Servo Motor Control Research Articles

Leveraging EMG Signals for Precision Control

This study explores the application of electromyography (EMG) signals for controlling mechanical systems using an Arduino Uno microcontroller, an Olimex EMG shield, sEMG electrodes, and a servo motor. EMG signals have been used for various applications such as prosthetics and assistive devices, proving to be a reliable source for control mechanisms. The experiment involves initial EMG testing, calculating RMS voltage, and integrating servo motor control. Results show that EMG signals can effectively control a servo motor, with improvements achieved through filtering, PID control, and multi-motor setups. Advanced techniques, such as Neural Networks, Reinforcement Learning, and Model Predictive Control, were also explored to enhance system performance. This research demonstrates the potential of EMG-based control systems in robotics and prosthetics, highlighting their future applications and innovations in adaptive technologies.

Intelligent iPD control estimation of Hardware in-the-Loop generated dynamics

We are concerned here by the study of explicit ultra-local model approximation in the context of model free intelligent Proportional Derivative (iPD) control, which to our knowledge has not been reported in the available control literature. The unmodeled dynamics estimation is approximated by integrals reducing real-time system measurements noise in the control loop and implemented using a Finite Impulse Response (FIR) digital filter. Hardware-in-the-Loop (HIL) perturbation load generated dynamics are used in an iPD control scheme exhibiting the unknown dynamics approximation. We use two DC servo motors interconnected by their shafts. The first DC servo motor is controlled by the proposed feedback-based iPD controller whereas the second DC servomotor is used as a programmable torque load to the controlled DC servo motor. Using HIL testing we can generate desired load torques directly showing how the iPD controller approximates HIL generated perturbations. For the proposed control iPD scheme, we present both computer-based simulation and experimental real-time control results.

Modelling and control of the lower hook mechanism for fishing net weaving machine based on active disturbance rejection controller

Abstract The lower hook mechanism of the fishing net weaving machine has significant impact on its weaving velocity and net quality. This study incorporated a multi-motor based lower hook mechanism, which is driven by multi-motors instead of mechanical cams. Compared with the traditional lower hook mechanism, the multi-motor based lower hook mechanism has the advantages of fewer mechanical wear and faster running speed. However, the working condition of the lower hook mechanism places a very high demand on its following accuracy and resistance to disturbance, so the servo motor should be highly accurate and highly robust. The commonly used controller of the servo motor is three closed loop proportion-integral (PI) controller. In this paper, we modelled and experimentally verified the multi-motor based lower hook mechanism, and rebuilt the servo motor controller by replacing the position loop PI and speed loop PI of the three closed loop PI controller with position-velocity joint active disturbance rejection controller. Simulations suggest that the active disturbance rejection controller performs better on both following accuracy and resistance to disturbance compared with the traditional PI controller in the application of lower hook mechanism.

Deviceization of high-performance and flexible Ag2Se films for electronic skin and servo rotation angle control

Ag2Se shows significant potential for near-room-temperature thermoelectric applications, but its performance and device design are still evolving. In this work, we design a novel flexible Ag2Se thin-film-based thermoelectric device with optimized electrode materials and structure, achieving a high output power density of over 65 W m−2 and a normalized power density up to 3.68 μW cm−2 K−2 at a temperature difference of 42 K. By fine-tuning vapor selenization time, we strengthen the (013) orientation and carrier mobility of Ag2Se films, reducing excessive Ag interstitials and achieving a power factor of over 29 μW cm−1 K−2 at 393 K. A protective layer boosts flexibility of the thin film, retaining 90% performance after 1000 bends at 60°. Coupled with p-type Sb2Te3 thin films and rational simulations, the device shows rapid human motion response and precise servo motor control, highlighting the potential of high-performance Ag2Se thin films in advanced applications.

