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Autonomous control systems can enhance the economic viability of complex systems such as nuclear microreactors. Advanced forms of these control systems benefit from techniques for high-resolution field data reconstruction from sensor data. Basis projection methods present a useful set of methodologies for nonparametric reconstruction of these field data. In this work, high fidelity multiphysics simulations are used to generate microreactor temperature distributions that are then used to evaluate the viability of a basis projection method which employs a generalized set of basis functions, or dictionaries. Additionally, metrics for evaluating the accuracy of the temperature distribution are presented that are motivated both from the fields of traditional signal processing and statistics. The performance of a method based on matching pursuits is compared to a method that uses the sequential ordering of basis functions. The matching pursuits-based method gives improved performance for highly sparse representations of temperature distributions. Additionally, accuracy is analyzed for multiple temperature distributions which represent different potential reactor operation configurations. Overall, the results from this work should be considered as an upper bound on the performance of basis projection methods which employ generalized basis functions for real time reactor control. • Autonomous systems enhance nuclear microreactor economic viability. • Basis projection methods aid in nonparametric field data reconstruction. • Matching pursuits-based method outperforms sequential basis function method. • Theoretical foundation provided for evaluating alternative bases. • Generalized bases evaluated for nuclear reactor field reconstruction. [ABSTRACT FROM AUTHOR]

The rapid and accurate control of air chamber pressure in slurry pressure balance (SPB) shield tunneling machines is crucial for establishing the balance between slurry pressure and soil and water pressure, ensuring the stability of the support face. A novel air chamber pressure control method based on nonlinear adaptive robust control (ARC) and using a pneumatic proportional three-way pressure-reducing valve is proposed in this paper. Firstly, an electric proportional control system for the air chamber pressure is developed. Secondly, a nonlinear state space model for the air chamber pressure regulation process is established. Utilizing experimental data from the SPB shield tunneling machine test bench, nonlinear adaptive identification is conducted through the nonlinear recursive least square algorithm. The results demonstrate the model's effectiveness and accuracy. Then, a nonlinear ARC for air chamber pressure is designed based on the backstepping method, and its Lyapunov stability is proved. Finally, the feasibility and effectiveness of the controller designed in this paper is verified through simulation and experiments. The results demonstrate that the developed control system can compensate for the nonlinearity and disturbance in the air chamber pressure regulation process. It can achieve good transient and steady-state performance and has good robustness against uncertainty. [ABSTRACT FROM AUTHOR]

With the amount of garbage on water surface ever increasing, traditional cleaning methods are no longer able to meet the requirements for safety, efficiency, as well as intelligence in applications. Hence, water surface garbage cleaning robots have emerged and become the focus of development in this field. After more than 20 years of development, it has entered the stage of practical application from research with various structure types designed and the realization of fully autonomous operations from garbage detection to collection. A large amount of literature has been accumulated, but there is still no comprehensive review. Therefore, this paper aims to provide an extensive review of development of water surface garbage cleaning robots in literature. The structures of the water surface garbage cleaning robots are classified with the characteristics of each type summarized. The typical framework of an autonomous water surface garbage cleaning robot is analyzed, and its three key technologies of perception, decision-making, and control are highlighted and discussed. Finally, the paper explores the trends of development for technologies in water surface garbage cleaning robots, which are centered on the challenges presented by current applications, to provide a reference for further research and application of water surface garbage cleaning robots. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, we have analyzed the input/output (I/O) activities of Cori, which is a high-performance computing system at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. Our analysis results indicate that most users do not adjust storage configurations but rather use the default settings. In addition, owing to the interference from many applications running simultaneously, the performance varies based on the system status. To configure file systems autonomously in complex environments, we developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm that utilizes the system log information to adjust storage configurations automatically. Our scheme aims to improve the application performance and avoid interference from other applications without user intervention. Moreover, DCA-IO uses the existing system logs and does not require code modifications, an additional library, or user intervention. To demonstrate the effectiveness of DCA-IO, we performed experiments using I/O kernels of real applications in both an isolated small-sized Lustre environment and Cori. Our experimental results shows that our scheme can improve the performance of HPC applications by up to 263% with the default Lustre configuration.

