Multiple Control Systems Research Articles

A comparative study based on control system for a pulse detonation engine

Pulse Detonation Engine has been a point of interest in the propulsion industry for some time and the interest has been rising due to its better output and results. But any system can perform its task efficiently based on the efficiency of its control system. In this paper we have presented a comparative study between multiple control systems- one based on Bluetooth technology, the other based on infrared sensor technology and one based on wired electrical system. For the wireless system, the signal received from either of the media is passed to the various sensors and systems connected through an Arduino board that further controls the solenoid valves and ignition system. Primarily, a control system circuit is developed using Arduino board and different sensors to connect the fuel supply and ignition system. In the next stage, Bluetooth sensors are connected using an android app and then an infrared sensor based system is integrated with the Arduino to control the engine and the performance of the two systems are compared. Whereas for wired system, every sub system is controlled through optical wires including the solenoid valves, the injection system and the sensors.

A Convolutional Neural Network Classifies Beat-to-Beat Arterial Pressure Spectrograms and Wavelet Transforms according to Age, Sex, and Metabolic State: Novel Frequencies for Cardiovascular Risk Appraisal

Hemodynamic homeostasis is under the control of multiple systems. Continuous blood pressure (BP) time series carry information relevant to the pathophysiological status of the cardiovascular system. Beside average BP, different measures of variability provide prognostic value for distinguishing between cardiovascular risk states. The latter are derived from time- and frequency- domain analysis of BP recordings and are limited by their capacity to provide cumulative risk appraisal among different risk groups. The prediabetic state is associated with cardiovascular risk which is often described to be age- and sex-specific. In this study, we sought to determine distinctive features of continuous arterial pressure (AP) time-series for risk appraisal in a pre-established prediabetic rat model. To overcome the shortcomings associated with conventional BP variability parameters, we trained a convolutional neural network (CNN) using spectrograms and scalograms generated from short-term Fourier- and Morlet wavelet- transforms of AP time series, respectively, from male and female prediabetic rats fed a mild hypercaloric diet for 12- or 24- weeks and their corresponding controls. The CNN consisted of 5 convolutional layers, separated by batch normalization, ReLU activation, and max pooling. The outcome of the fifth convolutional layer had 40% of the data dropped out and delivered to a fully-connected dense layer and then to a softmax function. The data was divided into 80% training, 10% validation, and 10% test sets. The model classified spectrograms and scalograms with test accuracy of 90%+ for the different binary and multi-class classification tasks. On backward propagation of model scores, different salient frequencies were identified as critical features for classification of AP time-series. The identified frequencies were much higher than those typically used in conventional power spectral density (PSD) analysis. Using 3-way ANOVA or principal component analysis for comparison and clustering of the different cardiovascular risk groups of age, sex, and metabolic disease, respectively, according to PSD at the identified peaks does not yield suffcient risk appraisal. This indicates that the CNN possibly captures non-linear relations which are undetectable using inherently linear methods.The results indicate sex- and age-specific patterns of cardiovascular deterioration in prediabetes. Use of artificial intelligence models for the identification of abnormal BP fluctuation patterns can provide suffcient risk appraisal needed for early intervention, particularly in at-risk patients lacking clinical signs of overt cardiovascular disease. None. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Demonstration of Autonomous Driving Control for a Retrofitted Wheel Loader

Most research on automating gravel pile transport using wheel loaders has been performed primarily through simulations. Thus, studies should evaluate the usefulness of automatic gravel pile transportation by demonstrating it with an actual wheel loader. This study demonstrates automatic driving control using a retrofitted 3-ton wheel loader for gravel pile transportation. The driving model of a retrofitted wheel loader, in which multiple control systems are interlocked, is considered a simple control model with one input and one output for the pedal and vehicle velocity as well as for the steering wheel and steering angular velocity. In this study, we propose a simple and practical method for constructing a driving model via simple response analysis using an actual machine by constructing a feedforward control model based on control input/output using step responses. In this study, feedforward control was applied to the translation of the vehicle, which has a large dead time. By generating the path following the target point from the vehicle predicted position after the dead time from the driving model, the appropriate control input value calculation considering the dead time was performed. By applying the proposed method to a retrofitted wheel loader in a real environment and evaluating the control performance through control experiments, the effectiveness of the proposed method in practice was demonstrated.

Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach

In the Multiple Model Control (MMC) strategies, a bank of simple local models is used to describe the behavior of complex systems with vast operation space. In this approach, the system operation space is divided into several subspaces, and in each subspace, a simple local model is assigned to describe the system behavior. This study addresses the two main challenges in this field which involve determining the optimal number of required local models to form the model bank and identifying the optimal distribution of the local models across the system operation space. Providing appropriate answers to these questions directly affects the performance of the MMC system. In this paper, GA-based automatic clustering method is suggested to form an optimal model bank. In this regard, an appropriate mapping is established between the concepts of MMC and automatic clustering, and a novel unsupervised algorithm is designed to determine the optimal model bank. Unlike the existing methods in the literature, the proposed method can form the global optimal model bank without entrapment into local optima regardless of the initial conditions of the used search algorithm. In this paper, the formation of the optimal model bank using the proposed method is investigated by considering the spacecraft attitude dynamics as a complex, MIMO, non-linear case study and its satisfactory and promising performance is demonstrated.

Phase‐Stable Multidimensional Coherent Spectroscopy Based on Dual‐Comb Interferometry

AbstractMultidimensional coherent spectroscopy (MDCS) is a highly intriguing field for the unique capacities to unravel quantum pathways of microscopic dynamics and discern different dipole–dipole interactions. These capabilities are not simultaneously accessible through any other techniques. The foundation of MDCS lies in the stable phase and accurate time information of the multi‐pulse sequences involved. Achieving the phase stability has traditionally relied on ingenious geometric optical designs and multiple feedback control systems, albeit at the cost of complexity. In this letter, a phase stability scheme for high‐resolution MDCS is proposed by replacing traditional laser sources with two highly coherent frequency combs. By employing dual‐comb interferometry, the phase distribution across a broad spectral range and the time information of all the excitation pulses are precisely measured, even in setups that lack phase stability. In the experiment, a rephasing MDCS of rubidium atoms is obtained by applying this approach, wherein the comb resolution is utilized to differentiate the couplings of hyperfine energy levels. This scheme simplifies the structure of MDCS and holds potential for applications in other MDCS systems involving many‐body interactions and non‐absorptive resonances.

Global Coordinated Stabilization of Multiple Simple Mechanical Control Systems on a Class of Lie Groups

Global Coordinated Stabilization of Multiple Simple Mechanical Control Systems on a Class of Lie Groups

Analysis of fuzzy and neural controllers in direct torque controlled synchronous motors

<p><span>In this study, multiple intelligent control systems for direct torque-controlled Synchronous motors are implemented and compared. Using a lookup table to pick a vector through the inverter voltage space, the direct torque control (DTC) system can be obtained. To replicate the state selector in relation to the look-up table, intelligent controllers are deployed. Intelligent logic controllers like fuzzy and neural are used to regulate the performance of permanent magnet synchronous motors (PMSM). In steady-state applications, neural and fuzzy controllers reduce the torque ripple and stator current harmonic distortion. These outcomes are compared with those obtained when the synchronous motor was put under the basic direct torque control method using a proportional integral (PI) controller. The accuracy and effectiveness of the suggested control topologies have been verified using computer simulation software like MATLAB/Simulink.</span></p>

Fly by Wire Advancements in Aviation over Conventional Flight Control Systems

Abstract: This report shows the advancement of flight control systems, from the old human controlled mechanical systems to the modern fly by wire systems in use today. It also compares the two systems, and investigates the reasons behind the change. This change is a significant and an extraordinary representation of the advancements made in the avionic industry and of the development of aeronautical technologies. The fly-by wire system represents a large fast forwarding in the industry, from complex mechanically controlled systems, to large hydraulically controlled systems, to computer assisted fly by wire system. The use of fly-by wire systems has significantly reduce the weight of the flight control systems, while increasing the scope to add multiple redundancy flight control systems, which can increase the safety in case of failures in a particular system. Further development in fly-by wire systems comes in the form of advancing from analog to digital computing systems

