Control System Design Research Articles

AC PWM Control System in Induction Motor using Microcontroller

The demand for efficient and precise control of induction motors has led to the development of advanced control systems, among which AC PWM (Pulse Width Modulation) stands out as a prominent technique. This project focuses on the design and implementation of an AC PWM control system for an induction motor utilizing a microcontroller. The system employs a microcontroller to generate PWM signals, controlling the power electronics responsible for modulating the voltage supplied to the motor. The report details the architecture, components, and methodology employed in the project. A comprehensive discussion on the PWM control algorithm, motor drive circuitry, and sensor integration is presented. The microcontroller programming and experimental results showcase the effectiveness of the proposed system in achieving precise control over motor speed and efficiency. The challenges faced during the project are discussed, along with the corresponding solutions implemented. The report concludes with a summary of achievements and suggestions for future enhancements. Overall, this project contributes to the field of motor control systems, offering a reliable and efficient solution for induction motor control through AC PWM with microcontroller integration

Design of intelligent agricultural landscape planning and ecological balance control system based on remote sensing images

Abstract With the modernization of agricultural production and the acceleration of urbanization, agricultural landscape planning and ecological balance control have become important issues in promoting sustainable development. Traditional agricultural production methods have problems such as resource waste, environmental pollution, and ecological damage. This article aimed to achieve scientific planning and management of agricultural landscapes, and promote the efficiency of agricultural production, rational utilization of resources, and protection of the ecological environment, thereby promoting sustainable agricultural development. This study first focused on denoising and feature extraction of agricultural remote sensing data images, using remote sensing technology to obtain agricultural landscape information. After that, artificial intelligence was adopted to achieve intelligent agricultural landscape planning, with the goal of maintaining ecological balance and promoting efficient, eco-friendly, and sustainable agricultural production. This study took the landscape of a certain city as an example to test the processing effect of remote sensing images, and it was proved that the effect was good. We tested the performance of an intelligent agricultural landscape planning and ecological balance control system based on remote sensing images. According to the experimental results, it can be concluded that the resource utilization efficiency of farmland A monitored by the intelligent agricultural landscape planning system using remote sensing image technology was 82%, while the traditional one was only 60%. This indicated that the collection effect of remote sensing image technology was much better than that of traditional technology. This article comprehensively and timely monitored and evaluated farmland through remote sensing image technology, including crop growth status, soil moisture, nutritional status, etc. The system can provide precise planting and management suggestions for farmers based on this information, and help them optimize farmland layout, crop selection, and irrigation management, thereby improving the production efficiency of farmland and crop yield.

Rectification and converter control of the FPSLGs for energy storage applications

AbstractThis article presents two power converters with controllers attached to the Free‐Piston Stirling Linear Generator (FPSLG) and energy storage system (ESS). The rectifier uses hysteresis‐SVPWM current regulation and CB‐SPWM, while the buck‐boost converter utilizes the dual‐loop PI control method. The storage battery receives energy from a linear‐generator with a rectifier and converter. The FPSE and linear motors in the FPSLG convert thermal to electrical energy. A two‐level electrical energy conversion technique using a three‐phase AC signal to DC and DC‐DC converter has been devised to reduce output electrical energy oscillation as well as stabilize the generating system's DC power. Initially, the FPSE's nonlinear model and the three‐phase permanent magnet linear‐motor's linear mathematical model were created and followed by the ESS and control mechanism simulation. The simulation shows that the ESS system and control approach can give stable power to the storage battery using linear‐generator power. The PR controller replaces the PI controller in the position loop to improve frequency and displacement tracing. The control system design was simulated in MATLAB and the topology's feasibility and correctness were confirmed. Comparative analysis shows that, rectifier uses the H‐SVPWM's unity power factor of AC and stability controllability of the DC‐side voltage is better than the CB‐SPWM approach.

Reinforcement Learning for Closed-loop Regulation of Cardiovascular System with Vagus Nerve Stimulation: A Computational Study.

(Objective). Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study. (Approach). Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate (HR) and the mean arterial pressure (MAP) for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control (PILCO). (Main results). Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of Deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems. (Significance). We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

Enhancing Autonomous Underwater Vehicle Decision Making through Intelligent Task Planning and Behavior Tree Optimization

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV’s capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios.

