Autonomous Control System Research Articles

Simplified Maneuvering Strategies for Rendezvous in Near-Circular Earth Orbits

The development of autonomous guidance control and navigation systems for spacecraft would greatly benefit applications such as debris removals or on-orbit servicing, where human intervention is not practical. Within this context, inspired by Autonomous Vision Approach Navigation and Target Identification (AVANTI) demonstration, this work presents new guidance algorithms for rendezvous and proximity operations missions. Analytical laws are adopted and preferred over numerical methods, and mean relative orbital elements are chosen as state variables. Application times, magnitudes and directions of impulsive controls are sought to minimize propellant consumption for the planar reconfiguration of the relative motion between a passive target spacecraft and an active chaser one. In addition, simple and effective algorithms to evaluate the benefit of combining in-plane and out-of-plane maneuvers are introduced to deal with 3D problems. The proposed new strategies focus on maneuvers with a dominant change in the relative mean longitude (rarely addressed in the literature), but they can also deal with transfers where other relative orbital elements exhibit the most significant variations. A comprehensive parametric analysis compares the proposed new strategies with those employed in AVANTI and with the global optimum, numerically found for each test case. Results are similar to the AVANTI solutions when variations of the relative eccentricity vector dominate. Instead, in scenarios requiring predominant changes in the relative mean longitude, the required ΔV exhibits a 49.88% reduction (on average) when compared to the original methods. In all the test cases, the proposed solutions are within 3.5% of the global optimum in terms of ΔV. The practical accuracy of the presented guidance algorithms is also tested with numerical integration of equations of motion with J2 perturbation.

DEVELOPMENT OF CONTROL LAWS OF UNMANNED AERIAL VEHICLES FOR PERFORMING GROUP FLIGHT AT THE STRAIGHT-LINE HORIZONTAL FLIGHT STAGE

The article proposes an improved approach to controlling groups of unmanned aerial vehicles (UAVs) aimed at increasing the overall efficiency and flexibility of the control process. The use of a heterogeneous external field, which varies both in magnitude and direction, allows achieving greater adaptability and accuracy in controlling a group of UAVs. A vector field for unmanned aerial vehicles determines the direction and intensity of the vehicles' movement in space. Such vector fields can be used to develop UAV control laws, including determining optimal flight paths, controlling speed, avoiding obstacles, and ensuring coordination of a group of UAVs. The subject of the study is the methods of controlling groups of autonomous UAVs, where each vehicle may have different speeds and flight directions. To solve this problem, various methods of using a heterogeneous field have been developed and proposed. Instead of using a homogeneous field that provides a constant flight speed, a vector field is used that adapts to different conditions and characteristics of the vehicles in the group. This method allows for effective group management, ensuring the necessary coordination and interaction between the vehicles. An analysis of recent research and publications in the field of autonomous system control indicates the feasibility of using machine learning, vector fields, and a large amount of data to successfully coordinate the movement of autonomous systems. These approaches make it possible to create efficient and reliable control systems. The aim of the study is to develop laws for controlling the movement of a group of autonomous unmanned aerial vehicles at the stage of straight-line horizontal flight based on natural analogues to improve the efficiency and reliability of their coordinated movement in different conditions. The main conclusions of the research are that the proposed method of controlling groups of UAVs based on a heterogeneous field can be implemented. It takes into account a variety of vehicle characteristics and environmental conditions that are typical for real-world use scenarios. This work opens up prospects for further improving the management of UAV groups and their use in various fields of activity. The article emphasises the relevance of technology development for autonomous unmanned systems, especially in the context of autonomous transport systems.

Towards self‐organizing logistics in transportation: a literature review and typology

AbstractDeploying self‐organizing systems is a way to cope with the logistics sector's complex, dynamic, and stochastic nature. In such systems, automated decision‐making and decentralized or distributed control structures are combined. Such control structures reduce the complexity of decision‐making, require less computational effort, and are therefore faster, reducing the risk that changes during decision‐making render the solution invalid. These benefits of self‐organizing systems are of interest to many practitioners involved in solving real‐world problems in the logistics sector. This study, therefore, identifies and classifies research related to self‐organizing logistics (SOL) with a focus on transportation. SOL is an interdisciplinary study across many domains and relates to other concepts, such as agent‐based systems, autonomous control, and decentral systems. Yet, few papers directly identify this as self‐organization. Hence, we add to the existing literature by conducting a systematic literature review that provides insight into the field of SOL. The main contribution of this paper is two‐fold: (i) based on the findings from the literature review, we identify and synthesize 15 characteristics of SOL in a typology, and (ii) we present a two‐dimensional SOL framework alongside the axes of autonomy and cooperativity to position and contrast the broad range of literature, thereby creating order in the field of SOL and revealing promising research directions.

Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method

Carrier-based aircraft landing involves complex system engineering characterised by strong nonlinearity, significant coupling and susceptibility to environmental disturbances, and autonomous landing of carrier-based aircraft under fault states is even more challenging and riskier. To address the control-system problems of loss of efficiency and performance due to actuator faults and performance degradation due to various unknown disturbances, presented here is fault-tolerant control for carrier-based aircraft based on adaptive fuzzy sliding-mode fault-tolerant control (AFSMFTC). First, three models are built (the carrier-based aircraft fault model, the carrier air wake model and the deck motion model), and the control framework of the autonomous landing control system is introduced. Next, a longitudinal and lateral flight channel controller comprising an adaptive fuzzy network, adaptive laws and a sliding-mode controller is designed using the AFSMFTC method. The adaptive fuzzy network implements fuzzy approximation for the sliding-mode switching terms to further offset errors induced by unknown disturbances, the adaptive laws compensate for actuator faults, and the sliding-mode controller ensures tracking of the overall flight path. Furthermore, the stability of the fault-tolerant method is demonstrated using the Lyapunov function. Finally, simulation and comparative experiments show that the proposed fault-tolerant method has outstanding control performance and strong fault-tolerant capability, thereby providing an effective and feasible solution for designing an autonomous landing system for carrier-based aircraft under fault states.

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

Advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

Vehicle-to-Vehicle Based Autonomous Flight Coordination Control System for Safer Operation of Unmanned Aerial Vehicles

The exponential growth of unmanned aerial vehicles (UAVs) or drones in recent years has raised concerns about their safe operation, especially in beyond-line-of-sight (BLOS) scenarios. Existing unmanned aircraft system traffic management (UTM) heavily relies on commercial communication networks, which may become ineffective if network infrastructures are damaged or disabled. For this challenge, we propose a novel approach that leverages vehicle-to-vehicle (V2V) communications to enhance UAV safety and efficiency in UAV operations. In this study, we present a UAV information collection and sharing system named Drone Mapper®, enabled by V2V communications, so that UAVs can share their locations with each another as well as with the ground operation station. Additionally, we introduce an autonomous flight coordination control system (AFCCS) that augments UAV safety operations by providing two essential functionalities: UAV collision avoidance and UAV formation flight, both of which work based on V2V communications. To evaluate the performance of the developed AFCCS, we conducted comprehensive field experiments focusing on UAV collision avoidance and formation flight. The experimental results demonstrate the effectiveness of the proposed system and show seamless operations among multiple UAVs.

A team‐centric metric framework for testing and evaluation of human‐machine teams

AbstractWe propose and present a parallelized metric framework for evaluating human‐machine teams that draws upon current knowledge of human‐systems interfacing and integration but is rooted in team‐centric concepts. Humans and machines working together as a team involves interactions that will only increase in complexity as machines become more intelligent, capable teammates. Assessing such teams will require explicit focus on not just the human‐machine interfacing but the full spectrum of interactions between and among agents. As opposed to focusing on isolated qualities, capabilities, and performance contributions of individual team members, the proposed framework emphasizes the collective team as the fundamental unit of analysis and the interactions of the team as the key evaluation targets, with individual human and machine metrics still vital but secondary. With teammate interaction as the organizing diagnostic concept, the resulting framework arrives at a parallel assessment of the humans and machines, analyzing their individual capabilities less with respect to purely human or machine qualities and more through the prism of contributions to the team as a whole. This treatment reflects the increased machine capabilities and will allow for continued relevance as machines develop to exercise more authority and responsibility. This framework allows for identification of features specific to human‐machine teaming that influence team performance and efficiency, and it provides a basis for operationalizing in specific scenarios. Potential applications of this research include test and evaluation of complex systems that rely on human‐system interaction, including—though not limited to—autonomous vehicles, command and control systems, and pilot control systems.

