From the control theory, bigger observer and controller gains are utilized to improve the position perturbation attenuation, while smaller observer and controller gains are utilized to improve the velocity perturbation attenuation. Therefore, it would be interesting to suggest optimizers to find the best observer and controller gains to improve the position or velocity perturbation attenuation. In this investigation, a high-gain controller is suggested to obtain the perturbation attenuation, a genetic high-gain controller is suggested to improve the position perturbation attenuation, and a compact high-gain controller is suggested to improve velocity perturbation attenuation in inverted pendulums. The high-gain controller utilizes a high-gain observer and a high-gain estimator to obtain the state and perturbation estimation. The genetic high-gain controller utilizes a genetic optimizer to find the best observer and controller gains. The compact high-gain controller utilizes a compact optimizer to find the best observer and controller gains. The suggested high-gain controllers are compared with the simplex and bat controllers to improve the position or velocity perturbation attenuation in two inverted pendulums. 

Genetic high-gain controller to improve the position perturbation attenuation and compact high-gain controller to improve the velocity perturbation attenuation in inverted pendulums.

In this paper, a combined structure of algorithms based on field-oriented control (FOC) and direct torque control (DTC) is proposed for improving the performance of a three-phase induction motor (IM) over a wide range of torque loads. The objective of this study is to investigate the state-of-the-art research performed on the resilient intersection control management between FOC and DTC algorithms for IM drive. Three algorithms are employed in the proposed scheme: Direct Torque Stator Flux Control (DTSFC), Direct Torque Rotor Flux Control (DTRFC), and the FOC algorithm. The main aim of the control law is to launch the drive system by selecting DTSFC or DTRFC during the transient state while FOC is switched smoothly to the steady-state. The proposed method had several advantages, including the ability to produce torque beyond the pull-out torque range at 340%, high torque dynamics during transient state within 0.5 ms, low chattering up to 0.05 N.m, and fixed frequency at steady-state operation. Furthermore, a comprehensive comparison study has been conducted, including the most recent research, to verify the effectiveness and superiority of the proposed technique. Results were obtained using both MATLAB/Simulink environment and also were experimentally performed by using a dSPACE DS1103 digital signal processing board to verify the concept of the proposed control scheme.

https://www.mdpi.com/2227-7390/10/20/3842/pdf?version=1666596257

A Combined Control Scheme of Direct Torque Control and Field-Oriented Control Algorithms for Three-Phase Induction Motor: Experimental Validation

Recently, condition monitoring (CM) and fault detection and diagnosis (FDD) techniques for rotating machinery (RM) have witnessed substantial advancements in recent decades, driven by the increasing demand for enhanced reliability, efficiency, and safety in industrial operations. CM of valuable and high-cost machinery is crucial for performance tracking, reducing maintenance costs, enhancing efficiency and reliability, and minimizing mechanical failures. While various FDD methods for RM have been developed, these predominantly focus on signal processing diagnostics techniques encompassing time, frequency, and time-frequency domains, intelligent diagnostics, image processing, data fusion, data mining, and expert systems. However, there is a noticeable knowledge gap regarding the specific review of image-based CM and FDD. The objective of this research is to address the aforementioned gap in the literature by conducting a comprehensive review of image-based intelligent techniques for CM and fault FDD specifically applied to induction motors (IMs). The focus of the study is to explore the utilization of image-based methods in the context of IMs, providing a thorough examination of the existing literature, methodologies, and applications. Furthermore, the integration of image-based techniques in CM and FDD holds promise for enhanced accuracy, as visual information can provide valuable insights into the physical condition and structural integrity of the IMs, thereby facilitating early FDD and proactive maintenance strategies. The review encompasses the three main faults associated with IMs, namely bearing faults, stator faults, and rotor faults. Furthermore, a thorough assessment is conducted to analyze the benefits and drawbacks associated with each approach, thereby enabling an evaluation of the efficacy of image-based intelligent techniques in the context of CM and FDD. Finally, the paper concludes by highlighting key issues and suggesting potential avenues for future research. 

Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors

This paper proposes a strategy to quantify a position sensor offset induced in a post-production (after calibration) permanent magnet synchronous machine (PMSM) drive system under field-oriented control while in operation. The importance of the position signal in a PMSM drive system is presented with results on the system behavior during a position sensor offset error. Subsequently, an algorithm is presented to quantify the induced error in real-time, based on voltage estimates. Simulation and experimental results are presented to justify the algorithm performance.

