control (STC) and newly developed terminal attractor technique (TAT). The benefits of the proposed SC algorithm involve the features of finite-time convergence, unaffected by parameter variations, and chattering-free phenomenon. A type-1 fuzzy logic system (T1-FLS) make the considered controller more robust and is utilized to estimate the undefined converter nonlinear dynamics without resorting to the usual linearization and simplifications of the converter model. Taking a switching DC-DC buck converter as a demonstration, the suggested AF-FTSC is thoroughly analyzed and executed on a dSPACE ds1103 controller board. The outcomes of the experiment confirm the competence and applicability of the suggested regulator.

Optimization of the DTC control for doubly-fed induction motor using a PID based-genetic algorithm: Experimental validation on the dSPACE 1104 board

This article presents a very interesting approach to Direct Torque Control (DTC) for a doubly-fed induction motor utilizing two voltage-source inverters. The speed control of this system is achieved using a proportional-integral-derivative (PID) controller optimized by a genetic algorithm. Although the conventional DTC method offers many advantages, such as effective and dynamic control, robustness, ease of use, and impressive results, it also has drawbacks, including fluctuations in electromagnetic torque and variable switching frequencies, which lead to vibrations and accelerated aging of the machine. The purpose of this article is to improve torque control based on the direct method (DTC) and to address these limitations. To achieve this, a new control strategy called Genetic Algorithm-based DTC (GA-DTC) is proposed. This strategy integrates the optimized PID controller and is implemented across the entire operational range of the system. The entire system is validated using the MATLAB/Simulink environment to analyze the machine’s characteristics, its transient behavior, and its performance in steady state. This integration leads to a notable improvement in the machine’s performance, particularly in tracking speed and torque set points, reducing response times, and decreasing overshoot. Experimental validation is carried out using a 1.5 kW rotating electrical machine (DFIM-DCM) connected to a resistive load. The experiments are conducted using the DSPACE DS1104 experimental system, and the system’s behavior is tested under various operating conditions. The results obtained show the evolution of speed, torque, as well as stator and rotor currents. According to these results, the motor’s performance has been improved: response time has decreased, settling time has increased, and torque ripple has been reduced.

Design and experimental validation of fault diagnosis and fault tolerant control in PMSG drives for wind energy conversion systems

This study explores a method for detecting and isolating faults in wind power conversion systems that use a permanent magnet synchronous generator (PMSG). The goal is to enhance the reliability of the inverter by implementing an fault detection method and fault-tolerant control. The converter incorporates an extra leg with two switches that can be quickly connected in place of a malfunctioning leg, ensuring continued operation. Our fault detection and isolation module employs a threshold-based approach, utilizing real-time signal measurements to identify anomalies exceeding predefined limits. The experimental validation of this module is conducted on a high-performance dSpace DS1104 controller board using MATLAB Simulink. Finally, a real-world experiment was conducted to confirm the effectiveness of the proposed fault detection and isolation (FDI) algorithm. The results successfully demonstrated the algorithm’s ability to detect and isolate switch faults.

Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation

ABSTRACTThis article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network‐based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady‐state performance. The stability of the entire closed‐loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start‐up, step‐reference tracking, and external step‐load torque disturbances. The real‐time experimentation is conducted on a laboratory prototype of power converter‐driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

High-efficiency MPPT strategy for PV Systems: Ripple-free precision with comprehensive simulation and experimental validation

This paper presents a newly developed maximum power point (MPP) tracking algorithm (MPPT) to boost the tracking performance of solar photovoltaic (PV) systems. By functioning PV arrays at their MPP and eliminating the ripple problem in the converter's output, the newly developed strategy can markedly improve the precision of tracking and overcome the issues faced by many existing algorithms. The proposed strategy uses an MPP voltage boundary to control the PV voltage and directly generate the required duty cycle using a mathematic expression, resulting in a high convergence speed, drift problem avoidance, and zero MPP fluctuations. To prove and validate the robustness of tracking of the suggested MPPT strategy, both simulation and experiment validations based on the MATLAB/Simulink and dSPACE DS1104 board platforms, respectively, are carried out under swift variations in solar irradiance. The effectiveness of the proposed approach is evaluated by comparing it to the InC algorithm in terms of tracking accuracy. The results from both simulations and experiments demonstrate that the suggested strategy surpasses the InC approach in multiple aspects. It exhibits a shorter response time, eliminates the ripple problem, and achieves a superior tracking efficiency of 99.60 %.