A Novel Nonsingular Fast Terminal Sliding Mode Control with Sliding Mode Disturbance Observer for Permanent Magnet Synchronous Motor Servo Control

This article proposes a novel nonsingular fast terminal sliding mode control (N-NFTSMC) with a sliding mode disturbance observer (SDOB) for permanent magnet synchronous motor (PMSM) servo control. Firstly, to reduce the chattering issue, a new sliding mode reaching law (NSRL) is proposed for the N-NFTSMC. Secondly, to further improve the dynamic tracking accuracy, we introduce a sliding disturbance observer to estimate unknown disturbances for feedforward compensation. Comparative simulations via Matlab/Simulink 2018 are conducted using the traditional NFTSMC and N-NFTSMC; the step simulation results show that the chattering phenomenon was suppressed well via the N-NFTSMC scheme. The sine wave tracking simulation proves that the N-NFTSMC has better dynamic tracking performance when compared with traditional NFTSMC. Finally, we carry out experiments to validate that the N-NFTSMC adequately suppresses the chattering issue and possesses better anti-disturbance performance.

Fuzzy inference system enabled neural network feedforward compensation for position leap control of DC servo motor

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods.

Securing industrial control systems: Developing a SCADA/IoT test bench and evaluating lightweight cipher performance on hardware simulator

This paper addresses the critical need for enhancing security in Supervisory Control and Data Acquisition (SCADA) networks within Industrial Control Systems (ICSs) to protect the industrial processes from cyber-attacks. The purpose of our work is to propose and evaluate lightweight security measures to safeguard critical infrastructure resources. The scope of our effort involves simulating a secure SCADA/IoT-based hardware test bench for ICSs, utilizing Modbus and MQTT communication protocols. Through case studies in remote servo motor control, water distribution systems, and power system voltage level indicators, vulnerabilities such as Denial of Service (DoS) and Man-in-The-Middle (MiTM) attacks are identified, and security recommendations are provided. To execute our work, we deploy lightweight ciphers such as Prime Counter & Hash Chaining (PCHC) and Ascon algorithm with Compression Rate (ACR) for secure information exchange between the plant floor and the control center. Evaluation of these ciphers on Raspberry Pi focuses on execution speed and memory utilization. Additionally, a comparison with the AGA-12 protocol standard for SCADA networks is conducted to underscore the efficacy of the proposed security measures. Our findings include the identification of SCADA network vulnerabilities and the proposal of lightweight security measures to mitigate risks. Performance evaluation of the proposed ciphers on Raspberry Pi demonstrates their effectiveness, emphasizing the importance of deploying such measures to ensure resilience against cyber threats in SCADA environments.

Design and Implementation of LPG Leakage Detection System using IoT-based Regulator Lever Automation on a Household Scale

Liquefied Petroleum (LPG) is very common in daily cooking activities. Therefore, preventive measures must be taken to reduce the risk of fire and explosion due to LPG leakages. One action that can be taken is to install a regulator lever that can open automatically when a leakage is detected. In this research, Pulse Width Modulation (PWM) is used to control the rotational speed of the servo motor on the regulator lever, which aims to implement an automatic control system that is responsive when an LPG leakage is detected. Using servo motor control to automatically open the regulator lever can reduce the impact of accidents or losses due to LPG leakages. The test results show that the regulator lever can be opened effectively with a pulse value of 0.55 ms at a rotation angle of 0° in 0 seconds, while it can be closed with a pulse value of 2.45 ms at an angle rotation of 180° in 1.66 seconds. This shows that the regulator responds appropriately to the level of danger detected and is precise in adjusting position. The use of PWM on servo motors in the regulator control system has proven effective in increasing responsiveness to LPG leakages, as seen from the fast response (0 to 1.66 seconds) and accuracy (100% success percentage for 15 trials). This shows that using PWM can respond to emergencies quickly and precisely.

Modeling and control of a novel mechatronic model of a 750 kW-FSWT with power-splitting transmission using RBF neural network: a bond graph approach

This work addresses issues that appear from wind turbines equipped with frequency converters and presents a new fixed-speed wind turbine (FSWT) concept based on a split power transmission, along with a Mechatronic Control Model. The wind turbine rotor drives the first transmission shaft, while a servomotor with variable speed powers the second input. The differential gear transmission output is linked to the electric grid through an asynchronous generator. To optimize the power extracted from the wind energy while minimizing excessive dynamic loads on the wind turbine, a mechatronic control model of a 750 kW-FSWT is applied using a proportional and integral controller (PI). The paper suggests employing neural networks and evolutionary algorithms for determining appropriate PI gains. For collective servomotor control of a 750 kW-FSWT, a radial basis function (RBF) neural networks based on PI controller is proposed. To acquire an optimum dataset for RBF training, the particle swarm optimization (PSO) evolutionary algorithm is harnessed. The robustness and effectiveness of the proposed model and controller are verified and confirmed through simulation results. Hands-on experience is conducted using the 20-sim software package.