UAV SWARM SYSTEMS

The rapid development of technology has introduced new and groundbreaking approaches to unmanned aerial vehicles (UAVs). One such innovation is the swarm UAV systems. These systems represent a technology where multiple small and often autonomous aircraft collaborate to perform specific tasks. An important component of this system is artificial intelligence. Swarm UAV systems can operate more effectively and coordinatedly by utilising artificial intelligence algorithms. Each UAV is equipped with autonomous control systems and artificial intelligence software. Through data analysis, object recognition, route planning, environmental adaptation and other complex tasks, artificial intelligence aids these UAVs in successfully completing tasks. As a result, swarm UAVs are capable of performing more complex and dynamic missions. The integration of artificial intelligence into swarm UAV systems holds great potential for several application fields. For instance, in search and rescue operations, AI can perform image analysis to detect individuals trapped under debris. In agriculture applications, AI can detect plant diseases or pests and optimize irrigation requirements. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. Additionally, we will focus on the potential future application areas of swarm UAV systems and the potential impacts of this integration. Swarm UAV systems and artificial intelligence represent a significant transformation in the world of technology, and this article will provide a resource to better understand these exciting developments. Keywords: Research of UAVs, integration of artificial intelligence, flight programs, autonomous control.

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

AbstractThis abstract discusses the significant progress made in autonomous vehicles, focusing on decision‐making systems and control algorithms. It explores recent advances, challenges, and contributions in the field, emphasizing the need for precise navigation and control. The paper covers various methodologies, including rule‐based methods, machine learning, deep learning, probabilistic approaches, and hybrid approaches, examining their applications and effectiveness in ensuring safe navigation. Additionally, it reviews ongoing research efforts, emerging trends, and persistent challenges related to decision‐making and manoeuvre execution in autonomous vehicles, addressing complex topics such as sensor measurement uncertainty, dynamic environment modelling, real‐time responsiveness, and safe interactions with other road users. The objective is to provide a comprehensive overview of the state of the art in autonomous vehicle navigation and control for readers.

Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier Method (ADMM) to the Receding Optimization of Model Predictive Control (MPC).

In order to improve the real-time performance of the trajectory tracking of autonomous vehicles, this paper applies the alternating direction multiplier method (ADMM) to the receding optimization of model predictive control (MPC), which improves the computational speed of the algorithm. Based on the vehicle dynamics model, the output equation of the autonomous vehicle trajectory tracking control system is constructed, and the auxiliary variable and the dual variable are introduced. The quadratic programming problem transformed from the MPC and the vehicle dynamics constraints are rewritten into the solution of the ADMM form, and a decreasing penalty factor is used during the solution process. The simulation verification is carried out through the joint simulation platform of Simulink and Carsim. The results show that, compared with the active set method (ASM) and the interior point method (IPM), the algorithm proposed in this paper can not only improve the accuracy of trajectory tracking, but also exhibits good real-time performance in different prediction time domains and control time domains. When the prediction time domain increases, the calculation time shows no significant difference. This verifies the effectiveness of the ADMM in improving the real-time performance of MPC.

Development of Lower Computer for Hydraulically Actuated Quadruped Bionic Robots Based on DSP

Background: Hydraulic quadruped robots have broad application prospects. Control system design is the core content of robot design. However, the micro-controllers used in the past have shortcomings such as long sampling period and simple algorithm. Methods: An electric control system of the layered, distributed structure for hydraulically actuated quadruped robots is designed considering a dog as a bionic model. In order to improve the response time and the steady precision of the system at the same time, a Fuzzy–PID compound control algorithm is put forward in this paper. The hardware and software of the control system are designed. Results: The lower computer’s control system for hydraulically actuated quadruped robots is developed using TMS320F28335 in series of DSP2000 as the core processor. Outside control circuit expands some external chips, such as AD7606, AD5754R, and PCA82C250, and a peripheral interface circuit is designed. Taking full advantage of the efficient processing power of the chip and the rich on-chip resources, the hardware circuit is simpler and reliable, and the software is also easy to implement. It is verified that the control system is rational and effective using experiments. Conclusions: The experimental results show that the control system designed in this paper is reasonable and can effectively control the joints of the quadruped robot. It has strong scalability and can meet the basic requirements of the autonomous mobile robot control system.

