Development Of Control Algorithms Research Articles

Deep Q‐network learning‐based active speed management under autonomous driving environments

AbstractEfficient traffic safety management necessitates real‐time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies to accommodate both conventional and AVs. This paper presents a study on the development of VSL control algorithms using deep reinforcement learning in a microscopic traffic simulation. As the rewards function, time‐to‐collision and speed were considered. To enhance traffic safety, VSL strategies were refined across various market penetration of connected AVs. Analysis revealed that safety and traffic density are improved by 53% and 59%, respectively, in market penetration rate (MPR) 50, marking significant safety improvements in congested and low MPR scenarios. These findings present the importance of developing and evaluating VSL strategies for mixed traffic flow, particularly in the context of increasing the prevalence of connected and AVs.

Development of a Stress-Free Algorithm for Control of Running Platforms Based on Neural Network Technologies

The article discusses the task of predicting human speed using neural network technologies and computer vision to minimize lags in treadmill control systems, which pose a health risk to the user. To solve this problem, a stress-free algorithm has been developed, including: predicting the position and speed of the user on the treadmill; calculating the treadmill speed based on the analysis of the user's position and movement characteristics; data collection and processing schemes for training neural network methods; and determining the necessary number of predicted frames to eliminate lags. The scientific novelty of the research lies in the development of a treadmill control algorithm that combines: computer vision technologies for recognizing the user's body model on the platform; neural networks; and machine learning methods to determine the final human speed based on combining data on the person's position in the frame and the current and predicted speed of the person. The proposed algorithm is implemented using Python libraries, and its validation was conducted during experimental studies analyzing the preceding 10 and 15 frames to predict the next 10 and 15 frames. Comparing machine learning algorithms (linear regression, decision tree, random forest, multilayer, convolutional, and recurrent neural networks) at different lengths of analyzed and predicted frames, the RandomForestRegressor algorithm showed the best accuracy in predicting position, while dense multilayer neural networks performed best in determining current speed. Experimental research has been conducted on applying the developed algorithm and models to determine human speed (achieving accuracy when forecasting in the range of 10-15 frames) as well as integrating them into treadmill control systems. Trials have shown the effectiveness of the proposed approach and the correctness of system operation under real conditions. The developed algorithm allows for not using noise-sensitive sensors that require attachment to the user's body but rather forecasting user actions through analyzing all points of the person's body to reduce lags in various human-machine systems.

Simulation-guided development of advanced PID control algorithm for skin cooling in radiofrequency lipolysis.

The clinical outcomes of bipolar radiofrequency (RF) lipolysis, a prevalent non-invasive fat reduction procedure, hinge on the delicate balance between effective lipolysis and patient safety, with skin overheating and subsequent tissue damage as primary concerns. This study aimed to investigate a novel bipolar radiofrequency lipolysis technique, safeguarding the skin through an innovative PID temperature control algorithm. Utilizing COMSOL Multiphysics simulation software, a two-dimensional fat and skin tissue model was established, simulating various PID temperature control schemes. The crux of the simulation involved a comparative analysis of different PID temperatures at 45 °C, 50 °C, and 55 °C and constant power strategies, assessing their implications on skin temperature. Concurrently, a custom bipolar radiofrequency lipolysis device was developed, with ex vivo experiments conducted using porcine tissue for empirical validation. The findings indicated that with PID settings of Kp =7, Ki =2, and Kd =0, and skin temperature control at 45 °C or 50 °C, the innovative PID-based epidermal temperature control strategy successfully maintained the epidermal temperature within a safe range. This maintenance was achieved without compromising the effectiveness of RF lipolysis, significantly reducing the risk of thermal damage to the skin layers. Our research confirms the substantial practical utility of this advanced PID-based bipolar RF lipolysis technique in clinical aesthetic procedures, enhancing patient safety during adipose tissue ablation therapies.

