Automatic Control Research Articles

Modeling of Induction Motor Direct Starting with and without Considering Current Displacement in Slot

This article presents a mathematical model of three-phase induction motor (IM) with a squirrel cage rotor and investigates its starting modes. Specifically, two scenarios are considered: direct starting of an IM and direct starting considering the current displacement effect in the rotor slots. Analyzing the starting modes of an IM without the use of automatic control systems is crucial for ensuring reliable, efficient, and safe operation of equipment across various industrial and commercial sectors. Understanding and accounting for the processes occurring during the starting mode of an IM allows for minimizing risks, enhancing energy efficiency, and reducing operational costs. This article details the mathematical modeling methods used for analyzing these starting modes and the results obtained from the modeling. These results were compared with data obtained experimentally, allowing for the assessment of the accuracy and reliability of the proposed model. The conducted research highlights the importance of considering current displacement in the rotor slots for accurate modeling and analysis of induction motor starting modes, particularly in capturing the differences in the amplitudes of the starting current and the faster transition to steady-state operation. Conclusions drawn from the comparison of modeling and experimental data provide valuable insights for the further development of control and operation methods for induction motors.

Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning

The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller.

A Review of the Application of the Laser-Light Backscattering Imaging Technique to Agricultural Products

Growing concerns about food safety and waste have increased consumer demand for high-quality agricultural products, particularly at the postharvest stage. This demand has prompted the development of non-destructive methods to assess or inspect the internal quality of fruits and vegetables. The backscattering imaging technique, also known as diffuse reflectance imaging, is considered a highly promising approach. Numerous studies have focused on practical applications, using laser light at selected wavelengths to develop quick multispectral methods. Due to the rapid interaction of photons with biological tissue, together with the highly computational performance of machine vision, backscattering imaging can offer a valuable alternative to traditional methods. Its primary benefits include quick measurements without chemical sample preparation, easy integration with high-throughput automatic quality control, and reduced waste, since this non-destructive technique does not damage samples. This review presents a comprehensive overview of backscattering imaging, including the measurement geometry, data analysis, and design considerations for vision systems. Additionally, it explores this technique’s advantages, challenges, and accuracy, as demonstrated using various case studies.

A Jupyter Notebook for teaching mathematical modeling with experiments

AbstractMathematical modeling and numerical simulation have a considerable presence in the vast universe of engineering disciplines, given their usefulness in explaining, comprehending, and simulating phenomena and processes with which engineers are in contact in their daily creative and problem‐solving work. For this reason, engineering study programs have at least one course dedicated to dealing with the mathematical modeling of dynamic systems as an essential complement to subsequent courses such as automatic control, structural dynamics, and mechanical vibrations. Nowadays, many technological tools illustrate the applications of mathematical modeling interactively through experiments that offer an incomparable motivation to the students to corroborate with real‐world examples, the utility and veracity of the theory presented to them in the classroom and that in many occasions seems lacking utility and direct relation with the world in which they develop. Based on those mentioned above, this paper presents an example of applying the Laplace transform in modeling physical systems, using a second‐order circuit attached to an Arduino Due board in conjunction with the Jupyter Notebook environment. The numerical and experimental results can be obtained through three optional kernels: Python, Octave, or MATLAB®. For educational purposes, the resulting computer application was presented to undergraduate students of Mechatronics Engineering as an illustrative complement to two courses entitled Signals and Systems Analysis, part of the second semester, and Mathematical Modeling, part of the fifth semester.

A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators

AbstractThe consideration of mileage settlement in the frequency regulation market has encouraged fast‐acting units, such as converter‐interfaced generators (CIG) and electric vehicle stations, to actively participate in load‐generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid‐responding units. In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage‐based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.