MVSR Normalization Algorithm Method for Improving Vehicle License Plate Recognition

Image processing and embedded systems are used in many applications such as object recognition, robotic applications, autonomous and remote control systems developed for the defense industry, medical applications, face recognition, and vehicle license plate recognition. Many vehicle license number plate detection methods are not effective under vehicle license plate images have a degree of rotation or low resolution images. Thus, we used MVSR normalization algorithm to detect vehicle license plate recognition for better accuracy and lower computational cost. The MVSR normalization algorithm, Mean–Variance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.

A Personal Microcomputer as an Access Control Management Platform in Road Transport

For many years, the use of new computer systems to control various elements of everyday human life has been observed. Separate systems manage access control; others are used to control blinds and roller shutters; and others manage systems in the garden. Many of these systems can be integrated using available systems. This paper presents an example of an access control management system based on the Raspberry Pi microcomputer and shows an analysis of its performance, accuracy, and possibility of improvement. This study used official devices manufactured by the Raspberry Pi Foundation; however, it is possible to create a similar system with custom parts. This project used open-source software. The authors argued that it is possible to create an autonomous vehicle access control system using microcomputers and optical character recognition technology. Using simple devices, the plate recognition system was built and tested, proving the thesis that it is possible to build an access control system using available devices. This also confirms the thesis that microcomputers can be used to control other systems in the human environment.

Planning and control of the ship curvilinear route

The possibility of applying the traditional navigation parameters used in pilotage (bearing and distance), as well as their sum and difference for planning and controlling the ship curvilinear route in coastal waters is being investigated. Mathematical apparatus is based on the classical navigation methods such as the theory of isolines, the lines of position, the principles of route planning, as well as the approximation of the curvilinear route. The mathematical justification of the circle arcs, ellipse and hyperbola used as the approximation lines of both the ship curvilinear route and the control isolines is given. The methods for estimating the vessel approaching to the point of the beginning and end of the turn and for controlling the ship movement along the planned path by measuring the navigational parameters are proposed. The problems of the physical and mathematical feasibility of the route arising at the junction points of the straight and curvilinear parts of the ship route are recommended to be resolved on the principles of transition curves (clothoids), considering the maneuvering characteristics of the vessel. The main theses of the research are supported by the graphical interpretation while the given expressions are ready for practical application. The formalization of the proposed methods in both automatic navigation systems and autonomous ship control systems will allow the navigator on the bases of the configuration of the relative position of landmarks and the ship curvilinear route to solve the problems of processing relevant navigation information in coastal and congested waters more efficiently in conjunction with the traditional pilotage methods, which is especially important for advanced autonomous navigation.

Corrigendum: RACS2: a framework of a remote autonomous control system for telescope observation and its application

Corrigendum: RACS2: a framework of a remote autonomous control system for telescope observation and its application

Research on autonomous decision-making technology for once-through steam generator

In this paper, the innovative research of the autonomous control system of the once-through steam generator (OTSG) is carried out, which is mainly aimed at the normal operating condition, especially the fault condition when the operating characteristics of the system change dramatically. The autonomous control system includes fault diagnosis, autonomous decision-making, hybrid control modules. The ISSA algorithm is applied in the research of autonomous decision-making modules because of its strong ability of optimization and fast convergence. This paper improves the SSA algorithm from two aspects: population initialization and finder position update and the optimization ability. The simulation results show that the autonomous control system proposed in this paper can make a decision in the shortest possible time, and provide an effective automatic control method to make the OTSG quickly and smoothly reach the safe state.

Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Existing research on auto-berthing of ships has mainly focused on the design and implementation of controllers for automatic berthing. For the real automatic docking processes, not only do external environmental perturbations need to be taken into account but also motion paths, docking strategies and ship mechanical constraints, which are important influential factors to measure autonomous docking methods. Through a literature review of ship path planning and motion control for automatic berthing, it is found that many studies ignore the interference of the actual navigational environment, especially for ships sailing at slow speed when berthing, or do not consider the physical constraints of the steering gear and the main engine. In this paper, we propose a hybrid approach for autonomous berthing control systems based on a Linear Quadratic Regulator (LQR) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which systematically addresses the problems involved in the berthing process, such as path planning, optimal control, adaptive berthing strategies, dynamic environmental perturbations and physically enforced structural constraints. The berthing control system based on the LQR and modified LQR-CMA-ES have been validated by simulation work. The simulation results show that the proposed method is able to achieve the automatic docking of the ship well and the system is robust and well adapted to environmental disturbances at slow speed when docking.