Model predictive control of blood glucose in critically ill patients using Gaussian processes

Abstract Stress-induced hyperglycemia and high glycemic variability are common in intensive care patients. Several clinical studies show the benefits of tight blood glucose control, including lower mortality. This article presents an algorithm for blood glucose control in the intensive care unit. An Unscented Kalman Filter is developed to estimate the glucose metabolism state and time-varying insulin sensitivity from blood glucose measurements. Gaussian Processes are used to predict future insulin sensitivity changes based on previous measurements. A model predictive controller is designed to estimate optimal insulin infusion based on current state, predicted insulin sensitivity and planned nutrition. The developed control algorithm allows individualized blood glucose control with reduced glycemic variability and reduced risk of hypoglycemia in the intensive care unit.

Neuroadaptive Fault-Tolerant Control Embedded With Diversified Activating Functions With Application to Auto-Driving Vehicles Under Fading Actuation.

This article presents a neuroadaptive fault-tolerant control method for path tracking of multiinput multioutput (MIMO) systems in the presence of modeling uncertainties and external disturbances. In dealing with modeling uncertainties, neural networks (NNs) with diversified activation/basis functions are considered, with which we establish a set of control algorithms that are robust against uncertainties, adaptive to unknown parameters, and tolerant to actuation faults. This is the first work that explicitly takes into account the neural weights uncertainties and activating function uncertainties in multiple layered neural networks in control design. In addition, we apply the developed control algorithms to unmanned ground vehicles (UGVs) with actuator failures. With the aid of Lyapunov stability theory, it is shown that the proposed control is able to drive the vehicle along the desired path with high precision and all the internal signals are uniformly ultimately bounded (UUB) and continuous. Both theoretical analysis and numerical simulation confirm the effectiveness of the designed strategy.

Determining the parameters of the functioning for a nonlinear ballistic system in a real external environment

Determining the parameters of the functioning for a nonlinear ballistic system in a real external environment

Parameter adaptive based neural network sliding mode control for electro‐hydraulic system with application to rock drilling jumbo

SummaryRock drilling jumbo is an important large construction machine used for tunneling construction, and its automation has an urgent demand in engineering. However, the electro‐hydraulic system of the rock drilling jumbo has strong parameters uncertainties and some dynamics that are hard to model accurately, which causes certain challenges for designing model‐based high‐performance control algorithms. To solve these challenges, a parameter adaptive based neural network sliding mode control algorithm is proposed to enhance control performance of the electro‐hydraulic system. The parameter adaptive law is developed to estimate unknown parameters of the system, the neural network is applied for compensating unmodeled dynamics, and then the final control law is designed by sliding mode control method, and the stability demonstration of the closed‐loop system is given. In the simulations, the effectiveness of the designed parameter adaptive law is verified. Extensive comparison experiments are performed on a real rock drilling jumbo driven by proportional valves, the experimental results demonstrate that the developed control algorithm obviously improves the control precision of hydraulic cylinder of the rock drilling jumbo compared with the traditional sliding mode and PID control algorithm, thus the designed control algorithm can be expanded and applied for general hydraulic servo control mechanism.

Overview of advances in ASDEX Upgrade plasma control to support critical physics research for ITER and beyond

The successful operation of fusion reactors requires plasma scenarios with good core confinement and acceptable first wall heat loads that are stable and robust to external perturbations. This poses both physical and technological challenges. One of the technologies that addresses these challenges is a complex feedback control system that supports advances in physical understanding and helps to ensure stable operating conditions. The operation of marginally stable plasmas often leads to off-normal events (such as disruptions) and feedback control can prevent these to some extent. This contribution gives an overview of the main results of the development and operation of the feedback control algorithms on ASDEX Upgrade (AUG). Fueling actuators, using a combination of gas valves and pellet injection, can simultaneously control neutral density of the divertor and the density of the plasma core above the Greenwald limit. Impurity injection is employed to control the position of the X-point radiator, allowing the creation of an ELM-suppressed H-mode with high radiation fraction. Heating actuators are used to control the plasma energy content, which supports advanced tokamak experiments and enables stable I-mode operation, and the electron temperature control, which supports turbulence studies. In control technology, AUG has pioneered the use of virtual actuators, which allow effective use of the limited number of heating actuators, adaptive control policies, and exception handling. Such technologies will also be used in ITER. Advanced nonlinear state observers (RAPTOR, RAPDENS) and codes to evaluate the power deposition properties (RABBIT, TORBEAM) are available for routine use in the AUG feedback controllers. Extensive use of the AUG discharge control system further enhances the research capabilities of this machine.