Hierarchical Control in Mechatronic Technological Systems

The topic of hierarchical control of technological machines is one of the most relevant in mechanical engineering technology. The most difficult issue in this area is the organization of interactions between different control levels, on the one hand, and the choice of automatic control methods for each of these control levels (control by deviation, control by disturbance, mixed control, etc.), on the other. In this article, in relation to machining technology, a method and corresponding device are proposed that make it possible to implement the control of cutting force parameters (axial cutting force and cutting torque) in an automatic control system for the deviation of cutting torque by changing the axial cutting force (lower level of control). The lower-level control ensures the required quality of the surface layer (surface integrity) of the machined parts. At the same time, the required dimensional accuracy of parts is ensured at the upper level of control, which is implemented by the CNC system of the machine. At the upper level, automatic control is carried out based on the deviation of the kinematic parameters of the movement of the working parts of the CNC machine (acceleration, speed, displacement). Control switching from upper to lower level and back is carried out without using a spindle linear axial movement sensor. Instead of this expensive sensor, a limit switch (a closed and opened pair of contacts) is used, which fixes the lowest axial position of the spindle (and cutting tool). Based on the signal of closing the specified contacts of the limit switch, a transition from the lower control level to the upper one is carried out. Thus, the upper-level system operates only when these contacts are closed, and the lower-level system operates only when they are open. In relation to the upper-level system, the lower-level control system implements the control “by disturbance” principle, also known in control theory as the “disturbance compensation principle”.

Continuous Process Verification 4.0 application in upstream: adaptiveness implementation managed by AI in the hypoxic bioprocess of the Pichia pastoris cell factory

The experimental approach developed in this research demonstrated how the cloud, the Internet of Things (IoT), edge computing, and Artificial Intelligence (AI), considered key technologies in Industry 4.0, provide the expected horizon for adaptive vision in Continued Process Verification (CPV), the final stage of Process Validation (PV). Pichia pastoris producing Candida rugosa lipase 1 under the regulation of the constitutive GAP promoter was selected as an experimental bioprocess. The bioprocess worked under hypoxic conditions in carbon-limited fed-batch cultures through a physiological control based on the respiratory quotient (RQ). In this novel bioprocess, a digital twin (DT) was built and successfully tested. The implementation of online sensors worked as a bridge between the microorganism and AI models, to provide predictions from the edge and the cloud. AI models emulated the metabolism of Pichia based on critical process parameters and actionable factors to achieve the expected quality attributes. This innovative AI-aided Adaptive-Proportional Control strategy (AI-APC) improved the reproducibility comparing to a Manual-Heuristic Control strategy (MHC), showing better performance than the Boolean-Logic-Controller (BLC) tested. The accuracy, indicated by the Mean Relative Error (MRE), was for the AI-APC lower than 4%, better than the obtained for MHC (10%) and BLC (5%). Moreover, in terms of precision, the same trend was observed when comparing the Root Mean Square Deviation (RMSD) values, becoming lower as the complexity of the controller increases. The successful automatic real time control of the bioprocess orchestrated by AI models proved the 4.0 capabilities brought by the adaptive concept and its validity in biopharmaceutical upstream operations.

Random Subspace Ensemble-Based Detection of False Data Injection Attacks in Automatic Generation Control Systems

Automatic Generation Control (AGC) systems in smart grids are increasingly vulnerable to cyber-attacks, particularly False Data Injection (FDI) attacks, due to their reliance on information and communication technologies. These vulnerabilities pose significant threats to the reliable operation of power systems. To address this challenge, this research paper introduces the machine learning (ML) based cyberattack detection technique designed to identify FDI attacks with the highest accuracy proficiently. The study involves a comprehensive analysis of three features: the original discrete signal feature, the cycle-to-cycle-based feature, and the sample-to-sample-based feature. These features are utilized for detecting FDI attacks and distinguishing them from normal load variations. The research meticulously collects data by simulating diverse FDI attacks, including step attacks, pulse attacks, random attacks, and normal load variation cases on an AGC power system. Four ML classifiers are selected and compared for the classification task. The simulation results reveal that the sample-to-sample-based feature proves highly effective in distinguishing FDI attacks compared to the original signal and cycle-to-cycle-based features. Notably, the results indicate that the random subspace ensemble (RaSE) classifier, utilizing sample-to-sample-based features, effectively identifies and classifies all normal and FDI attacks. This research provides valuable insights into the potential of ML techniques for enhancing FDI attack detection in the AGC of power systems. It provides a potential pathway for overcoming the limitations associated with traditional model-based FDI detection methods.