HYBRID NEURO-FUZZY-GENETIC ALGORITHMS FOR OPTIMAL CONTROL OF AUTONOMOUS SYSTEMS

In recent years, there has been an increasing demand for efficient and robust control algorithms to optimize the performance of autonomous systems. Traditional control techniques often struggle to handle the complexity and uncertainty associated with such systems. To address these challenges, hybrid neuro-fuzzy-genetic algorithms have emerged as a promising approach. This paper presents a comprehensive review of the application of hybrid neuro-fuzzy-genetic algorithms for optimal control of autonomous systems. The proposed algorithms combine the strengths of neural networks, fuzzy logic, and genetic algorithms to achieve adaptive and optimal control in real-time scenarios. The neuro-fuzzy component provides the ability to model and handle complex and uncertain systems, while the genetic algorithm component facilitates the optimization of control parameters. The combination of these techniques enables autonomous systems to adapt and optimize their control strategies based on changing environments and objectives. The paper discusses the underlying principles of hybrid neuro-fuzzy-genetic algorithms, their advantages, and challenges. It also provides a review of the state-of-the-art research in this field, highlighting successful applications and potential future directions. Overall, the integration of neuro-fuzzy-genetic algorithms in autonomous systems holds great promise for achieving optimal control in various domains, including robotics, aerospace, and autonomous vehicles.

Impact of Subarachnoid Hemorrhage on the Cardiac Autonomic Function During Rehabilitation in Children After Severe Traumatic Brain Injury.

This study aimed to investigate the impact of traumatic subarachnoid hemorrhage (tSAH) on cardiac autonomic control system (CACS) function in children after severe traumatic brain injury (TBI) during the subacute rehabilitation period. Thirty-three participants, 8-18 years of age, 42 (14-149) days after severe TBI at the beginning of the subacute rehabilitation, were included in the study. Six participants were diagnosed with tSAH during acute medical care (tSAH group). Heart rate variability (HRV) was assessed by the standard deviation of the N-N interval (SDNN) and the square root of the mean square differences of successive R-R interval (RMSSD) using a Polar RS800CX device while sitting at rest for 5 min. A second assessment was performed 8 weeks later. No significant difference between the tSAH and non-tSAH groups were found in the demographic and functional characteristics or injury severity. However, in comparison to the non-tSAH group, the tSAH group had lower SDNN (23.9 [10.5-47.3] vs. 43.9 [21.8-118.8], respectively; p = 0.005) and RMSSD values (11.8 [5.9-24.5] vs. 29.6 [8.9-71.7], respectively; p = 0.004). Neither group demonstrated changes in HRV values at rest in the second assessment, whereas the significant difference in SDNN (p = 0.035) and RMSSD (p = 0.008) remained. Children diagnosed with SAH after severe TBI presented poorer CACS function during the subacute rehabilitation. Given that reduced HRV values may be a marker for potential heart disease, the medical team should be aware of the influence of existing tSAH. Future studies with larger sample sizes and longer follow-up periods are warranted to further investigate this topic. ClinicalTrials.gov number: NCT03215082.

Towards the Testing and Validation of Autonomous Ships: Design of a Variable Stability Ship Control System

Maritime autonomous surface ships (MASSs) have received increased attention in both academic research and industrial applications in the global shipping and shipbuilding industry in recent years. According to its definition, a MASS can independently operate without human interaction. This brings the problem of how to test and evaluate the capabilities of the autonomous navigation control system developed for MASS. This paper proposes the novel concept of a variable stability ship and the design of its control system. A variable stability ship consists of a model-scaled ship, a model-following controller, and a hardware and software platform to implement its functions. The variable stability ship can replicate the dynamic responses of different reference ships despite their dissimilarities in physical parameters and hydrodynamic characteristics with the design of the model-following controller. Therefore, it can be used to test the performance of different control strategies on various ship types such that the R&D costs of physical model-based tests can be reduced, as well as shortening the time spent in developing physical ship models. Preliminary simulations are carried out to show its effectiveness and applicability.