Fuzzy Modeling Framework Using Sector Non-Linearity Techniques for Fixed-Wing Aircrafts

This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed-wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprising fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed-wing aircraft systems. Here, the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity red techniques to recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models, like parallel and non-parallel distributed compensation control laws using linear matrix inequality optimization, enables the development of control algorithms that guarantee stability conditions for a wide range of operations points, avoiding the classical gain scheduling schemes, where the stability issues can be extremely challenging.

Model Development for Off-Road Traction Control: A Linear Parameter-Varying Approach

The number of highly automated machines in the agricultural sector has increased rapidly in recent years. To reduce their fuel consumption, and thus their emission and operational cost, the performance of such machines must be optimized. The running gear–terrain interaction heavily affects the behavior of the vehicle; therefore, off-road traction control algorithms must effectively handle this nonlinear phenomenon. This paper proposes a linear parameter-varying model that retains the generality of semiempirical models while supporting the development of real-time state observers and control algorithms. First, the model is derived from the Bekker–Wong model for the theoretical case of a single wheel; then, it is generalized to describe the behavior of vehicles with an arbitrary number of wheels. The proposed model is validated using an open-source multiphysics simulation engine and experimental measurements. According to the validated results, it performs satisfactorily overall in terms of model complexity, calculation cost, and accuracy, confirming its applicability.

Impact study on continuous overcharging of precycled lithium batteries and control algorithm development using machine learning approach

ABSTRACT This research aims to understand the impact of overcharging on precycled Lithium-Nickel Manganese Cobalt (Li-NMC) batteries through continuous charge and discharge cycles. These Li-NMC cells underwent standard charging and discharging, maintaining their state of health (SOH) above 90% even after 200 cycles. Continuous overcharging of these precycled Li-NMC cells caused their SOH to degrade below 20% within the next 180 cycles. To investigate the underlying reasons for such significant differences in SOH degradation, scanning electron microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) analyses were performed on both pristine and overcharged batteries. The structural changes in the batteries due to overcharging provide valuable insights into the degradation mechanisms. In addition, to predict the health degradation of precycled batteries by utilizing a gradient boost (GB) and support vector machine (SVM) regressor with hyperparameters optimized through Bayesian optimization, tuning of these hyperparameters increases accuracy, where mean squared error (MSE) and root mean squared error (RMSE) decrease more than 65% of the conventional algorithm. The GB and SVM regressors were trained using data acquired during the charge and discharge cycles of the batteries. The trained model demonstrated promising performance in predicting SOH degradation, providing valuable guidance for developing control algorithms for battery management strategies.

Development of PLC-Based Hardware Test-Bench Prototype for Solar-Wind-Battery-Based Microgrid System’s Control Algorithm Validation

Programmable Logic Controllers (PLCs) are gaining traction in microgrid operation due to their real-time deployment, reliable and robust operation, and fast response, even in harsh environments. Recognizing the critical need for robust and reconfigurable microgrid control and operation, this research proposes a hardware test-bench prototype using the Reconfigurable Allen-Bradley—Micro820/2080-LC20-20QBB PLC to demonstrate control algorithms aiming for efficient control, energy management, and resilient microgrid automation. The design and construction of a prototype controller test bench, along with the PLC configuration, wiring diagrams, and control logic, constitute the significant contributions of this research. The proposed controller test bench’s performance is evaluated through similar condition simulations in the presence of disturbances induced by the reduced contributions of renewable energy sources, such as solar and wind. The results illustrate the effectiveness of the developed control algorithm implemented in the proposed controller, ensuring the stable operation of the microgrid. Consequently, this work advances the simulation-based validation of microgrid control algorithms and provides insights into the practical application of a PLC-based hardware test bench under various environmental conditions.

