Vehicle Control System Research Articles

Synthetic data formation for machine learning recognition of underwater objects

The main problem of machine learning for control systems of unmanned underwater vehicles is objectively very small samples of real data. The study aim was to develop an approach to the creation of synthetic data describing underwater objects for as the samples for training and validation in machine learning of control systems for auton-omous unmanned underwater vehicles. The subject of the study was a variety of underwater objects because real information about the shape, size and their external images is very limited. The data augmentation method was used, which makes it possible to obtain additional data on underdetermined objects of observation from the initial data while maintaining the classification features. Eight models have been developed that imitate the influence of various factors of the aquatic environment and allow using various augmentation methods (changing the position, adding noise, glare to the image, defocusing to create fuzziness; fragmentation, etc.) to obtain an almost unlimited number of images of any objects of man-made activity immersed in underwater environment, to varying degrees similar to the reference. Examples of the use of augmentation models that take into account changes in illumina-tion, transparency and the presence of an underwater landscape are given. Such synthetic (model) images may be the basis of a training set for machine learning to recognize and identify underwater objects. The trained model can be used as the basis of a decision support system for operators of remote-controlled unmanned underwater vehicles and as the basis for building control systems for autonomous uninhabited underwater vehicles for moni-toring underwater spaces.

Anti-Sleep Alarm for Drivers

Abstract: Nowadays it is very difficult to stay active all the time due to busy schedules. Falling asleep at the wheel can lead to serious consequences, accidents, and even death. This situation is much more common than we realize and therefore it is very important to fight this problem. So, to solve this problem, we developed a sleep alarm for drivers. This system alerts the user when he falls asleep at the wheel, thus preventing accidents and saving lives. This system is handy for long-distance travelers and late-night drivers. Designed with a blink sensor, it takes safety measures a step further by designing a system to stop the vehicle when the driver's eyes are closed. The system is developed using Arduino microcontroller. The eye blinking sensor is connected to a microcontroller that continuously monitors the frequency of the driver's eye blinking.If the driver's blink rate falls below a certain threshold, the system warns the driver and sends a signal to the vehicle's controlsystem to stop the vehicle.

The observer of traction electromagnetic torque at the shaft of a traction electric motor

BACKGROUND: Currently, the requirements to energy efficiency of transport vehicles are more and more tightened due to service cost reduction. Energy efficiency is the most relevant for them as it directly influences on the key feature — maximum mileage. Therefore it is necessary to control electric motors in a way to ensure maximal surface grip coefficient excluding propulsors’ slipping, as well as to establish operation modes of motors and switching between the modes to the one having the maximal efficiency. In this case, for effective control, it is necessary to define electromagnetic torque at the shaft of an electric motor and resistance torque at wheels rapidly for generating corresponding control and correcting exposures. AIMS: Development of the theoretical basis and the law of estimation the electromagnetic torque at the shaft of a traction electric motor of transport vehicles for the sake of efficient control of traction electric drive and its diagnostics. METHODS: Simulation of the observer of the electromagnetic torque at the shaft of a traction electric motor was carried out in the MATLAB/Simulink software package. RESULTS: The article provides with theoretical basis of formatting the law of optimal estimation of the electromagnetic torque at the shaft of a traction electric motor of transport vehicles for the sake of efficient control of traction electric drive, its diagnostics, as well as the results of simulation the law of estimation of the electromagnetic torque at the shaft of a traction electric motor obtained in the MATLAB/Simulink. CONCLUSIONS: Practical value of the study lies in ability of using the proposed observer for development of control system and diagnostics of transport vehicles.