Synthetic transcription factors (synTFs) that are inducible by exogenous or autonomous inputs are useful in cell and gene therapy for temporal and dosage control of the expression of beneficial transgenes. Recent progress in engineering DNA-binding domains (DBDs) and transcriptional activation domains (TADs) composed primarily of human sequences reduces concerns about the immunogenicity of synTF components. Control modules for synTFs have been developed that prioritize the use of clinically approved small-molecule drugs. Novel autonomous control strategies enable the programming of cell and gene therapies that can sense and respond to conditions in their environment or intracellular signaling state. Human-based components and controllers responsive to diverse inputs are clearing the way for the adoption of synTF systems in the clinic for cancer immunotherapy and gene therapy of metabolic or genetic diseases. Synthetic transcription factors (synTFs) that control beneficial transgene expression are an important method to increase the safety and efficacy of cell and gene therapy. Reliance on synTF components from non-human sources has slowed progress in the field because of concerns about immunogenicity and inducer drug properties. Recent advances in human-derived DNA-binding domains (DBDs) and transcriptional activation domains (TADs) paired with novel control modules responsive to clinically approved small molecules have poised the synTF field to overcome these hurdles. Advances include controllers inducible by autonomous signaling inputs and more complex, multi-input synTF circuits. Demonstrations of advanced control strategies with human-derived transcription factor components in clinically relevant vectors and in vivo models will facilitate progression into the clinic. [ABSTRACT FROM AUTHOR]

Self-organization is a key strategy for improving the performance of an aerial swarm ad hoc network. The proliferation of low-cost VTOL drones has broadened the application domain of aerial swarms, and the need for synchronized communication among network entities has become crucial. However, existing ad hoc approaches struggle to maintain multi-hop connections in contested environments characterized by frequent topology changes and intermittent links. To overcome these limitations, we introduced STDMA protocol, which enables the self-configuration of drones without reliance on a ground controller. In continuation of the earlier work, we conduct extensive experiments to evaluate the performance of the proposed protocol. Comparative simulation experiments cover various scenarios with different network sizes, frame lengths, and traffic loads. The STDMA protocol achieves optimal access delay in highly contested environments and reduces delay by approximately 19.181%. Moreover, it exhibits improved channel utilization compared to the E-ASAP/SM protocol, with a 4.5 times increase.

Active power sharing and voltage regulation are two of the major control challenges in the operation of the voltage source converter based multi-terminal high-voltage DC (VSC-MTDC) system when integrating large-scale offshore wind farms (OWFs). This paper proposes two novel adaptive voltage reference based droop control methods to regulate pilot DC voltage and share the power burden autonomously. The proposed Method I utilizes DC grid lossy model with the local voltage droop control strategy, while the proposed Method II adopts a modified pilot voltage droop control (MPVDC) to avoid the large errors caused by the DC grid lossless model. Dynamic simulations of a five-terminal MTDC grid are carried out using MATLAB/Simulink SimPowerSystems /Specialized Technology to verify the proposed autonomous control methods under various types of disturbance and contingency. In addition, comparative study is implemented to demonstrate the advantages of the proposed methods.