Closed-loop Control System Design of Wind Rope Hoisting of Transmission Tower

Abstract The traditional wind rope (tricing pendant) hoisting method of power transmission towers mainly relies on manpower to observe the posture of the hanger and adjust the retraction and release speed of the control rope, so as to avoid the scraping of hanger and the assembled tower body. In this paper, the ADAMS/Cable modeling tool is used to establish the transmission tower hoisting dynamic model with the JC27151CG tower as the object, and the reasonable installation position of the tricing pendant retraction system is analyzed. And based on the reasonable installation position, the hoisting strategy and hoisting control system of JC27151CG tower are studied. The main control system hardware of the control device mainly includes Delta PLC, servo driver, AC contactor and DC relay. The main control program is divided into two modes, manual and automatic, with a closed-loop control servo motor system. The manual mode operates the servo motor through buttons to realize forward and reverse rotation. In automatic mode, the system intelligently adjusts the lifting speed and attitude according to the data of the navigation system to ensure a smooth and safe lifting process. The research outcomes not only offer novel theoretical perspectives and methods for the hoisting of power transmission towers but also provide robust technical support for practical engineering operations.

Advancing Robotic Fruit Picking: Adaptive End-Effector Approach

In modern agriculture, the demand for efficient fruit picking methods is constantly increasing due to labor shortages and the need to improve productivity. Traditional fruit picking methods often rely on manual labor, which is labor-intensive, time-consuming, and prone to inconsistency. This study aims to develop a novel end effector for agricultural fruit picking robots, which improves the efficiency and accuracy of fruit picking through highly flexible and adaptive design. It can effectively handle fruits of diverse types, shapes, and hardness. By adopting a three-finger scheme and independent servo motor control, the simulated movements of human hands are realized. Shape memory alloy (SMA) and electroactive polymer (EAP) as intelligent driving materials, as well as flexible materials such as silicone, are explored to optimize the grip performance and fruit protection of the actuator. High-precision visual and tactile sensors are also integrated to support the accurate identification of fruits of different maturity. The performance of the actuator is evaluated through advanced data analysis techniques, including descriptive statistics, hypothesis testing and regression analysis. The findings suggest that the proposed design optimization measures based on data analysis results can further enhance the adaptability and efficiency of the robot in future applications, thereby addressing the challenges faced in modern agricultural practices and contributing to increased productivity and sustainability in the agriculture sector.

Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

UAV Control Based on Pattern Recognition in Aquaculture Application

This study proposes a drone application for the net cage aquaculture industry. A visual control structure is applied to the drone to obtain water-quality information surrounding the net cages. This study integrates a hexacopter, camera, onboard computer, flight control board, servo motor, and global positioning system’s auto-cruise function to adjust the drone position and control the servo motor retractable sensor to reach the desired target at an accurate location. In object identification, a deep learning neural network is used to identify the net cages. An onboard computer calculates the horizontal distance between the drone and the net cage. A “You only look once” (YOLO) neural network is used to detect the net cage images. Considering the hardware calculation speed and ability, an onboard computer is applied to process the flight control board and control the drone. In the mission, an aerial camera detects targets (net cage) and provides visual information to the drone for the target approaching control process. After executing the water-quality measurement, the drone will end the mission and return to the base. This study modifies the architecture of YOLO, compares it with the original model, and then finds a proper architecture for this mission. This study aims to assist cage aquaculture operators by using drones to measure water quality, which can reduce aquaculture’s labor costs.