Design of Multi-circuit Motor Control System based on Fabric Defect Detection Device

In order to realize the motion control of multiple motors of the fabric defect detection device, a ZYNQ-based multiple motor control system is designed in conjunction with the fabric defect detection device. In this system, pulse width modulation technique is used to control the multi-channel motors. The flexible logic operability of FPGA is utilized to achieve real-time high-speed control of multi-channel pins, where FPGA is responsible for data caching and acquisition control, and ARM is responsible for data communication and transmission. The logic array was programmed and simulated in Vivado software. Simulation results show that the system is able to control multiple motors synchronously and stably, verifying the effectiveness and feasibility of the system.

Performance evaluation of political teaching MOOC system based on task scheduling and AI system

In the network control system, due to the limitation of network bandwidth and control system conditions, the information transmission of sampling and control must be completed in a certain time, otherwise it will cause information transmission delay, thus reducing the control performance of the network system. This paper builds a MOOC political teaching platform based on task scheduling and artificial intelligence system, and the use of MOOC ideological and political education model can improve students' participation and practicability. Under the MOOC model, students should be able to learn by participating in online video courses, skillfully applying theoretical knowledge of ideological and political education, and then by participating in physical classroom discussions. The simulation results show that the system can realize real-time data transmission, and the information transmission of sampling and control must be completed in a certain time, otherwise it will cause information transmission delay, thus reducing the control performance of the network system. Due to the limitation of network bandwidth, if multiple control systems work together, there will be transmission delays and other situations, which will directly affect the control performance of the system and cause system instability.

Fault isolation and fault‐tolerant control for nonlinear stochastic distribution control systems with multiplicative faults

AbstractIn this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously.

An IoT-based Smart Home Application with Barrier-Free Stairs for Disabled/Elderly People

Home automation based on the Internet of Things (IoT) includes various components such as lighting, security, and remote control. Smart Home (SH) components should be varied and customised according to the user's specific needs. Therefore, in this study, a SH system needed by users with specials is designed and implemented on a model. The NodeMCU microcontroller with ESP8266 Wi-Fi module, Radio Frequency Identification (RFID) tags, temperature-humidity, motion detection, gas and moisture sensors and several actuators are used to build the system. Thanks to If This Then That (IFTTT), Google Assistant, and Blynk, the SH components can be managed remotely and with voice commands via a user-friendly Android-based mobile interface. The multiple control system and the ability to control the home components with different methods make this study comprehensive. In particular, the barrier-free stairs design has added innovation to the SH system for disabled people. Thus, the accessibility, security, and comfort requirements of disabled people are met, and their quality of life is improved to live independently.

Dual-output PID transient control of an electric-assisted air charge system

Turbo-compressor assembly (also called turbocharger) is added to diesel engines to increase their power density and improve performance. However, during power step-up operations, the added turbocharger system could lead to a significant delay of engine response (also called turbo-lag) which negatively impact drivability. In this paper, an external electric compressor (eBoost) is added to a turbocharged, large displacement diesel engine to significantly reduce turbo-lag, along with a bypass valve. Now, the air charge system has two control targets (boost (intake manifold) pressure and rate of exhaust-gas recirculation (EGR)) but with four control actuators including EGR valve, variable geometry turbo (VGT) vane position, eBoost speed and bypass valve, making it challenge for developing the associated control scheme. In this paper, a dual-output PID (proportional-integral-derivative) controller is proposed for controlling the boost pressure using the VGT and eBoost to reduce turbo-lag, and a transition logic is also devised to detect transient operating conditions to activate the eBoost, along with the closing and opening of the bypass valve, with maximum benefit. The EGR rate PID control remains unchanged. The addition of the eBoost is shown to improve transient response time by up to 55% and reduce transient NOx emissions by up to 42% during transitional operations without negatively impacting steady-state engine performance or emissions. Note that transient performance can be further improved for production implementation when a single engine control module is used by eliminating communication delay among multiple control systems.