End-to-end deep reinforcement learning for control of an autonomous underwater robot with an undulating propulsor

This paper focuses on the development and implementation of control algorithms for positioning an Autonomous Underwater Vehicle (AUV) with an undulating propulsor, using reinforcement learning methods. It provides an analysis and overview of works incorporating reinforcement learning methods such as Actor-only, Critic-only, and Actor-Critic. The paper primarily focuses on the Deep Deterministic Policy Gradient method and its implementation using deep neural networks to train the Actor-Critic agent. In the agent's architecture, a replay buffer and target neural networks were utilized to address the data correlation issue that induces training instability. An adaptive ar-chitecture was proposed for training the agent to force the robot to move from the initial point to any target point. Additionally, a random target point generator was incorporated at the training stage so as not to retrain the agent when the target points change. The training objective is to optimize the actor's policy by optimizing the critic and maximizing the reward function. Reward function is determined as the distance from the robot's center of mass to the target points. Consequently, the reward received by the agent increases when the robot gets closer to the target point and becomes maximal when the target point is reached with an acceptable error.

Development of control algorithm for autonomous intelligent robotic complex (AIRTC)

The work is dedicated to the development of a control algorithm for the autonomous intelligent robotic complex (AIRTC). The unmanned ground vehicle consists of a corpus that contains batteries, motor wheels, sensors, video cameras, on-board computer for processing data from sensors and video cameras. The general dimensions of the autonomous mobile robotic platform are 780x650x550 mm. The small size of the mobile platform allows it to be used in small areas, where the use of other vehicles is economically unprofitable. The key role in the autonomous mode of operation of the robotic complex is intended to the use of machine learning technologies to realize the possibility of performing the task assigned to the autonomous robotic platform in indoor and outdoor areas. An algorithm has been developed to control the basic platform of an autonomous robotic technical complex and to detect key obstacles automatically using a neural network. The accuracy of detecting the eight designated classes of obstacles was 85%.

The Game Theory Application in the Transport Flows Control Algorithm Development in the Intelligent Transport System

Traffic management in modern intelligent transport systems includes monitoring the actual traffic situation in real time (volumes, speeds, incidents, etc.) and management or controlling transport traffic using this information. At the same time, cars as intelligent transport systems components must be equipped with communication capabilities for exchanging information with other vehicles (V2V) and with road infrastructure (V2I). All this is connected with the presence of special equipment connected to the on-board network for local data collection, which can be exchanged between cars and with a central communication station using wireless Internet. At the same time, the issue of developing the traffic organization algorithms themselves is still open. This problem can be solved with the help of game theory, a fairly new but rapidly developing part of modern mathematics. Unlike optimization theory, which studies the possibilities of constructing an optimal solution for the entire system as a whole, game theory studies ways to optimize individual benefits in competition with other persons (events) that rationally seek to satisfy their own benefits. The problem of “smart” regulation of intersections is quite difficult to solve, it combines the efforts of scientists from different fields of knowledge. However, the rapid development of ICT technologies and their rapid application to transport tasks makes it possible to develop some approaches to optimize the current situation at the intersection.

A Study on the Thermal Behavior of Series and Parallel Connection Methods in the Process of Hydrogenation of Ship-Borne Hydrogen Storage Cylinder

As a subdivision of the hydrogen energy application field, ship-borne hydrogen fuel cell systems have certain differences from vehicle or other application scenarios in terms of their structural type, safety, environmental adaptability, and test verification. The connection method of the ship-borne hydrogen storage cylinder (SHSC) is very important for the hydrogen fuel cell ship, and the structural parameters of the SHSC are particularly important in the hydrogen refueling process. To ensure the safe and reliable operation of the hydrogen-powered ship, research on the filling of the SHSC under different connection modes was carried out during refueling. In our study, a thermal flow physical model of the SHSC was established to research the hydrogen refueling process of the series and parallel SHSCs. The influence of series and parallel modes of the SHSCs on the hydrogen refueling process was explored, and the evolution law of the internal flow field, pressure, and temperature of series and parallel SHSCs under different filling parameters was analyzed by numerical simulation. Our results confirmed the superiority of the parallel modular approach in terms of thermal safety during refueling. The results can supply a technical basis for the future development of hydrogen refueling stations and ship-board hydrogenation control algorithms.