Comparative study of real-time photovoltaic fault diagnosis using artificial intelligence: Fuzzy logic and neural network approaches

ABSTRACT Solar photovoltaic (PV) systems are becoming increasingly popular for renewable energy production. However, due to environmental and operational conditions, various faults can occur in PV modules, which can cause a significant reduction in system performance. This study suggests employing two distinct approaches, Fuzzy Logic (FL) and Artificial Neural Networks (ANN), to diagnose defects in photovoltaic (PV) systems. Both the simulation and measuring are used to compare the performance of various strategies. The simulation was performed using MATLAB/Simulink, while the experiment was carried out using the dSPACE DS1104 platform to apply the diagnostic model built in the Matlab/Simulink® program. The suggested approaches have been verified using an experimental database of climatic and electrical attributes obtained from a photovoltaic (PV) panel situated at the LGEB Laboratory of the University of Biskra in Algeria. For six single- and multi-fault types, partial shading, soiling, SC of one by-pass diode, SC of two by-pass diodes, by-pass diode shunted and shading & by-pass diode disconnected. A dSPACE DS1104 platform which shows the results and informs the user about the state of the PV panel has also been developed. The results of the simulation show that both FL and ANN methods can accurately detect and diagnose the six types of faults by 100% and 99.7%, respectively. The study demonstrates the effectiveness of both FL and ANN methods for PV fault diagnosis. However, in the experimental tests, the ANN method shows better performance in terms of accuracy compared to FL by 99.6% and 99.2%, respectively, and speed takes only 1.04s, while FL consumes 3.02s and could be a more practical solution for real-time surveillance PV fault diagnosis

A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm

To incorporate Local Energy Management System (LEMS) based Tertiary Controller (TC) operation with multiple Photovoltaic based Distributed Generations (PV-DGs) level Primary Controller/ PC: Independent DG Controllers (IDGC) more adequately, a new representation learning (Symmetrical Input-Output weight Encoded Data Compression/ SIODC, with Exponential Expansion based Random Vector Functional Link Network towards Softmax layer’s Control Reference Estimation/ ExRVFLN-SoCRE) based MPPT controller is proposed in this paper in terms of Secondary Controllers (SCs). A new Hybrid LEMS (HyLEMS) is presented towards the proposed Deep Neural Network based SCs (i.e. DNN-SCs) in a distributed (SCdist), as well as centralized (SCcent) manner, to ease the computational, and communication requirements. To investigate that multiple PV-DGs based duty cycle (kth instant Control References/CRs estimation) controllers are considered here with auxiliary Battery Energy Storage Systems (BESS), and AC utility, integrated to Common DC feeder in terms of DC-DC converter dynamics. To avoid Control Reference Estimation Error (CREE) due to initial randomization, optimization subroutines are incorporated for SIODC by generalized semi-supervised learning, and for ExRVFLN-SoCRE by Lagrange multiplier weighted, rms based cost function. The proposed control performance is verified in MATLAB Simulink® based average modeling, and validated through dSPACE DS1104 based RTI with multi-PV (emulators) test-bench as well.

Kinematic control in a four‐wheeled Mecanum mobile robot for trajectory tracking

AbstractThe challenges of the modern world require mobile robots with the ability to navigate in congested environments with high levels of manoeuvrability. Therefore, the Mecanum wheel may be viable for addressing this challenge. This paper presents the experimental results of a kinematic control strategy, which involves considering the dynamic model as a black box, with only the input and output signals being known. To do this, high‐level and low‐level controllers are formulated and explained. The high level aims to control the desired position of the mobile robot, which can be useful for navigation tasks. On the other hand, low‐level control involves nested controllers to regulate the speed of the mobile robot wheels. Both levels are related and computed through pure kinematics transformations with a dSPACE ds1103 card and MATLAB/Simulink software. In total, 120 experiments were conducted to determine the repeatability of the tests, using the combination of three widely explored control techniques in the literature: proportional‐integral‐derivative (PID), PID plus sliding modes, and PID plus quasi‐sliding modes. The experiments conducted are described in detail, and the results are analysed using statistical indices based on the RMS error and percentage improvement.