Principles and Methods of Servomotor Control: Comparative Analysis and Applications

Servomotors have found widespread application in many areas, such as manufacturing, robotics, automation, and others. Thus, the control of servomotors is divided into various principles and methods, leading to a high diversity of control systems. This article provides an overview of types of servomotors and their basic principles and control methods. Principles such as digital signal processing, feedback control principle, field-oriented control, and integration with Industry 4.0 are discussed. Based on these control principles, the article presents popular control methods: PWM control, current control, two-loop control, fuzzy-logic control, and programmable control. The article concludes with a comparison of the presented methods on several criteria, and as an example, it includes the results of modeling a servomotor using the fuzzy-logic control method.

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network

Due to its advantages of having a high power-to-weight ratio and being energy-efficient, the electro-hydraulic servo pump control system (abbreviated as EHSPCS) is frequently employed in the industrial field, such as the electro-hydraulic servo pump control (EHSPC) servomotor for steam turbine valve regulation control. However, the EHSPCS has strong nonlinearity and time-varying features, and the factors that cause system performance degradation are complex. Once a system failure occurs, it may lead to serious accidents, causing serious casualties and economic losses. To address the above issues, a system health assessment method based on LSTM-GRNN-ANN (LGA) deep neural network is proposed in this paper. Firstly, with oil volume gas content, servo motor air-gap flux density, and system leakage coefficient as the health assessment performance indicators, a health assessment performance index system for the EHSPCS is built, Furthermore, the system performance index threshold is set. Secondly, an LGA deep neural network is constructed by combining LSTM, GRNN and ANN, and a deep neural network based on the LGA is used to create an EHSPCS health assessment model. Subsequently, system feature parameter extraction, algorithm design, and parameter debugging are carried out. Finally, an EHSPCS experimental platform is established, typical system failure simulation experiments are designed, and comparative experimental analysis is conducted. The experimental findings demonstrate that the average accuracy of the system health assessment model based on the LGA deep neural network suggested in this paper is 96.37%, compared to 89.84%, 87.99% for LSTM and GRNN, which validates the accuracy of the system health assessment model based on the LGA deep neural network.

Implementasi Convolutional Neural Network Pada Robot Tikus Micromouse Untuk Navigasi Labirin Dengan Pendeteksi Garis Menggunakan Raspberry Pi

Micromouse is a science and technology project competition that began in the late 1970s. Simply put, the micromouse competition is an event where robotic mice can navigate a 16x16 maze without human assistance. Convolutional Neural Network, also known as CNN, is an extension of the Artificial Neural Network (ANN) designed to process two-dimensional data and is often used for classification, recognition, and prediction tasks. In the conducted research, a robotic mouse was designed using Raspberry Pi to control servo motor movements and utilize a camera to detect maze obstacles, such as intersections, through image scanning. The robotic mouse was tested using a CNN architecture model on TensorFlow, assisted by a line detection algorithm. The testing results indicated that the CNN model architecture showed signs of overfitting (the model learned features too well from the training data) with an accuracy of 96.53%, and the model accuracy evaluation result was 97.97% on the designed dataset. As part of the testing, a maze size of 5x5 was used. The testing of the Convolutional Neural Network model and line detection algorithm, when applied to the robotic mouse, demonstrated that the mouse could navigate according to the path. However, the recorded data was influenced by external light reflecting off the surface of the path, causing the line detection algorithm to perceive it as an obstacle, leading the robot to turn back..

Alternating current servo motor and programmable logic controller coupled with a pipe cutting machine based on human-machine interface using dandelion optimizer algorithm - attention pyramid convolution neural network

<abstract> <p>The proposed research addresses the optimization challenges in servo motor control for pipe-cutting machines, aiming to enhance performance and efficiency. Recognizing the existing limitations in parameter optimization and system behavior prediction, a novel hybrid approach is introduced. The methodology combines a Dandelion optimizer algorithm (DOA) for servo motor parameter optimization and an Attention pyramid convolution neural network (APCNN) (APCNN) for system behavior prediction. Integrated with a Programmable Logic Controller (PLC) and human-machine interface (HMI), this approach offers a comprehensive solution. Our research identifies a significant research gap in the efficiency of existing methods, emphasizing the need for improved control parameter optimization and system behavior prediction for cost reduction and enhanced efficiency. Through implementation on the MATLAB platform, the proposed DOA-APCNN approach demonstrates a noteworthy 30% reduction in computation time compared to existing methods such as Heap-based optimizer (HBO), Cuckoo Search Algorithm (CSA), and Salp Swarm Algorithm (SSA). These findings pave the way for faster and more efficient pipe-cutting operations, contributing to advancements in industrial automation and control systems.</p> </abstract>