Algorithm for forming traction and braking torque settings at the shaft of a traction motor by means of a single pedal

BACKGROUND: Transport service providers and consumers specify requirements to vehicles for increasing energy efficiency, cost reduction, as well as improving ergonomics and comfort. Fuel costs are among the significant costs, so it is very promising to use vehicles with electric drives, such as battery-powered electric vehicles. It is very important for them to have energy-efficient control of traction drives that are the main sources of energy loss, characterized by high efficiency that helps to increase single-charge mileage. To achieve this, it is necessary to form the settings of the requested engine torque using the accelerator pedal only, taking into account traffic conditions, vehicle characteristics over the entire velocity range. AIMS: Development of the fundamentals and the law of determining the settings of the traction and regenerative torque of a traction electric drive, the run-out mode depending on motion velocity and the accelerator pedal position, taking into account the required level of comfort and ergonomics of control. METHODS: The law of determining the setting of the traction and regenerative torque of the traction electric drive, the run-out mode were modeled in the MATLAB/Simulink software package. RESULTS: The fundamentals of building the law of determining of the setting of the traction and regenerative torque of the traction electric drive, the run-out mode for effective control, increasing ergonomics and comfort of a vehicle, as well as the results of virtual simulation of the formation of settings in the MATLAB/Simulink in conditions close to the operational are given in the paper. CONCLUSIONS: The practical value of the study lies in the possibility of using the proposed law for determining the setting of the traction and regenerative torque of the traction electric drive, the run-out mode for the development of a traction drive control system for transport vehicles.

Research into the Peculiarities of the Individual Traction Drive Nonlinear System Oscillatory Processes

Auto-oscillations may occur in moving vehicles in the area where the tire interacts with the support base. The parameters of such oscillations depend on the sliding velocity in the contact patch. As they negatively affect the processes occurring in the electric drive and the mechanical transmission, reducing their energy efficiency, such processes can cause failures in various elements. This paper aims to conduct a theoretical study into the peculiarities of oscillatory processes in the nonlinear system and an experimental study of the auto-oscillation modes of an individual traction drive. It presents the theoretical basis used to analyze the peculiarities of oscillation processes, including their onset and course, the results of simulation mathematical modeling and the experimental studies into the oscillation phenomena in the movement of the vehicle towards the supporting base. The practical value of this study lies in the possibility to use the results in the development of algorithms for the exclusion of auto-oscillation phenomena in the development of vehicle control systems, as well as to use the auto-oscillation processes onset and course analysis methodology to design the electric drive of the driving wheels.

Development of a line following autonomous spraying vehicle with Machine vision-based leaf density estimation for cherry tomato greenhouses

Traditional manual spraying systems and even modern automated ones often neglect the critical factors of plant growth stage and coverage density during spraying for plants, which can result in suboptimal yields. To address this issue, this study aims to describe the development of an efficient and affordable autonomous spraying vehicle (ASV) for application in cherry tomato greenhouses. The ASV integrates a visual autonomous spraying system (VASS) and a vehicle control system (VCS). The VASS uses leaf density signalling with machine vision and a vertical spray boom to target leaves for precision spraying application. Leaf density signalling is an algorithm developed to recognise and estimate leaf density by machine, simplifying the real-time determination of leaf density levels in plants. The performance of the algorithm demonstrates an accuracy of 84 % with an average video processing speed of 33.3 ms. The VASS is integrated into VCS to facilitate remote or autonomous navigation. The visual line-following system (VLFS) supports ASV in autonomously navigating between narrow-spaced rows in cherry tomato greenhouses, thereby automating the entire spraying process. The analysis of the comprehensive performance of the ASV and the comparative analysis with knapsack sprayers in field experiments are discussed and presented. Comparative analysis with manually operated sprayers demonstrates the better performance of the ASV. These experiments highlight the potential of the ASV to replace traditional manual spraying methods while enhancing spraying precision.