PEMBELAJARAN KONTROL SISTEM PNEUMATIK SEBAGAI PENUNJANG KOMPETENSI SISTEM HIDROLIK DAN PNEUMATIK DI SMK NEGERI 1 ADIWERNA

Community Service Activities carried out are providing understanding and thinking patterns about pneumatic systems control that work electro-pneumatically to students of SMK Negeri 1 Adiwerna grade XII majoring in Technical Light Vehicle (TLV). Knowledge of pneumatic systems is one of the competencies that are usually difficult to understand by TLV students. Whereas pneumatic systems have many applications in the industrial world. By using a facility in the form of an Electro-Pneumatic Trainer, the delivery of discussions about pneumatic system control will be more easily understood by students. By referring to the wiring diagram, students are explained to be able to read and understand the intent and working process of the pneumatic systems control and put it into practice immediately. There are 3 types of pneumatic systems control delivered, (1) single-cylinder pneumatic control systems, (2) single-cycle multiple cylinder pneumatic control systems, (3) continuous cycle multiple cylinder pneumatic control systems. Students are very enthusiastic about receiving explanations and practicing all three control systems.

Disturbance rejection enhancement using predictive control for the fixed‐wing UAV with multiple ailerons

SummaryThe performance of small fixed‐wing unmanned aerial vehicles (UAVs) is easily degraded by exogenous disturbances. In an attempt to improve the performance, the structural change is made in conventional small fixed‐wing UAV through segmentation of each aileron control surface into multiples. Detailed system identification experiments are performed on each aileron pair in the wind tunnel to acquire linear dynamic models for the roll attitude of the UAV. These experiments have provided the transfer functions for the aileron control surfaces based on corresponding frequency response. Three distinctive model predictive controllers are designed and deployed in real‐time hardware to achieve the roll attitude control. The roll attitude control experiments are validated in wind tunnel under both normal and turbulent environments. The results show that multiple input and single output control system provides significant improvement in the roll attitude and disturbance rejection performance. The collective actuation of multiple control surfaces improves roll stability by to when compared to the single aileron pair based conventional control in the presence of turbulent flight conditions.

Dynamic Wide Area Situational Awareness: Propelling Future Decentralized, Decarbonized, Digitized, and Democratized Electricity Grids

The millions of consumer-owned distributed energy resources (DERs) forecast for the grid by 2050 will trigger a major system change away from centralized monopolistic utilities to decentralized community projects exploiting innovative business models. Such a disruptive change, needed to switch to a carbon-neutral economy, requires rethinking both the economics and dynamics of power systems, knowing that the electric power sector is going digital. DER-driven uncertainties will impose costly operational margins and preventive measures based on solving very complex optimization problems. Keeping human operators in the loop to supervise actions can limit reaction time severely by making it difficult to respond in a timely manner when multiple control systems are required to stabilize the grid. The operator will hence need to be assisted by an artificial intelligence system trained at learning “good” decisions by imitating operators and assessing the associated operational risk. Hierarchical monitoring and control systems working in tandem with decentralized markets and resources will allow the true secure limits of decentralized, decarbonized, digitized, and democratized (4D) grids to be identified. This will be done with preventive/corrective actions executed in seconds while balancing grid cost versus safety, reliability, and stability.

The role of reinforcement learning and value-based decision-making frameworks in understanding food choice and eating behaviors.