A small modular autonomous underwater vehicle (AUV) offers several benefits including enhanced mobility, cost-effectiveness, compact and portable structure, and small size. This paper proposes a comprehensive design and implementation approach for a small modular AUV, named as ARMs1.0, utilizing cutting-edge 3D printing technology. The main cabin shell of the AUV features a modular design and is manufactured using 3D printing technology. The control module and sensing equipment are installed in a sealed compartment. To achieve forward, pitching, and yawing motions, the AUV is equipped with ducted propeller and four independent rudders. The modular approach in AUV design has been implemented, considering both the main cabin shell as well as the subsections and segments of the AUV. Additionally, a centralized control system architecture design is developed based on the specific tasks of the AUV. The composition and functions of key units are described in detail, and an autonomous depth-tracking control strategy is formulated. Based on the experimental results for AUV motion in horizontal and vertical planes, including autonomous depth tracking tests, the ARMs1.0 AUV demonstrates the capability to successfully perform required maneuvering tasks. The designed small modular AUV has achieved accurate depth tracking, precise heading following and exhibits excellent maneuverability. [ABSTRACT FROM AUTHOR]

Introduction: Our work introduces a real-time robotic localization and mapping system for buried pipe networks. Methods: The system integrates non-vision-based exploration and navigation with an active-vision-based localization and topological mapping algorithm. This algorithm is selectively activated at topologically key locations, such as junctions. Non-vision-based sensors are employed to detect junctions, minimizing the use of visual data and limiting the number of images taken within junctions. Results: The primary aim is to provide an accurate and efficient mapping of the pipe network while ensuring real-time performance and reduced computational requirements. Discussion: Simulation results featuring robots with fully autonomous control in a virtual pipe network environment are presented. These simulations effectively demonstrate the feasibility of our approach in principle, offering a practical solution for mapping and localization in buried pipes. [ABSTRACT FROM AUTHOR]

The dynamic window approach (DWA) serves as a pivotal collision avoidance strategy for mobile robots, meticulously guiding a robot to its target while ensuring a safe distance from any perceivable obstacles in the vicinity. While the DWA has seen various enhancements and applications, its foundational computational process has predominantly remained constant, consequently resulting in a heightened level of time complexity. Inspired by the velocity invariance assumption inherent in the DWA and the utilization of polar coordinate transformations in the model, we introduce a high-speed version of the DWA. [ABSTRACT FROM AUTHOR]

The rapid and accurate control of air chamber pressure in slurry pressure balance (SPB) shield tunneling machines is crucial for establishing the balance between slurry pressure and soil and water pressure, ensuring the stability of the support face. A novel air chamber pressure control method based on nonlinear adaptive robust control (ARC) and using a pneumatic proportional three-way pressure-reducing valve is proposed in this paper. Firstly, an electric proportional control system for the air chamber pressure is developed. Secondly, a nonlinear state space model for the air chamber pressure regulation process is established. Utilizing experimental data from the SPB shield tunneling machine test bench, nonlinear adaptive identification is conducted through the nonlinear recursive least square algorithm. The results demonstrate the model’s effectiveness and accuracy. Then, a nonlinear ARC for air chamber pressure is designed based on the backstepping method, and its Lyapunov stability is proved. Finally, the feasibility and effectiveness of the controller designed in this paper is verified through simulation and experiments. The results demonstrate that the developed control system can compensate for the nonlinearity and disturbance in the air chamber pressure regulation process. It can achieve good transient and steady-state performance and has good robustness against uncertainty.

A new algorithm for making autonomous decisions when controlling spacecraft carrying out descent in the atmosphere is developed, which allows to carry out stable control of the spacecraft relative to the nominal flight trajectories, which provide to reliably fulfill the targets of space missions. Analytical dependences are formed, with the help of which it is possible to obtain high-precision calculations of the parameters of the movement of a spacecraft in the atmosphere and determine corrective programs for controlling the apparatus. This makes it feasible to implement the movement of a spacecraft in the atmosphere along trajectories close to optimal, even under conditions of significant influence of disturbing factors on the dynamics of the flight of the vehicle. The authors give an estimate of the performance of the algorithm for making autonomous decisions on the example of parrying disturbing influences during the descent of a spacecraft in the atmospheres of Mars and Jupiter. It is shown that with complete qualitative agreement between the data calculated using the analytical dependences and the results of numerical integration, the computational errors do not exceed 3%. With the most unfavorable combinations of navigation errors and atmospheric density variations, the development of the corrective control programs developed in most cases ensures a qualitative coincidence of the disturbed and nominal trajectories. The developed algorithm for making autonomous decisions based on analytical dependencies can be effectively applied when a spacecraft moves in planetary atmospheres under various boundary conditions, constraints, design characteristics of the spacecraft and atmosphere models.