Research on precise route control of unmanned aerial vehicles based on physical simulation systems

In order to understand the precise route control system of unmanned aerial vehicles, the author proposes a research on precise route control of unmanned aerial vehicles based on wireless sensor networks and physical simulation. The author first introduces the definitions of various coordinate systems and related motion parameters of unmanned aerial vehicles. On this basis, the nonlinear dynamic equations and kinematic equations of the unmanned aerial vehicle were obtained through force analysis. Secondly, based on wireless sensor networks, a route control system for unmanned aerial vehicles was designed. There may be deviations between the actual route and the planned route of drones during flight, which not only reduces the quality of operations but also affects operational efficiency. Wireless sensor network is a technology closely integrated with unmanned aerial vehicles, which can be used to control the route of unmanned aerial vehicles. The system consists of four parts: unmanned aerial vehicle platform, sensor nodes, convergence nodes, and control center. The control of the route is completed through a two-dimensional coordinate system following algorithm. Finally, the drone will undergo flight tests before and after the installation of this route control system. The drone before the installation of this system will use traditional route control methods. There are two types of planned routes: straight and curved, with the curve being a circular shape with a radius of 5 km; Record the deviation value between the actual route and the planned route every 150 s during the flight, and compare the control accuracy of the two methods on the route. The experimental results show that the wireless sensor network is more stable in tracking straight and curved routes, and has high accuracy in route control.

A Comparative Study of Longitudinal Vehicle Control Systems in Vehicle-to-Infrastructure Connected Corridor

<div>Vehicle-to-infrastructure (V2I) connectivity technology presents the opportunity for vehicles to perform autonomous longitudinal control to navigate safely and efficiently through sequences of V2I-enabled intersections, known as connected corridors. Existing research has proposed several control systems to navigate these corridors while minimizing energy consumption and travel time. This article analyzes and compares the simulated performance of three different autonomous navigation systems in connected corridors: a V2I-informed constant acceleration kinematic controller (V2I-K), a V2I-informed model predictive controller (V2I-MPC), and a V2I-informed reinforcement learning (V2I-RL) agent. A rules-based controller that does not use V2I information is implemented to simulate a human driver and is used as a baseline. The performance metrics analyzed are net energy consumption, travel time, and root-mean-square (RMS) acceleration. Two connected corridor scenarios are created to evaluate these metrics, including one scenario reconstructed from real-world traffic signal data. A sensitivity analysis is also performed to quantitatively identify key parameters that have the highest impact on the three metrics of interest.</div>

Sliding Mode Controller for Autonomous Tractor-Trailer Vehicle Reverse Path Tracking

In the past few years, there has been a growing interest among researchers in developing control systems for autonomous vehicles, specifically for tractor-trailer systems. This newfound interest is driven by the potential benefits of enhancing safety, reducing costs, and addressing labor shortages in the industry. Two industries that could reap the rewards of these systems’ advancements are cargo and agriculture transportation. One of the challenging tasks for the truck trailer vehicle is driving in reverse. Backward path tracking of tractor-trailers is a complex control problem with practical applications. The difficulty in controlling the vehicle arises due to its unstable internal dynamics, coupled nonlinear terms, and the under-actuated nature of the system. There is also a limit to the angle at which the steering can be turned before the risk of a jackknife accident increases significantly. In response to these challenges, this paper introduces a robust sliding mode controller designed for path tracking in reverse-driving tractor-trailer systems. The novelty of our work lies in addressing these challenges, which have not been extensively studied in the past. The proposed controller is analyzed, and its performance is tested and verified using different scenarios. The simulation examples show superior control performance, and we anticipate that this novel controller holds the potential to be widely adopted as a fundamental component in the path-tracking algorithms of autonomous truck trailer systems.