A new control method for leader–follower consensus problem of uncertain constrained nonlinear multi-agent systems

The design of Adaptive Fuzzy Sliding-Mode Control (AFSMC) is discussed in this study for the distributed leader–follower consensus problem of nonlinear uncertain multi-agent systems with input and state constraints. Avoiding the critical circumstances related to the violation of input and state constraints is an important issue in the design of consensus control systems. The limitations of existing adaptive consensus controllers developed for full-state constrained systems motivated the present novel method without restrictive structural assumptions. To convert the original problem to an unconstrained consensus control problem, the proposed control system uses a barrier function-based state transformation method and the input-state linearization methodology. As a result, each agent’s adaptive fuzzy sliding-mode control input, which consists of a fuzzy system and a robust term, is constructed based on the derived representation of the system dynamics. The fuzzy system is used to approximate the dynamic equations of the agent, its neighbors, and maybe the leader in each local feedback control law structure, and the robust term is designed to compensate for the fuzzy approximation error. The magnitudes of the robust terms and the output vectors of the fuzzy systems are also determined using the proposed adaptation laws. The second Lyapunov theorem and Barbalat’s lemma are used to verify the closed-loop system’s asymptotic stability. The effectuality of the proposed methodology is confirmed by numerical simulations for several Autonomous Underwater Vehicles (AUVs). Simulation results show that by means of the proposed AFSMC, the leader–follower consensus is achieved without state constraint violation and performance degradation.

Fractional order modelling and optimal control of dual active bridge converters

As the construction of new power systems centred around renewable energy gains traction, the installed capacity of new energy sources has experienced explosive growth. Research has shown that the actual external characteristics of inductors and capacitors in circuits exhibit fractional order characteristics. As a core device in dual active bridge converters, the non-integer order of inductors and capacitors has an important impact on their dynamic performance, frequency domain characteristics, and control system design. This paper conducts modelling and control research on dual active bridge converters based on fractional-order calculus theory. First, based on the definition of fractional-order calculus, according to the operating mode of the fractional-order dual active bridge converter and the characteristics of fractional-order components, The fractional-order state space average model was established, and the influence of the order of inductance and capacitance on the amplitude frequency characteristics, phase frequency characteristics and dynamic performance of the converter was analyzed. Secondly, the average model and circuit model were built on the MATLAB/Simulink simulation platform, and the simulation results under different fractional orders were compared. Finally, in order to further improve the control performance of the converter, a fractional-order P I λ control strategy is designed based on the established state space average model of the fractional-order dual active bridge converter and based on the transfer function from the control to the output of the fractional-order dual active bridge converter. The research results show that the fractional-order model can more accurately describe the actual characteristics of the converter. In addition, the fractional-order controller P I λ enables the fractional-order dual active bridge converter to obtain better robustness and improve the dynamic performance of the converter.

Fuzzy sliding mode control of automotive semi-active suspension based on optimization convergence law

Vehicle suspension smoothness and stability are important indicators for evaluating vehicles. This paper firstly establishes a 1/4 automobile semi-active suspension model, which is used as a reference model for comparison by optimizing the canopy model, and secondly designs the sliding mode controller, including the tracking model error design and the control system design, and then incorporates the optimized saturation function convergence law to control the convergence speed, and then combines it with the fuzzy control to improve the accuracy and robustness of the control system. The optimized semi-active suspension control system will enhance the vehicle’s vibration, smoothness, and stability. The final simulation test results show that the optimized sliding mode control suspension body vertical acceleration is reduced by 26.9%, suspension dynamic deflection by 36.7%, and tire dynamic load dynamic load is reduced by 28.4%.

Development of the interferometer control system on HFRC

The CO2 laser interferometer is an important diagnostic system in the HUST Field Reversed Configuration (HFRC) and can provide routine electron density measurement for plasma experiments. A commercial laser (λ = 10.6 μm) with a maximum output power of 20 watts is used as the laser source for the interferometer, which can pose a threat to the safety of experimenters and experimenters' experimental environment. Moreover, most of the components of the system are sensitive to the temperature and humidity of the environment. For the aim of safe and stable operation, it is necessary to develop a CO2 interferometer control system (CICS). A module was innovatively designed with a servo to regulate power according to commands and protect timely when a fault takes place. Additionally, a special device using a stepper motor can automatically regulate and release the laser according to the different stages of the experiment. The hardware design of the CO2 control system incorporates the ESP32 microcontroller, servo and stepper motor, which can automatically monitor, regulate and protect in case a fault takes place. The software implementation of the interaction is based on the OneNET platform and is first used on the CO2 interferometer, linking in the Internet based on Internet of Things (IoT) technologies to each sensor and device in the control system, which can achieve visualization monitoring and remote control. With the operation of CICS, the CO2 interferometer can run stably and be timely protected.