Unmanned aerial vehicle (UAV), which is one of the significant means of maritime and aviation reconnaissance, is recognized as the main reconnaissance force to obtain information control and air control in the future sea battlefield. By combing the models and attribute characteristics of foreign small and medium-sized UAVs on the sea, this paper studies the application mode and application advantages of small and medium-sized ISR UAVs in the sea battlefield, and discusses their problems faced by the application, then analyzes the key technologies, such as launch and recovery, autonomous control, mission load, communication link, platform design, finally looks forward to their development trend. This paper provides reference and ideas for the popularization, application and planning development of small and medium-sized ISR UAVs in the field of maritime.

As part of the main trends in the development of world space activities - expanding the composition of near-Earth orbital constellations of spacecraft, intensifying the study of planets and bodies of the solar system, increasing the requirements for the quality and reliability of space expeditions - the problem of developing and improving the methodology of optimal control, system analysis, decision support to design highly efficient spacecraft control systems is brought to the fore. These studies include the formation of methodological approaches to the study of the optimal control of spacecraft during their movement in the atmospheres of planets, the autonomous control of a spacecraft under conditions of uncertain flight situations, etc. The problematic issues of designing deep space expeditions include the organization of effective control of the spacecraft with its considerable distance from ground stations. At the same time, an uncontested condition for the successful implementation of flight programs is the development and application of autonomous spacecraft control systems based on the use of highly efficient technologies for collecting and processing measurement information. This determines the need to improve the methods and algorithms of autonomous decision-making on the spacecraft control. The authors develop a new methodological approach to the structural construction of autonomous spacecraft control systems based on the created technologies for identifying flight situations: processing measurements; forming logical decision rules; forecasting flight trajectories and on-board equipment operability. A formal statement of the problem of autonomous decision-making on spacecraft control is provided.

AbstractQuadcopters are in enormous demand in mission-critical applications where there is a large risk to human life, such as border patrolling, emergency rescue, and production monitoring in chemical industries. Autonomously flying quadcopters can ensure higher levels of safety than remotely controlled quadcopters. Due to the complexity of autonomous quadcopters, there is a risk of failure, posing an enormous challenge to successful mission completion. Additionally, due to space constraints and the need for extended operation, the controller area and propulsion power are to be lower along with higher speeds of tracking for reaching the destination as early as possible. With these conflicting requirements, these metrics can be optimized in stages by reconfiguring the controllers on FPGA-based systems. Different controllers and suitable references are chosen and brought into action based on the metric that is lagging once they are found suitable to operate safely. This combination of control and reference reconfiguration and switching of main/auxiliary controllers is expected to ensure speed, area, and power optimization with improved fault resilience, ensuring better mission completion possibilities. This has been verified with Simulink-based algorithms of both continuous systems and fixed point-based digital systems and FPGA-based system co-simulation with the Simulink-based quadcopter model. Finally, the synthesis and implementation of the FPGA-based system is also taken up on Zynq Ultrascale±based devices with Vivado v2018.3-based IDE.

Introduction: Our work introduces a real-time robotic localization and mapping system for buried pipe networks.Methods: The system integrates non-vision-based exploration and navigation with an active-vision-based localization and topological mapping algorithm. This algorithm is selectively activated at topologically key locations, such as junctions. Non-vision-based sensors are employed to detect junctions, minimizing the use of visual data and limiting the number of images taken within junctions.Results: The primary aim is to provide an accurate and efficient mapping of the pipe network while ensuring real-time performance and reduced computational requirements.Discussion: Simulation results featuring robots with fully autonomous control in a virtual pipe network environment are presented. These simulations effectively demonstrate the feasibility of our approach in principle, offering a practical solution for mapping and localization in buried pipes.