Sistem Otomatis Pengendalian Pakan Ikan dengan Sumber Energi Matahari Menggunakan Mikrokontroller Arduino

Humans have made many discoveries in the modern era that make work easier, including advances in technology, healthcare, agriculture, and cultivation. Automated methods and equipment are among thedevelopments that make human work easier. As a result, a device for feeding fish that can functionautomatically according to the feeding plan and time is designed using a servo motor controller for the fishfeeder powered by solar energy and an Arduino microcontroller module as the controller program. Solarpanels are power plants that receive power from sunlight and are equipped with a charge controller. Theinverter acts as a DC to AC voltage converter, while the battery stores and distributes the voltage. Thescheduling system used by this tool is determined by Arduino software that has been programmed in astructured manner. It will operate on a pre-set schedule, which requires it to be used between 8 am and 5pm, and its output capacity of 10 feed container valve openings is set consistently and pre-programmed.During the test, the average power consumption was 142.75 watts during the day and 125.72 watts at night,with a total consumption of 108.93 watts at night.

Arduino-Based Safety System for Gas Flame Monitoring, Leakage Detection, and Utensil Presence Using Sensor Integration and SMS Alerts

Abstract: In this research project, we present an innovative Arduino-based safety system designed to enhance household gas safety by monitoring gas flame, detecting gas leakage, and ensuring the presence of utensils in the cooking area. By integrating various sensors such as a flame sensor, gas leakage sensor, and ultrasonic sensor, coupled with servo motor control and GSM technology, our system provides real-time monitoring and proactive response to potential hazards. This project showcases the integration of hardware components and intelligent control algorithms, enabling an efficient and reliable safety system. The use of Arduino as the central control unit provides a flexible and cost-effective solution for gas safety applications. Experimental results demonstrate the effectiveness of our system in detecting potential hazards, triggering appropriate actions, and notifying users via SMS alerts.

Implementation Process Simulation and Performance Analysis for the Multi-Timescale Lookup-Table-Based Maximum Power Point Tracking under Variable Irregular Waves

The efficacy of the multi-timescale lookup-table-based maximum power point tracking (MLTB MPPT) in capturing energy at various fixed sea states has already been demonstrated. However, it remains imperative to conduct a more comprehensive evaluation of the MPPT tracking performance under varying sea states in practical scenarios. Additionally, it is crucial to engage in an in-depth analysis of the dynamic process and energy loss/consumption associated with MLTB MPPT implementations. This paper focuses on the implementation process simulation and performance analysis for the MLTB MPPT under variable irregular waves. Firstly, the structure of the wave power controller based on a MLTB MPPT algorithm is described in detail, as well as that of a controlled plant, known as a novel inverse-pendulum wave energy converter (NIPWEC). Secondly, mathematical models for the MLTB MPPT are developed, taking into account the efficiency of each link. In this paper, we present simplified modelling methods for both permanent magnet synchronous generator (PMSG) vector control and permanent magnet synchronous motor (PMSM) servo control. Finally, the tracking performance of the MLTB MPPT in the presence of variable irregular waves is comprehensively analyzed by simulating the implementation process and comparing it with two other MPPT algorithms, i.e., the frequency- and amplitude-control-based MPPT and the lookup-table-based internal mass position adjustment combined with the optimal fixed damping search. Results show that the MLTB MPPT (Method 2) is a competitive algorithm. Besides, a significant portion (>12%) of the time-averaged absorbed power is actually lost during the power generation process. On the other hand, the power required for a mass-position-adjusting mechanism is relatively small (approximately 0.2 kW, <1.5%). The research findings can offer theoretical guidance for optimizing the operation of NIPWEC engineering prototypes under actual sea conditions.