Dspace implementation of real-time selective harmonics elimination technique using modified carrier on three phase inverter

Introduction. In the contemporary world, alternative electrical energy has become an integral part of our daily existence, with the majority of our electrical materials, electronic devices, and industrial equipment relying on this energy source. Consequently, ensuring the quality of the electrical signal obtained is of paramount importance in the process of converting and distributing electrical energy. The improvement of the output voltage inverter can be achieved through adjustments to the inverter structure or by refining the control strategy. The novelty of the presented research lies in an innovative approach that employs real-time modulation for efficient control over the reduction of harmonics, alongside managing the fundamental component. This approach is applicable to both bipolar and unipolar configurations, featuring quarter-wave and half-wave symmetries. Purpose. Employing this modulation strategy aims to enhance the durability of the switching components and enhance the voltage output of the inverter. Methods. The methodology is founded on a sine-sine modulation as its foundational model. It constitutes an inventive pulse width modulation technique in which a reference sinusoidal waveform, operating at the desired signal frequency, is compared to a modified carrier signal with an identical time period as the reference signal. Results. This paper introduces a broader and more comprehensive approach, alongside specific solutions, for the control and mitigation of harmonics in three phase voltage source inverters. The proposed method offers precise control over the reduction of harmonics and the fundamental component, and it can be implemented in extensive power electronic converters. Practical value. To assess the effectiveness of the given control approach, we conducted simulations as well as real-time implementation employing Dspace DS1104 controller board. The outcomes were highly favorable, confirming the effectiveness and validity of the suggested control algorithm. References 15, tables 4, figures 7.

Enhancing Photovoltaic-Powered DC Shunt Motor Performance for Water Pumping through Fuzzy Logic Optimization

This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module’s operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios.

Hysteresis based predictive torque control without weighting factors for induction motor drives

AbstractThis work proposes a weighting‐factor‐free finite control set predictive torque control (FCS‐PTC) for induction motor drives by incorporating a hysteresis controller to eliminate stator flux error from the cost function. The tuning of the weighting factor coefficient in FCS‐PTC is critical for improved drive performance. Conventionally, the tuning is based on an intuitive understanding of the dynamical system and empirical methods, which may not yield optimal results. The proposed method regulates the flux by incorporating a separate two‐level hysteresis controller and torque by the conventional cost function‐based PTC. The cost function uses torque error only and employs a reduced number of voltage vectors. The reduction of voltage vectors is obtained through the flux hysteresis output and sector determination. The proposed work is implemented experimentally on the dSpace DS1104 controller board for a two‐level three, three‐phase voltage source inverter‐fed induction motor drive. The experimental results validate the effectiveness of the proposed work under different drive tests compared to conventional PTC and direct torque control (DTC). The obtained results show comparable dynamic performance of the drive with lower computational burden than conventional PTC. Along with computational benefit, the proposed work demonstrates improved total harmonic distortion (THD) at low‐speed regions due to the absence of a weighting factor in the cost function.

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system’s robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology.

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

Smart IoT Irrigation System Based on Fuzzy Logic, LoRa, and Cloud Integration

Natural resources must be administered efficiently to reduce the human footprint and ensure the sustainability of the planet. Water is one of the most essential resources in agriculture. Modern information technologies are being introduced in agriculture to improve the performance of agricultural processes while optimizing water usage. In this scenario, artificial intelligence techniques may become a very powerful tool to improve efficiency. The introduction of the edge/fog/cloud paradigms, already adopted in other domains, may help to organize the services involved in complex agricultural applications. This article proposes the combination of several modern technologies to improve the management of hydrological resources and reduce water waste. The selected technologies are (1) fuzzy logic, used for control tasks since it adapts very well to the nonlinear nature of the agricultural processes, and (2) long range (LoRa) technology, suitable for establishing large distance links among the field devices (sensors and actuators) and the process controllers, executed in a centralized way. The presented approach has been validated in the laboratory by means of a control scheme aimed at achieving an adequate moisture level in the soil. The control algorithm, based on fuzzy logic, can use the weather forecast, obtained as a cloud service, to reduce water consumption. For testing purposes, the dynamics of the water balance model of the soil were implemented as hardware in the loop, executed in a dSPACE DS1104. Experiments proved the viability of the presented approach since the continuous space state output controller achieved a water loss reduction of 23.1% over a 4-day experiment length compared to a traditional on/off controller. The introduction of cloud services for weather forecasting improved the water reduction by achieving an additional reduction of 4.07% in water usage.