Dynamic Switching Control of Power-Split Hybrid Electric Vehicles Based on Time Delay Prediction and Interference Compensation

The deterioration of vehicle dynamics caused by the simultaneous action of time delay and interference is a key issue for the switching control system in a power-split hybrid electric vehicle (PS-HEV). This paper presents an innovative compound control method based on extended state observer (ESO) incorporating time delay during the mode transition process. First, the coupling effect of time-varying delay and external disturbance during the typical mode switching process is analyzed in depth, and the double deterioration mechanism of the two coupling effects on vehicle dynamics and ride comfort is revealed. To accurately estimate the system disturbance under the influence of time delay, an unconventional ESO is designed further by adopting the Markov chain time delay prediction model, and the main tool for interference observation is selected by comparing the practicability, reachable bandwidth and observation accuracy. Moreover, the stable mode switching performance and interference suppression are thoroughly investigated, and the smooth transmission of multi-power is realized through the multi-module fusion design of the compound coordinated controller. Finally, the hardware-in-the-loop test is implemented to validate the effectiveness of the control strategy, which also provides a novel idea and lays a theoretical foundation for the optimal design of the PS-HEV controller.

Vehicle compound planning and control system

Vehicle compounds act as transshipment points in the supply chain of finished vehicles and face constant performance pressure in their position as a link between the manufacturer and the customer. Improving the process of vehicle movements on compounds holds large potential for raising a compound’s competitiveness. Recent approaches have mainly focused on improving vehicle storage allocation to gain short travel distances for the vehicles on the compound. We go one step further and aim to improve the compound’s productivity by optimizing the drivers’ transfers from one order to the next. We propose a modified operational process flow and design a planning and control system that assigns orders on an individual level. The assigned orders are within walking distance whenever possible. Shuttle tours only come into play where no order starting point is in close vicinity to a previous order. We model the system as an online optimization that aims at two objectives, i.e., time-minimal order execution for drivers and time-minimal shuttle bus routing. The online optimization allows for a rolling planning, which enables the system to account for real-world uncertainties and unforeseen events. We evaluate the system in a discrete-event simulation that maps a real-world use case. The results show that the proposed approach reaches a roughly 25% higher vehicle handling rate than the status quo processes. Due to its generality of the optimization-based problem formulation, the approach is also applicable to other fields, such as vehicle relocation in car-sharing systems or intra-logistic scenarios in production processes.

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.

Construction of a model of steganographic embedding of the UAV identifier into ADS-B data

Secure data exchange in the control system of unmanned aerial vehicles (UAVs) is an important aspect for preventing unauthorized access and safety of aerial vehicles. Given the problems of automatic dependent surveillance-broadcast (ADS-B) data protection, the safety level of UAV flight tasks and air traffic in general is significantly reduced. Therefore, the protection of ADS-B data is an urgent task. The object of the study is the process of steganographic protection of ADS-B format data. A relevant problem of estimating the probabilistic time characteristics of the steganographic protection process is solved, taking into account the features of data embedding in the ADS-B format container. To solve it, a mathematical formalization of the methods of finding probabilistic-temporal characteristics of steganographic systems was carried out. A model of steganographic data transformation operations based on the Chinese remainder theorem has been built. The main difference of the model is taking into account the features of the ADS-B format data. This made it possible to formalize and evaluate the time functions of steganographic encoding and decoding of UAV identifiers with an integrated ADS-B system. A model of steganographic data transformation operations based on the finite integral ring theorem has been constructed. A list of operations performed in the developed algorithm has been compiled. This made it possible to carry out mathematical formalization of operations for complex use in the model of steganographic protection of UAV identifiers with a built-in ADS-B system. The mathematical model was studied and the estimation of the random value of the time of steganographic transformation of data, as well as the confidence interval, was performed. With the help of the reported set of models, it is possible to estimate the probability of the algorithm’s execution time falling within the given interval. The results of the calculation of probabilistic-time characteristics could be used in models of a higher level of the hierarchy

Observer-based safety monitoring of nonlinear dynamical systems with neural networks via quadratic constraint approach

The safety monitoring for nonlinear dynamical systems with embedded neural network components is addressed in this paper. The interval-observer-based safety monitor is developed consisting of two auxiliary neural networks derived from the neural network components of the dynamical system. Due to the presence of nonlinear activation functions in neural networks, we use quadratic constraints on the global sector to abstract the nonlinear activation functions in neural networks. By combining a quadratic constraint approach for the activation function with Lyapunov theory, the interval observer design problem is transformed into a series of quadratic and linear programming feasibility problems to make the interval observer operate with the ability to correctly estimate the system state with estimation errors within acceptable limits. The applicability of the proposed method is verified by simulation of the lateral vehicle control system.