Design and Simulation of Voltage Control System for Simscape Boost Converter Model With Disturbances

Design and Simulation of Voltage Control System for Simscape Boost Converter Model With Disturbances

Design of Control System for the Super-X Test Facility

Design of Control System for the Super-X Test Facility

喷杆倾角控制系统的设计与实验

During field operations of the spray boom sprayer, the distance between the ends of the spray boom and the height of the crop canopy affects the uniformity of spraying, requiring operators to manually adjust the spray boom to be parallel to the crop canopy, which impacts operational efficiency. This study presents the design of a boom tilt control system, consisting of a main control node, distance measurement node, vehicle tilt detection node, and spray boom tilt control node. The bus communication protocol for the spray boom tilt control system is defined according to the ISO11783 standard, and a serial communication network is designed, along with the development of a real-time dynamic monitoring interface for the spray boom. The system automatically monitors the height of the boom and the tilt of the vehicle, makes decisions based on the detection information, controls the electric actuator, and adjusts the tilt of the boom. Leveraging the advantages of fast computing speed and user-friendly human-machine interface of the PC, as well as the high cost-effectiveness and small size of the microcontroller, and the multi-master-slave structure of the CAN bus, this system can complete data acquisition, processing, and other functions required for spray boom tilt control, achieving automatic adjustment of spray boom tilt. This enhances spray uniformity and operational efficiency of the sprayer, while reducing the workload of operators.

Design of High-Precision Driving Control System for Charge Management

Due to the interaction of accumulated charges on the surface of a test mass with the surrounding electric and magnetic fields, the performance of inertial sensors is affected, necessitating charge management for the test mass. Discharge technology based on Ultraviolet LEDs is internationally recognized as the optimal solution for charge management. Precision driving of Ultraviolet LEDs is considered a key technology in charge management. This paper presents the driving control system used for Ultraviolet LEDs, achieving precision pulse-width-modulation-type current output with controllable pulse width and amplitude. The system generates the pulse-width-controllable pulse voltage signal via analog pulse-width modulation, and subsequently regulates the amplitude of the PWM signal through range switching. To convert the voltage into the pulse-width-modulation-type driving current, the improved Howland current source is employed. The test results demonstrate that the driving control system can output controllable current in the range of 0.01 mA to 10 mA, with a minimum step of 0.01 mA. The accuracy of the current reaches 1%, the stability within 1 h is better than 1%, and the load regulation is better than 2%. The driving control system provides an important reference for the integration of charge management system and the precision drive control method for LEDs.

Integrated elastic identification and LOS angular rate extraction for slender rockets: A continuous-discrete maximum correntropy Kalman filter approach

Slender rockets experience significant elastic deformation during flight due to aeroelastic forces, impacting their dynamic behavior and posing challenges for traditional line-of-sight (LOS) angular rate estimation methods. This paper addresses this challenge by proposing a novel and highly accurate line-of-sight angular rate extraction model. This model integrates elastic identification with line-of-sight angular rate extraction, employing estimated generalized coordinates to calculate seeker elastic deformation. After elastic decoupling of the seeker, the slender rocket's line-of-sight angular rate can be estimated accurately. Additionally, the model incorporates a continuous-discrete maximum correntropy Kalman filter (CD-MCKF) to effectively handle the non-Gaussian measurement noise and limitations associated with discretizing the continuous model. Simulations demonstrate that the proposed method achieves significantly higher accuracy compared to existing approaches. The improved accuracy of this method can provide more precise calculations for the design of slender rocket guidance and control systems, ultimately improving the flight stability and control accuracy of rockets, and laying a solid foundation for the integrated guidance and control research of slender rockets.

