Control System Design Research Articles

A Lyapunov-based control design for centralised networked control systems under false-data-injection attacks

ABSTRACT A central processing unit receives data from all agents and transmits control commands in a Networked Control System (NCS) which is centralised. Centralised NCSs have numerous applications in industrial settings due to their efficiency, simplicity and cost-effective design. However, centralised NCSs are vulnerable to false data injection (FDI) attacks. Despite the fact that researchers have developed detection and mitigation defense mechanisms during past several years, most of these methods have focused on systems with linear dynamics. Furthermore, the existing literature only assumes the injection of FDI attacks on measurement signals. In this paper, we assume that an adversary has injected the FDI attack into both state measurements and control signals with nonlinear dynamics while considering communication noises and disturbances. We propose a secure nonlinear control design that mitigates FDI attacks in real time by combining learning and model-based approaches. We used Lyapunov stability analysis to design the controller, estimator and updating laws of the neural network (NN). In addition, we selected a network of two robots with Euler–Lagrange dynamics to illustrate the robustness of the proposed controller and estimator.

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Adaptive output feedback control system design for nonlinear systems via neural networks

AbstractAdaptive output feedback control based on output feedback exponential passivity (OFEP) has a simple structure and strong robustness in regard to disturbances and system uncertainties. However, it is difficult for most nonlinear systems to satisfy the conditions of OFEP. Thus, the introduction of a suitable parallel feedforward compensator (PFC) to construct an OFEP‐augmented system with the controlled system has been used, but the control output cannot achieve perfect tracking because of the output of the introduced PFC. As a solution, introducing a feedforward (FF) input to build a 2 degree of freedom (2‐DOF) is a simple and effective way to solve this problem. In this paper, we propose the design schemes for suitable PFC and FF input of nonlinear systems via the use of neural networks (NN), respectively. Besides, to cope with possibly present input disturbances, we also provide a method to achieve disturbance compensation based on NN to reduce their interference. Finally, the effectiveness of all proposed design schemes is confirmed through numerical simulations.

Precision Regulation and Forecasting of Greenhouse Tomato Growth Conditions Using an Improved GA-BP Model

Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

Newly Constructed Subway on Over-Track Bridge Safety and Vibration Reduction Measure

Primarily generated at the interface between the wheel and the rail, railroad vibrations then propagate through the supporting soil. If these vibrations reach nearby bridges and buildings, they amplify the vibration nuisance and cause ground noise, which has detrimental effects on nearby residents, sensitive equipment, and historic structures. By analyzing measured data from metro vibration field vibration experiments, this article attempts to contribute to the body of knowledge on environmental vibration propagation patterns by offering insightful conclusions. Before analyzing the deformation response of the metro jet system (MJS) vibration isolation piles to the structure and the ground, we investigated the effect of MJS vibration isolation piles in the ground of the existing subway tunnel structure on the control of vibration of the proximate structure and conducted dynamic tests on the vibration of bridges without vibration isolation measures caused by operating subway trains. The tests determined that the acceleration of the bridge’s lateral vibration exceeded the code limit; one of the contributing factors was that the bridge’s structure had already sustained damage. The utilization of MJS isolation piles was also discovered to safeguard the extant bridge pile foundations. The paper presents an innovation in the form of economically viable vibration mitigation strategies that were implemented subsequent to the identification that the lateral vibration acceleration of the preexisting bridge surpassed the prescribed code standards. Considerable insight is gained regarding the design and implementation of vibration control systems for structures situated near caverns, encompassing deep foundation works.

Digital twin model of a large scale hot molten metal ladle pouring system

AbstractIn steel-making processes, large quantities (frequently exceeding 300 t) of liquid metal are transferred between vessels. In Basic Oxygen Steel (BOS) making process, metal is poured from Hot Metal (HM) ladles, utilising overhead gantry cranes, into furnaces for further processing. Due to the large quantities of liquid metal poured, this operation poses significant safety concerns associated with metal spillage and releases of heat emissions. This can further lead to damage being caused to surrounding infrastructure. Pouring automation can reduce the likelihood of metal spillage, optimising ladle movement for reduction in heat emission releases. Given the hazardous nature of this operation, robust testing and evaluation of automated crane pouring movements is required prior to their application. A digital twin (DT) model of an overhead gantry crane/HM ladle system is presented here, intended to provide a safe testing environment for controlled pouring movement and serve as a testbed for control system design studies. Accurate crane movement is achieved using multi-body dynamics, solving for non-linearities present due to rigid joint frictional components. The flow rate of HM is estimated through the application of a dynamic model, allowing the modelling of system dynamics due to differences in HM pouring weights. The devised DT model is evaluated by simulating real crane movement and making a comparison on the resultant changing HM weight inside the ladle. The devised DT removes the need for construction of a physical model or performing tests directly on the HM pouring system.

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%.

Quadrotors’ double-loop controller design with tensor product model transformation and partial fully actuated method

In the existing work of tensor product (TP) model transformation, the TP model transformation-based work on the quadrotor’s control system design is scarce, the direct TP model transformation control strategy that applied to the quadrotor fails due to the calculation complexity, infeasibility of the huge amount of linear matrix inequalities, and the complexity of solving the linear matrix inequalities. To solve this problem, a partial TP model transformation-based double loop fuzzy controller has been studied in this work, the double-loop hybrid control scheme combines the fully actuated control method and the TP model transformation, while the fully actuated control method is used to the position subsystem control loop, and the TP model transformation based fuzzy controller is applied to the attitude control of the quadrotor. Moreover, for comparison purpose, a varying-input method based on TP model transformation is extended to the quadrotor’s system control. The double-loop hybrid control scheme could also be extended with other TP model transformation based tensor sampling methods, such as, uniform sampling method and varying-input method. At last, the proposed algorithms are evaluated and compared on Parrot Mambo Minidrone. Moreover, the varying-input control method is also evaluated on MFP450 and CUAV V5+ based hexarotor to verify the proposed method.

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.

Flying qualities based time-varying stability augmentation system design for tiltrotor conversion control

Tiltrotors have three flight modes that pose control problems and quality defects during the conversion process. To address this, a novel flying qualities-based time-varying stability augmentation system is designed to achieve multi-mode, nonlinear, and time-varying stability. The system integrates a nonlinear time-varying control law with the flying qualities requirements for all three flight modes. It consists of an inner and outer loop control framework, where the control law in the inner loop is designed based on the Lyapunov theorem of stability. The reference models in the outer loop are derived from the flying qualities criteria to meet level one flying qualities requirements. To evaluate the conversion process, a time-varying flying qualities evaluation method is developed, which includes the conversion path, pilot model, and time-varying flying qualities index. The proposed time-varying stability augmentation control system is then tested through simulation during the conversion process. A pilot-aircraft closed-loop system is established for conducting experiments. Comparison between simulation results and pilot-in-loop experiment results demonstrates the effectiveness of the proposed control system. Furthermore, it proves that the evaluation method is suitable for analyzing time-varying systems. This research can be valuable in designing and evaluating stability augmentation controls for strongly time-varying systems.

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.