Summary: Deep reinforcement learning has been recently a very active field of research. The policies generated with the use of this class of training algorithms are flexible and thus have many practical applications. In this article we present the results of our attempt to use the recent advancements in reinforcement learning to automate the management of resources in a compute cloud environment. We describe a new approach to self‐adaptation of autonomous management, which uses a digital clone of the managed infrastructure to continuously update the control policy. We present the architecture of our system and discuss the results of evaluation which includes autonomous management of a sample application deployed to Amazon Web Services cloud. We also provide the details of the training of the management policy using the Proximal Policy Optimization algorithm. Finally, we discuss the feasibility to extend the presented approach to further scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to transform the results of qualitative risk analysis into a numeric optimal control problem for autonomous ship navigation. Today, making autonomous high-level decisions replacing a crew onboard is considered difficult, in some part due to the complexity of managing the operational risks involved. Although human supervisors, e.g., located in remote operating control centers are still needed for safety and liability reasons, there is a growing demand for complex decisions to be made by the onboard control system itself, both during normal operations and in emergencies. This paper suggests general principles for how the results from systems-theoretic process analysis (STPA) can be transformed into a quantitative and computationally tractable optimization problem, solved by a MPC-based decision-making algorithm for autonomous navigation. The proposed method is demonstrated and evaluated by simulating an autonomous ship navigating in a coastal environment. It is concluded that the proposed method may serve as a reasonable and valuable bridge between the realms of qualitative risk analysis and numerical optimal control for risk-aware autonomous control and decision-making. [ABSTRACT FROM AUTHOR]

Quadcopters are in enormous demand in mission-critical applications where there is a large risk to human life, such as border patrolling, emergency rescue, and production monitoring in chemical industries. Autonomously flying quadcopters can ensure higher levels of safety than remotely controlled quadcopters. Due to the complexity of autonomous quadcopters, there is a risk of failure, posing an enormous challenge to successful mission completion. Additionally, due to space constraints and the need for extended operation, the controller area and propulsion power are to be lower along with higher speeds of tracking for reaching the destination as early as possible. With these conflicting requirements, these metrics can be optimized in stages by reconfiguring the controllers on FPGA-based systems. Different controllers and suitable references are chosen and brought into action based on the metric that is lagging once they are found suitable to operate safely. This combination of control and reference reconfiguration and switching of main/auxiliary controllers is expected to ensure speed, area, and power optimization with improved fault resilience, ensuring better mission completion possibilities. This has been verified with Simulink-based algorithms of both continuous systems and fixed point-based digital systems and FPGA-based system co-simulation with the Simulink-based quadcopter model. Finally, the synthesis and implementation of the FPGA-based system is also taken up on Zynq Ultrascale±based devices with Vivado v2018.3-based IDE. [ABSTRACT FROM AUTHOR]