Прогнозирование рисковых ситуаций крена катамарана в штормовых условиях

The problem of uncertainty of catamaran behavior during a storm is considered. The main danger of uncertainty lies in the magnitude of the roll by which the catamaran will deviate, since this can lead to adverse consequences with the cargo or the hull of the vessel. The study of this uncertainty is considered from two positions: the study of control actions in the implementation of the worst-case scenarios of unrest at the stage of selecting a control system and online forecasting of roll thresholds. A complex model of catamaran and external disturbances is used. External perturbations are obtained using a formative filter and linear approximation of the Pearson-Moskowitz spectrum, forces and moments. The study of the quality of management is carried out by calculating the most likely trajectories to the specified threshold states and then comparing them with each other. This calculation is performed by solving an optimization problem of the Lagrange-Pontryagin type with an action functional as a criterion and a complex catamaran model and external disturbances as a coupling equation. Using the methods of the theory of large deviations, estimates of the probabilities of movement to threshold values along the most likely trajectories are obtained. Using these estimates and comparing them with the current state of the catamaran made it possible to realize the forecast of risky situations. Examples of trajectories leading to threshold states are given for several cases of wind and heading angles of the vessel obtained with different control circuits. At the same time, it can be seen that depending on the control, fluctuations arise, which must be paid attention to when designing the control. An example of estimating the probability of movement to the threshold state during the rolling of the catamaran is given. The use of threshold state profiles allows not only to obtain useful information in the design of control systems, but also to improve the safety of navigation.

Design and Experiment of an Unoccupied Control System for a Tracked Grain Vehicle.

In order to enhance crop harvesting efficiency, an automatic-driving tracked grain vehicle system was designed. Based on the harvester chassis, we designed the mechanical structure of a tracked grain vehicle with a loading capacity of 4.5 m3 and a grain unloading hydraulic system. Using the BODAS hydraulic controller, we implemented the design of an electronic control system that combines the manual and automatic operation of the chassis walking mechanism and grain unloading mechanism. We utilized a hybrid A* algorithm to plan the traveling path of the tracked grain vehicle, and the path-tracking controller of the tracked grain vehicle was designed by combining fuzzy control and pure pursuit algorithms. Leveraging binocular vision technology and semantic segmentation technology, we designed an automatic grain unloading control system with functions of grain tank recognition and grain unloading regulation control. Finally, we conducted experiments on automatic grain unloading control and automatic navigation control in the field. The results showed that both the precision of the path-tracking control and the automatic unloading system meet the requirements for practical unoccupied operations of the tracked grain vehicle.

Dual-input configuration of three-phase split-source inverter for photovoltaic systems with independent maximum power point tracking

This paper presents a dual-input configuration for the three-phase split-source inverter (SSI) to be used with photovoltaic (PV) systems, it is denoted as DSSI. Compared to using one SSI for each PV input, the proposed configuration uses a lower number of semiconductors. Thus, lower cost along with higher power density can be acquired. The proposed DSSI inherits the brilliant features of the conventional SSI. This includes continuity of input current and high voltage boosting gain. The proposed DSSI offers independent control for the power extracted from each PV source. Thus, independent maximum power point (MPP) tracking is ensured. In addition, no circulating current between the input ports is generated. Simple modulation technique is developed for the proposed configuration. In addition, the control system design to ensure independent MPP tracking along with power quality requirements is illustrated. Comparative study with other dual-input topologies is presented. Simulation, experimental as well as real-time simulation results for DSSI are presented. The results show good performance under dual- and single-input scenarios. In addition, well dynamic performance is verified. Thus, performance at steady-state and dynamic conditions is validated for the proposed DSSI. An efficiency of 92.3 % is achieved by the experimental prototype.

A Gaussian-Process-Based Model Predictive Control Approach for Trajectory Tracking and Obstacle Avoidance in Autonomous Underwater Vehicles

To achieve the efficient and precise control of autonomous underwater vehicles (AUVs) in dynamic ocean environments, this paper proposes an innovative Gaussian-Process-based Model Predictive Control (GP-MPC) method. This method combines the advantages of Gaussian process regression in modeling uncertainties in nonlinear systems, and MPC’s constraint optimization and real-time control abilities. To validate the effectiveness of the proposed GP-MPC method, its performance is first evaluated for trajectory tracking control tasks through numerical simulations based on a 6-degrees-of-freedom, fully actuated, AUV dynamics model. Subsequently, for 3D scenarios involving static and dynamic obstacles, an AUV horizontal plane decoupled motion model is constructed to verify the method’s obstacle avoidance capability. Extensive simulation studies demonstrate that the proposed GP-MPC method can effectively manage the nonlinear motion constraints faced by AUVs, significantly enhancing their intelligent obstacle avoidance performance in complex dynamic environments. By effectively handling model uncertainties and satisfying motion constraints, the GP-MPC method provides an innovative and efficient solution for the design of AUV control systems, substantially improving the control performance of AUVs.

Design and multilevel reconstruction method of intelligent power industry control system based on digital twins

Design and multilevel reconstruction method of intelligent power industry control system based on digital twins

Main approaches and features of the design of aircraft hydro-mechanical control systems

Main approaches and features of the design of aircraft hydro-mechanical control systems