Controlled drainage with subirrigation systems: Reduce water supply by automatic control

Controlled drainage with subirrigation (CDSI) is a viable measure to supply, retain or discharge groundwater, thereby contributing to freshwater availability in agriculture under changing environmental conditions. Relatively simple CDSI systems can be controlled manually to set a few drainage levels. More advanced systems can be controlled remotely to set any drainage level (between a technical maximum and minimum). CDSI potentially improves hydrological conditions for crop growth, but the required external water supply can be large. Therefore, the objective of this paper is to investigate whether external water supply for subirrigation can be reduced by automatic control of CDSI systems in relation to crop water demand. Field measurements of a CDSI pilot in the Dutch sandy Pleistocene uplands were combined with weather forecasts to simulate the optimal drainage level and day by day water demand and supply using the agro-hydrological Soil, Water, Atmosphere, Plant model (SWAP). Firstly, model simulations showed that the water requirement reduced by 60 mm (dry growing season), 253 mm (average growing season) and 348 mm (wet growing season) using a dynamically managed crest level (CDSI-dyn) compared to using a fixed crest level (CDSI-fix), with minor effects on crop yield. Secondly, model simulations showed that a higher hydraulic resistance to downward seepage, a higher ditch water level or deeper roots reduced the water supply (up to 100 mm). Thirdly, accepting 10 % daily crop drought and oxygen stress for CDSI-dyn reduced the water supply requirement with 235–628 mm (dry vs wet growing season) compared to CDSI-fix. In conclusion, the required water volume for CDSI could be substantially reduced by automated control of the drainage level and water supply rate, while maintaining crop yield or accepting minor reductions, which increases the potential of implementation of CDSI systems.

Optimal control of automatic voltage regulator system using hybrid PSO-GWO algorithm-based PID controller

Optimal control of automatic voltage regulator system using hybrid PSO-GWO algorithm-based PID controller

Improving a shell-tube latent heat thermal energy storage unit for building hot water demand using metal foam inserts at a constant pumping power

The phase transition heat transfer during the melting and solidification processes of phase change materials (PCMs) was modeled in a shell-tube thermal energy storage unit. For the first time, special attention was dedicated to the heat transfer enhancement on the heat transfer fluid (HTF) side by placing metal foam (MF) inserts in the HTF tube. The MF inserts on the HTF side were placed next to the fin bases to produce the maximum heat transfer enhancement. A two-temperature local thermal non-equilibrium model was applied to accurately model the transient heat transfer in the MF domains. To perform a fair heat transfer analysis and consider the hydrodynamic aspects of heat transfer, the pumping power for driving the HTF fluid was kept fixed. The finite element method with automatic time-step control was used to integrate the model equations. Results show that the insertion of the MF in the HTF tube enhances conduction heat transfer but reduces flow for a constant pump power. Using a small amount of MF provides a good flow rate and a convective heat transfer while using a large amount of MF provides good conduction heat transfer but a poor flow rate and convection. A moderate amount of MF inserts results in poor conduction and a poor flow rate, notably increasing the phase transition time. A 5% MF insert could provide about a 13% shorter solidification time compared to a 25% MF insert. Thus, a well-designed and engineered MF insert configuration on the HTF side can reduce the phase transition time and improve the energy storage power.

Enhancing tapping mode atomic force microscopy using AutoResonance control and a hybrid dynamic model

We present a novel method for analyzing tapping mode atomic force microscopy (AFM), enhancing topography reconstruction’s accuracy and speed. Our approach integrates an automatic resonance-tracking control scheme with a hybrid dynamic model that considers interactions between the AFM tip and the sample, and the oscillating sensor dynamics. A key aspect of our method is a simplified model that captures intricate dynamic behavior as a function of the distance from the topography. This model enables the immediate approximation of the distance from the topography, eliminating the necessity to lock onto the nominal distance as done with classical AFMs. To validate the accuracy, we conducted numerical simulations of Van der Waals and capillary interaction forces, as well as experimental measurements of a coin’s topography. The results affirm the effectiveness of our approach in enhancing AFM imaging and characterization capabilities.