In this paper, a control strategy for a combination PV-BESS-SC hybrid system in islanded microgrid with a DC load is designed and analyzed using a new topology. Although Battery Energy Storage System (BESS) is employed to keep the DC bus voltage stable; however, it has a high energy density and a low power density. On the other hand, the Supercapacitor (SC) has a low energy density but a high-power density. As a result, combining a BESS and an SC is more efficient for power density and high energy. Integrating the many sources is more complicated. In order to integrate the SC and BESS and deliver continuous power to the load, a control strategy is required. A novel method for controlling the bus voltage and energy management will be proposed in this paper. The main advantage of the proposed system is that throughout the operation, the State of Charging (SOC), BESS current, and SC voltage and current are all kept within predetermined ranges. Additionally, SC balances fastchanging power surges, while BESS balances slow-changing power surges. Therefore, it enhances the life span and minimizes the current strains on BESS. To track the Maximum Power Point (MPP) or restrict power from the PV panel to the load, a unidirectional boost converter is utilized. Two buck converters coupled in parallel with a boost converter are proposed to charge the hybrid BESS-SC. Another two boost converters are used to manage the discharge operation of the BESS-SC storage in order to reduce losses. The simulation results show that the proposed control technique for rapid changes in load demand and PV generation is effective. In addition, the proposed technique control strategy is compared with a traditional control strategy. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper describes the design, implementation, and field‐testing of a low‐cost autonomous control system on the Platform for Expanding Autonomous Underwater Vehicles (AUV) exploRation to Longer ranges (PEARL). PEARL is a floating, solar‐powered platform designed as a servicing station for AUVs. This paper describes the field testing of a prototype system currently without the ability to dock and recharge AUVs. In this prototype, autonomous operations are realized through the open‐source MOOS‐IvP marine robotics software running on an embedded architecture including a Raspberry Pi microcomputer, Arduino microcontroller, and onboard inertial measurement unit and GPS sensors. A solar charge controller monitors energy system statistics in real‐time. Field tests included station‐keeping and waypoint missions in a freshwater pond in Orleans, MA. An RMS tracking error of 0.9–2.2 m was achieved during waypoint pattern executions, with an average battery usage of 13% per hour. Net power consumption rate will change due to variations in currents and winds, as well as variations in solar irradiance. The implementation of an autonomous sun‐tracking mode to maximize solar panel flux while station‐keeping is also evaluated. The field testing results will inform the design of a full‐scale PEARL capable of docking with AUVs. This research helps pave the way for autonomous solar‐powered service stations in the ocean that can connect the underwater world with space. [ABSTRACT FROM AUTHOR]

Individuals who use myoelectric upper-limb prostheses often rely heavily on vision to complete their daily activities. They thus struggle in situations where vision is overloaded, such as multitasking, or unavailable, such as poor lighting conditions. Non-disabled individuals can easily accomplish such tasks due to tactile reflexes and haptic sensation guiding their upper-limb motor coordination. Based on these principles, we developed and tested two novel prosthesis systems that incorporate autonomous controllers and provide the user with touch-location feedback through either vibration or distributed pressure. These capabilities were made possible by installing a custom contact-location sensor on the fingers of a commercial prosthetic hand, along with a custom pressure sensor on the thumb. We compared the performance of the two systems against a standard myoelectric prosthesis and a myoelectric prosthesis with only autonomous controllers in a difficult reach-to-pick-and-place task conducted without direct vision. Results from 40 non-disabled participants in this between-subjects study indicated that vibrotactile feedback combined with synthetic reflexes proved significantly more advantageous than the standard prosthesis in several of the task milestones. In addition, vibrotactile feedback and synthetic reflexes improved grasp placement compared to only synthetic reflexes or pressure feedback combined with synthetic reflexes. These results indicate that autonomous controllers and haptic feedback together facilitate success in dexterous tasks without vision, and that the type of haptic display matters.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Drive is the property of Electric Drive Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The autonomous grasping operation is the key to the intelligent logistics sorting manipulators. Since the current logistics sorting manipulators mostly use visual sensors to identify objects, they are extremely vulnerable to the changes in the lighting environment. Therefore, the research considers the influence of complex lighting environment on the autonomous grasping of logistics sorting manipulators. Furthermore, it verifies the effectiveness of SAC-AE-ICM algorithm through simulations and experiments. The experimental results show that SAC-AE-ICM algorithm can quickly achieve convergence in many experiments and can obtain global optimization. In the process of automatic capture, the success rate of SAC-AE-ICM algorithm can reach 90%, which is 25% higher than that of the method without ICM and has better convergence. The success rate was as high as 77% for unknown or irregular targets. In practical experiments, SAC-AE-ICM can effectively capture under good lighting conditions. However, under low light conditions, the probability of capturing unknown targets is about 72.5%. Overall, the success rates for single-object and multi-object capture are 88% and 85%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)