A Three-Level Neutral-Point-Clamped Converter Based Standalone Wind Energy Conversion System Controlled with a New Simplified Line-to-Line Space Vector Modulation

Wind power systems, which are currently being constructed for the electricity worldwide market, are mostly based on Doubly Fed Induction Generators (DFIGs). To control such systems, multilevel converters are increasingly preferred due to the well-known benefits they provide. This paper deals with the control of a standalone DFIG-based Wind Energy Conversion System (WECS) by using a three-level Neutral-Point-Clamped (NPC) converter. The frequency and magnitude of the stator output voltage of the DFIG are controlled and fixed at nominal values despite the variable rotor speed, ensuring a continuous AC supply for three-phase loads. This task is achieved by controlling the DFIG rotor currents via a PI controller combined with a new Simplified Direct Space Vector Modulation strategy (SDSVM), which is applied to the three-level NPC converter. This strategy is based on the use of a line-to-line three-level converter space vector diagram without using Park transformation and then simplifying it to that of a two-level converter. The performance of the proposed SDSVM technique in terms of controlling the three-level NPC-converter-based standalone WECS is demonstrated through simulation results. The whole WECS control and the SDSVM strategy are implemented on a dSPACE DS 1104 board that drives a DFIG-based wind system test bench. The obtained experimental results confirm the validity and performance in terms of control.

Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: An experimental validation

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power Control (DPC) approach has garnered considerable attention due to its simple and highly adaptable algorithm. This approach has been increasingly recognized in numerous applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system’s performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board.

A hybrid search space reduction algorithm and Newton–Raphson based selective harmonic elimination for an asymmetric cascade H-bridge multi-level inverter

Abstract In this paper, a selective harmonic elimination (SHE) method for an asymmetric cascade H-bridge multi-level inverter (ACHB-MLI) has been performed by using hybrid search space reduction and Newton–Raphson (SSR-NR) algorithm. Three H-bridges are cascaded in a single phase configuration, while each of the bridge is fed with V dc , 3V dc and 6V dc respectively. This particular asymmetric combination of dc sources will produce 10 possible switching combinations and 21-levels in the output voltage ranging from −10V dc to +10V dc . Hence, there will be 10 SHE equations corresponding to fundamental and harmonics, and the solution will be the 10 switching angles which satisfy those SHE equations. While traditional gradient-based techniques like the Newton–Raphson method can yield the global optimal solution for the SHE equations, they necessitate an initial guess. Conversely, meta-heuristic methods often face challenges related to sub-optimal convergence. To address these issues, a novel the hybrid SSR-NR algorithm has been introduced in this paper. This method combines stochastic and deterministic elements, offering a promising solution for solving the SHE equations. In the first step, the evolutionary SSR algorithm will be used and the output of the SSR algorithm is given to the NR method as an initial guess to converge to the final solution, this way the proposed hybrid SSR-NR algorithm is able to address the drawbacks of both the algorithms such as initial guess requirement for NR method and sub-optimal convergence characteristics of evolutionary algorithms. The simulations were carried out in Matlab/simulink environment for a 21-level ACHB-MLI. The results obtained showcase the efficacy of the proposed SSR-NR algorithm in solving the SHE equations. For demonstrating the effectiveness of SSR algorithm the results were also compared with other algorithms such as particle swarm optimization and grey wolf optimizer as well. Further, the experimental results on a laboratory prototype using dSPACE DS1104 R&D controller board were also presented in the paper, to validate the proposed methodology.

Robust state and output feedback stabilization for Lipschitz nonlinear systems in reciprocal state space: Design and real-time validation

In many engineering problems, modelling the state and output derivative variables in reciprocal state space form can be generated more easily than in standard state space one. The formulation of a robust [Formula: see text] control problem using feedback principle for continuous Lipschitz nonlinear systems with uncertainties in reciprocal state space is presented in this article. In contrast to the existing approaches, the considered model is affected by unknown disturbances, parameter uncertainties and derivative Lipschitz nonlinearities. The asymptotic stability based on the proper Lyapunov functions of the closed-loop system is guaranteed. The [Formula: see text] control design resolution problem is ensured through the linear matrix inequality technique, the lemmas and the use of new variables with S-procedure. The performance of the proposed approach is shown through the experimental results using real-time implementation with a digital signal processing device (DSpace DS 1104).

PMSM Field-Oriented Control with Independent Speed and Flux Controllers for Continuous Operation under Open-Circuit Fault at Light Load Conditions

Presented in this article is a permanent magnet synchronous motor (PMSM) control under open-circuit fault (OCF) operation using field-oriented control (FOC) with independent speed and flux controllers. The independent control allows the motor to operate efficiently under varying conditions. A simplified control approach is employed to control the PMSM under the OCF situation; the actual flux and torque of the PMSM are directly measured by the stator currents, eliminating the need for estimators or phase-locked-loop (PLL) systems. Matlab/Simulink is employed for the simulation, while hardware experiments are conducted using a dSPACE DS1104. The simulation and the hardware results demonstrate the control method’s effectiveness in maintaining continuous motor operation during OCF, its robustness against OCF conditions, and its ability to adapt under varying conditions, including speed, flux, and load torque change.