Electric Vehicle Power System in Intelligent Manufacturing Based on Soft Computing Optimization

Soft computing technology has attracted extensive attention in computer engineering and automatic control domains because it can deal with uncertainties, fuzziness, and complex practical problems. This study adopts a Genetic Algorithm (GA) in soft computing technology to realize the cooperative optimization of electric vehicle's dynamic and economic performance. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness, and complex practical problems, making GA an effective tool for solving complex optimization problems. Firstly, the electric vehicles’ power system structure and energy management strategy are investigated and analyzed. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Thirdly, NSGA-II is used to construct the electric vehicles’ power and energy configuration, with power and economic performance as the main optimization objectives. Meanwhile, a fuzzy logic controller is designed to adjust the parameters of GA online, so that the optimization process is closer to the actual operating conditions. Finally, the relevant variables are selected to achieve the optimization goal, the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the optimized electric vehicle’s acceleration time is remarkably reduced, the dynamic performance is improved by more than 7%, and the power loss is reduced by 5%. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. This study provides a new idea and method for the performance optimization of electric vehicles. Moreover, it offers a valuable reference for the innovation and development of electric vehicle technology in the intelligent manufacturing field. This study indicates that electric vehicles could be more efficient, energy-saving, and environmentally friendly to serve people's travel needs in the future.

ПРОБЛЕМЫ РЕАЛИЗАЦИИ СИСТЕМ ПОЛУАВТОМАТИЧЕСКОГО УПРАВЛЕНИЯ, ВКЛЮЧАЮЩИХ КОНТУР ОБРАТНОЙ СВЯЗИ ПО УСИЛИЮ

The issues of force-torque sensing in semi-automatic systems are considered. The paper describes the main control principles in ergatic systems from the viewpoint of automatic control theory, considers a method for improving the control quality by introducing additional feedback for the operator. The work proposes force-torque sensing as the most promising feedback channel for the implementation, describes the relevance of this channel in the fulfilled automatic control systems, gives examples. A description of the feedback loop for the efforts on the manipulator’s working element is given. The work highlights the following as the main problems in implementing the considered semi-automatic systems: the kinematic chain influence, filtering and delay, gravity compensation, as well as the problem of the natural feedback loop.

Soil Moisture Detection and Linear Deceleration Control Strategy Enhancing Trenching Depth Precision and Stability for Rapeseed Sowing

Sowing depth significantly affects the germination of rapeseed, and different soil moisture conditions require corresponding sowing depths. However, most current trenching devices do not account for soil moisture content, and commonly used hydraulic or constant-force trenching equipment also exhibits deficiencies in stability and consistency. To address these challenges, this study developed an automatic depth adjustment control system based on soil moisture content. A soil moisture detection device and an innovative sliding mechanism that maintained the soil moisture sensor in a relatively stationary position relative to the soil during seeder movement were introduced. An automatic sowing depth adjustment device was designed to modulate the sowing depth. A control strategy that incorporated the Kalman filtering algorithm and linear deceleration equations was conducted. At an observation noise covariance matrix (Q/R) of 0.001, a deceleration range of 40 mm and a minimum speed of 10, the control system exhibited minimal overshoot (approximately 4%) and steady-state error (approximately 3.2 mm). It effectively adjusted the trenching depth while operating at speeds ranging from 2 to 3.6 km/h, successfully adapting to variations in soil topography. The system performance tests revealed that the control system adjustment time (ts) was 534 ms and the steady-state error remained within 1 mm. Under three different soil moisture content conditions, the sowing depth qualification rate and stability coefficients consistently surpassed 90% and 80%, respectively. This research offers a sowing depth adjustment control system based on soil moisture content, contributing to more precise depth regulation for rapeseed sowing.