A Review of Mechanisms to Vary the Stiffness of Laminar Jamming Structures and Their Applications in Robotics

Laminar jamming (LJ) is a method to achieve variable stiffness in robotics that has attracted notable attention because of its simple working principle and potential high stiffness variation. This article reviews the lock/unlock mechanisms of LJ structures. The application of these mechanisms in robotics is discussed, including grippers, continuum robots, wearable robots, robot arms, and more. Furthermore, the performance and limitations of the mechanisms to vary the stiffness of LJ are qualitatively and quantitatively analyzed. This performance analysis focuses mainly on the potential of LJ mechanisms to be applied in robot arms with variable stiffness and their potential to attenuate the impact between human beings and robot arms. The modeling of LJ through analytical and finite element methods is described, and their evolution towards design methodologies is discussed. To conclude, the directions and recommendations that should be followed in research on LJ are discussed. These include the improvement of existing lock/unlock mechanisms, the development of new lock/unlock mechanisms, and the development of more control algorithms for robot arms that incorporate LJ structures.

Design and development of intelligent control strategies and algorithms for automated control of biotechnical objects under uncertainty

Uncertainty of environmental conditions is typical for most biological and agricultural systems. This uncertainty has recently become a serious challenge because of global climate changes. Due to delayed response to unpredictable environmental changes, the functioning of these systems is not always optimal. Another source of uncertainty is the adaptive response of a biological system, which usually does not coincide with the chosen objective function. This study focuses on developing intelligent control strategies to improve the performance of biotechnical systems under uncertainty. We use cause–effect diagrams to analyze the effect of uncertainties on the key performance indicators (KPIs). Our concept of intelligent control is based on the statistical evaluation of environmental uncertainties, while the decision-making is based on game theory. This intelligent control concept is universal and could be applied to different classes of biotechnical systems, from small animal farms to large industrial plants. Sugar production is considered as one of the objects of research, which requires minimizing sugar losses in four main unit operations: diffusion, purification, evaporation and crystallization. Each unit operation was modeled with the artificial neural networks (ANN) trained on the laboratory datasets. Based on simulated or real inputs from the technological process, ANNs predict outputs in terms of quality, losses, and energy efficiency. In stationary mode, ANNs are used to predict optimal technological regimes. They are used to correct technological regimes to maximize the objective function in real time. The application of intelligent algorithms improved process performance and energy efficiency.

Asymptotic Tracking Control for Mismatched Uncertain Systems with Active Disturbance Rejection

By introducing a set of exact disturbance estimators, a continuously tracking controller for a class of mismatched uncertain systems with exogenous disturbances will be proposed. The most appealing superiority is that the proposed exact disturbance estimators can not only estimate the external disturbances but also achieve an asymptotic estimation performance. Furthermore, with the help of a set of first-order asymptotic filters and an auxiliary system, the developed control algorithm is able to compensate for these total disturbances feedforwardly. Consequently, the whole closed-loop stability with an asymptotic tracking performance is strictly analyzed, and meanwhile applications are conducted to indicate the effectiveness of the proposed controller.

Development and performance evaluation of intelligent algorithm for optimal control of a hybrid heat pump system during the cooling season

The purpose of this study is to develop a control method for a hybrid heat pump system based on an artificial neural network (ANN) to reduce energy use and create a more comfortable thermal environment. The proposed optimal control method uses an ANN-based predictive model to predict the heat storage tank and indoor temperature during the cooling period and controls the flow rate of the circulation pump on the heat source and load side of the system. The performance of the predictive model for the heat storage tank temperature (R2 = 0.9988; coefficient of variation of the root mean square error [CV(RMSE)] = 1.06 %; normalized mean bias error [NMBE] = 0.16 %; and mean absolute error [MAE] = 0.09℃) and the indoor temperature (R2 = 0.9893; CV(RMSE) = 1.66 %; NMBE = 0.16 %; and MAE = 0.15℃) was excellent. The temperature control of the heat storage tank using the optimal algorithm exhibited an improvement of 18.39 % for the CV(RMSE), 3.10 % for the NMBE, and 1.31 °C for MAE compared with rule-based. For the indoor temperature, the optimal algorithm improved the CV(RMSE) by 1.30 %, NMBE by 0.42 %, and MAE by 0.29℃ compared to rule-based. The energy use was reduced by 52.85 % for the entire system using the optimal control method compared with the existing control strategy under similar outdoor conditions. Using the proposed control method, it is thus possible to improve thermal comfort and reduce carbon emissions in the building sector by improving the control and energy performance of hybrid heat pump systems.