The obesogenic food environment includes easy access to highly-palatable, energy-dense, "ultra-processed" foods that are heavily marketed to consumers; therefore, it is critical to understand the neurocognitive processes the underlie overeating in response to environmental food-cues (e.g., food images, food branding/advertisements). Eating habits are learned through reinforcement, which is the process through which environmental food cues become valued and influence behavior. This process is supported by multiple behavioral control systems (e.g., Pavlovian, Habitual, Goal-Directed). Therefore, using neurocognitive frameworks for reinforcement learning and value-based decision-making can improve our understanding of food-choice and eating behaviors. Specifically, the role of reinforcement learning in eating behaviors was considered using the frameworks of (1) Sign-versus Goal-Tracking Phenotypes; (2) Model-Free versus Model-Based; and (3) the Utility or Value-Based Model. The sign-and goal-tracking phenotypes may contribute a mechanistic insight on the role of food-cue incentive salience in two prevailing models of overconsumption-the Extended Behavioral Susceptibility Theory and the Reactivity to Embedded Food Cues in Advertising Model. Similarly, the model-free versus model-based framework may contribute insight to the Extended Behavioral Susceptibility Theory and the Healthy Food Promotion Model. Finally, the value-based model provides a framework for understanding how all three learning systems are integrated to influence food choice. Together, these frameworks can provide mechanistic insight to existing models of food choice and overconsumption and may contribute to the development of future prevention and treatment efforts.

Multiple Instance Learning With Random Forest for Event Logs Analysis and Predictive Maintenance in Ship Electric Propulsion System

In this article, a novel weakly supervised machine learning approach is proposed for intelligent predictive maintenance (IPdM). It employs balanced random forest and multiple instance learning based on event logs from ships’ electric propulsion systems. The objectives are predicting failure likelihood, time to failure, and explainable predictions to ensure timely crew intervention. The IPdM approach uncovers, then learns, and classifies <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequences of</i> events that represent early causes or symptoms to forecast imminent failures. In particular, this article contributes effective solutions to irregular, imbalanced, and unlabeled data issues where conventional methods become obsolete. First, the events occur at irregular intervals; they include alarms, warnings, and operational information collected across multiple units and control systems. Second, the datasets exhibit extreme imbalance due to few failures and multiple failure modes; this entails biased predictions. Third, the training datasets are weakly labeled; only the failure timestamp is known without any expert input on prior causes or early symptoms. Temporal random indexing is proposed to transform textual log messages into a numerical lower dimensional space where timeseries analyses are applicable. Balanced random-forest models are developed for unbiased classification and regression. The overall approach learns recursively the ungiven data labels while training the base learners. The IPdM approach is validated through millions of events of multithousand types collected from two years of seagoing vessels. It successfully forecasts actual propulsion failures and performs better when compared with contemporary methods.

The Influence of Cooperation on the Operation of an MPC Controller Pair in a Nuclear Power Plant Turbine Generator Set

The paper discusses the problem of cooperation between multiple model predictive control (MPC) systems. This approach aims at improving the control quality in electrical energy generation and forms the next step in a series of publications by the authors focusing on the optimization and control of electric power systems. Cooperation and cooperative object concepts in relation to a multi MPC system are defined and a cooperative control solution for a nuclear power plant’s turbine generator set is proposed. The aim of enabling information exchange between the controllers is to improve the performance of power generation. Presented and discussed simulation tests include various variants of information exchange between the turbine and synchronous generator MPC controllers of the nuclear power plant.

Autophagy‐independent mitochondrial quality control: Mechanisms and disease associations

AbstractMaintaining a healthy and functional mitochondrial network is crucial for cell survival and function. Protein misfolding and dysfunction are particularly prevalent in them due to physiological adaptations and stress conditions. For mitochondria to function properly, multiple quality control systems have evolved to ensure that there are enough mitochondria to meet cells’ needs, damaged mitochondrial proteins or mitochondrial parts can be eliminated using these pathways. Several mechanisms control mitochondrial quality, including protein, organelle, and cellular levels. As the extensive study of canonical mitochondrial quality‐mitophagy, this paper reviews the processes and mechanisms of mitochondrial quality control independent of autophagy, including mitochondrial protein and DNA degradation, mitochondria‐derived vesicles and mitochondria‐derived compartments, mitochondrial secretion, tunneling nanotubes, mitocytosis, and mitolysosome exocytosis. Understanding these novel quality control pathways may provide insights into mitochondrial homeostasis and for developing targeted treatments for diseases where these systems fail.