Post Production PMSM Position Sensor Offset Error Quantification via Voltage Estimation

This paper introduces a brand-new, inspired optimization algorithm (the chaotic billiards optimization (C-BO) approach) to effectively develop the optimal parameters for fuzzy PID techniques to enhance the dynamic response of the solar–hydrogen drive of an induction motor. This study compares fuzzy-PID-based C-BO regulators to fuzzy PID regulators based on particle swarm optimization (PSO) and PI-based PSO regulators to provide speed control in solar–hydrogen, induction-motor drive systems. The model is implemented to simulate the production and storage of hydrogen while powering an induction-motor drive which provides a great solution for the renewable energy storage problem in the case of solar pumping systems. MATLAB/Simulink 2021a is used to simulate and analyze the entire operation. The laboratory prototype is implemented in real time using a DSP-DS1104 board. Based on the simulation and experimental results, the proposed fuzzy-PID-based C-BO has reduced speed peak overshoot by 45.3% when compared to a fuzzy PID based PSO speed regulator and by 68.13% when compared to a PI-based PSO speed controller in the case of a large-scale motor. Additionally, the proposed speed regulator has a 6.1% faster speed rising time than a fuzzy-PID-based PSO and a 9.5% faster speed rising time than a PI–PSO speed controller. It has an excellent dynamic responsiveness value when compared to the other speed regulators.

/pdf/solar-hydrogen-variable-speed-control-of-induction-motor-3g03jbra.pdf

Solar Hydrogen Variable Speed Control of Induction Motor Based on Chaotic Billiards Optimization Technique

Genetic and particle swarm optimization algorithms based direct torque control for torque ripple attenuation of induction motor

An artificial neural network based harmonic distortions estimator for grid- connected power converter-based applications

Solar photovoltaic energy generation has garnered substantial interest owing to its inherent advantages, such as zero pollution, flexibility, sustainability, and high reliability. Ensuring the efficient functioning of PV power facilities hinges on precise fault detection. This not only bolsters their reliability and safety but also optimizes profits and avoids costly maintenance. However, the detection and classification of faults on the Direct Current (DC) side of the PV system using common protection devices present significant challenges. This research delves into the exploration and analysis of complex faults within photovoltaic (PV) arrays, particularly those exhibiting similar I-V curves, a significant challenge in PV fault diagnosis not adequately addressed in previous research. This paper explores the design and implementation of Support Vector Machines (SVMs) and Extreme Gradient Boosting (XGBoost), focusing on their capacity to effectively discern various fault states in small PV arrays. The research broadens its focus to incorporate the use of optimization algorithms, specifically the Bees Algorithm (BA) and Particle Swarm Optimization (PSO), with the goal of improving the performance of basic SVM and XGBoost classifiers. The optimization process involves refining the hyperparameters of the Machine Learning models to achieve superior accuracy in fault classification. The findings put forth a persuasive case for the Bees Algorithm’s resilience and efficiency. When employed to optimize SVM and XGBoost classifiers for the detection of complex faults in PV arrays, the Bees Algorithm showcased remarkable accuracy. In contrast, classifiers fine-tuned with the PSO algorithm exhibited comparatively lower performances. The findings underscore the Bees Algorithm’s potential to enhance the accuracy of classifiers in the context of fault detection in photovoltaic systems. 

Electrical Faults Analysis and Detection in Photovoltaic Arrays Based on Machine Learning Classifiers

Analysing Various Control Technics for Manipulator Robotic System (Robogymnast)

Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common for data classification research, and classifying diseases and faults, which is essential due to frequent alterations in the training dataset, in which it would be expensive using most methods to construct a different classifier every time this happens. Therefore, KNN can be used effectively as it does not require a residual classifier to be constructed in advance. KNN offers ease of use and can be applied across a broad variation spectrum. Here, a novel KNN classification approach is put forward using the Bayesian Optimization Algorithm (BOA) for optimisation. This paper seeks to make classification more accurate and suggest alterations of nearest neighbour K value to use information about dataset structure and the similarity measure of distance. The findings of experimental work based on the University of California Irvine (UCI) repository datasets in general shows improved performance of classifiers compared with conventional KNN and give greater reliability without a significant time cost to speed.

A new enhancement of the k-NN algorithm by Using an optimization technique

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