Bias control for optical single-sideband transmitter based on single IQ modulator

The optical in-phase and quadrature modulator (IQM) is typically utilized as an optical single-sideband (SSB) transmitter for its simple structure and low cost. It requires the modulator biases to be slightly offset for generating the optical carrier. However, this poses significant challenges in the bias control circuits, as most off-the-shelf automatic bias control (ABC) modules are designed to stabilize the IQM at its carrier suppression point for coherent optical communication. In this paper, a novel arbitrary ABC scheme is proposed to stabilize the carrier-to-signal power ratio (CSPR) of an optical SSB signal over long-term operation. This scheme uses SSB rather than double-sideband (DSB) sinusoidal signals as the dither signals. It does not violate the minimum phase condition, thus avoiding performance penalties compared to the conventional ABC scheme in which the DSB dither signal is used. We also develop a closed-form expression to reveal the relationship between the control parameter in the ABC algorithm and the CSPR of the optical SSB signal. By this means, the optical SSB transmitter can automatically adjust its bias voltages and stabilize them at a given CSPR value. The experimental results with ∼80 Gb/s optical SSB signal show that our proposed ABC scheme is effective in stabilizing the bias of the IQM at any point and maintains the CSPR value of the optical SSB signal over hours. It is also shown that the proposed method leads to 1 dB improvement in the receiver sensitivity compared with the traditional ABC technique.

Improving Precision of LM Guide Form Grinding with Automatic Nozzle Position Control Device

Improving Precision of LM Guide Form Grinding with Automatic Nozzle Position Control Device

Ultrasonic Enhancement for Mineral Flotation: Technology, Device, and Engineering Applications

In the past five years, the number of articles related to ultrasonic mineral flotation has increased by about 50 per year, and the overall trend is on the rise. The most recent developments in ultrasonics for flotation process intensification are reviewed herein, including effects of ultrasound treatment on an aqueous slurry, improvement in flotation methods and technological processes, device development tracking, and application effects in mineral process engineering. At this point in time, there are pilot-scale flotation tests to evaluate the feasibility of ultrasonic pretreatment technology for industrial use to enhance residue flotation separation, and the results showed that the recovery rate of concentrate is increased by about 10%. Four aspects of ultrasonic flotation process improvement are summarized, namely, changing the ultrasonic parameters, the synergistic effect of ultrasound and reagents, the ultrasonic effect of particles with different-sized fractions, and application to new systems. In addition, the effect of ultrasonic flotation mechanisms is explored through a quadratic model and numerical simulation. The combination of ultrasonic flotation with other fields, such as magnetic fields, to enhance the separation efficiency and recovery of minerals is also a future trend. It is also proposed that ultrasonic flotation technology will be used with big data, industrial Internet of Things, and automatic control technology to achieve deep bundling, optimizing the flotation process by implementing remote monitoring and control of the flotation process.

Design and Implementation of an S-band Receiver for Small Satellite Based on the Manufactured Subsystems

In this work the design and implementaion of a receiver for small satellites in range of 1.9 GHz to 3.0 GHz frequency were presented. We concentrated on developing a new architecture for receiving satellites in challenging environments and integrated some of the author’s previously published designs. By using the protected mechanism for RF circuit of receiver, a mutil-stage in IF amplifier and automatic gain control in IF amplifer has been designed, this receiver has a higher gain (78 dB), a larger dynamic ranger (80 dB), a high stability and sensitivity (110 dBm) at 2.15 GHz frequency. The receiver module works well; and due to the flexible structure, easy assembly and low-cost characteristics, it can be applied for small satellites and ground stations.

Parametric correction in the control system of the electric propulsion of autonomous underwater vehicles affected by random inputs

The paper deals with the problem of controlling a DC propulsion motor of an autonomous underwater vehicle using a bidirectional non-isolated DC-DC converter. An automatic speed control system with a parametric controller was developed in the Matlab / Simulink package. To reduce energy conversion stages, dc-dc converters are often used to control dc motors AUV. The result of the work is an adaptive control system for the speed of the AUV propeller, which would provide high accuracy for a wide range of speeds and at the same time limit the maximum current and voltage at the armature. The indicators of the quality of performance in various modes of operation were determined, maps of the controller settings were compiled, and parametric correction was introduced. The operation of the system is analyzed and drawbacks are eliminated, such as voltage surges when switching from one speed to another, a decrease in overshoot and settling time. The performance of the system is analyzed under the conditions of random inputs from the propeller. The description of the laboratory stand for the study of the control system is given. A description of the laboratory study of the operation of the "DC converter—engine" system under conditions of random fluctuations in the moments on the motor shaft is given. It is shown that the proposed system has shown itself well under laboratory conditions and the experimental results are consistent with modeling in Matlab / Simulink.