Curved-Line Path-Following Control of Fixed-Wing Unmanned Aerial Vehicles Using a Robust Disturbance-Estimator-Based Predictive Control Approach

In this research, the design of a robust curved-line path-following control system for fixed-wing unmanned aerial vehicles (FWUAVs) affected by uncertainties on the latitude plane is studied. This is undertaken to enhance closed-loop system robustness under unknown uncertainties and derive the control surface deflection angle directly used to control FWUAVs, which has rarely been studied in previous works. The system is formed through the mass center position control (MCPC) and yaw angle control (YAC) subsystems. In the MCPC, the desired yaw angle, which is treated as the reference signal for the YAC subsystem, is calculated analytically using path-following errors, current flow angles, and the yaw angle. In the YAC, a disturbance estimator is designed to estimate uncertainties such as nonlinearities, couplings, time variations, model parameter perturbations, and unmodeled dynamics. Predictive functional controllers are designed to target nominal systems in the absence of uncertainties, such that the estimations of the uncertainties can be incorporated through feedback for closed-loop system robustness enhancement. The simulation results show that higher path-following precision and stronger robustness for the FWUAVs based on the proposed approach can be achieved using only rough model parameters compared with the conventional nonlinear dynamic inversion, which requires detailed model information.

Dynamic mass estimation framework for autonomous vehicle system via bidirectional gated recurrent unit

AbstractThe precise estimation of vehicle mass is crucial for the optimal performance of electronic control systems in autonomous vehicles. However, the nonlinear nature of vehicle dynamics makes it a challenging task to estimate the mass accurately. In response to this concern, this paper proposes a dynamic vehicle mass estimation framework underpinned by a bidirectional gated recurrent unit, developed using deep neural networks. The bidirectional mechanism and gated recurrent unit network are adopted to elevate the precision of the neural network estimator. The dataset used for training and validation is collected from heavy‐duty vehicle simulations and real vehicle road tests. The average root mean square error, mean absolute percentage error, and mean absolute error evaluated over simulation tests are 92.66 kg, 0.93%, and 79.67 kg, respectively, and those in real vehicle data tests are 16.61 kg, 0.13%, and 16.61 kg, respectively. The outcomes manifest that the method put forth surpasses the contrasted approaches in relation to accuracy in the conducted experiments.

IoT-Based Automatic Braking Control System for Electric Vehicles and Monitoring System

IoT-Based Automatic Braking Control System for Electric Vehicles and Monitoring System

Preface

The 2023 4th International Conference on Electrical, Electronic Information and Communication Engineering (EEICE 2023) took place on April 21st to 23rd, 2023 in Dalian, China (hybrid event), attracting about 150 delegates around the world. The previous three conferences of EEICE series were successfully held via virtual form, enjoying great popularity.EEICE 2023 aims to bring together leading academic scientists, researchers, and practitioners to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the domain of interest from around the world to exchange, share their experiences and research results on all aspects of electrical, electronic information and communication engineering.EEICE 2023 is the premier interdisciplinary platform for the presentation of new advances and research results in related fields. Prospective authors are kindly encouraged to contribute to and help shape the Conference through submissions of their research abstracts, papers, posters and high-quality research contributions describing original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of electrical, electronic information and communication engineering, and speakers are cordially invited for presentation at the Conference.At this meeting we had a comprehensive overview of this fascinating field and of future scenarios thanks to the participation of leaders of the most important projects. Professor Wei Yue from Dalian Maritime University, China performed a keynote speech on the title Connected and Automated Vehicles Control with Communication Constraints. The efficient and safe operation of connected and automated vehicle systems is highly dependent on the ability of the vehicles to communicate with each other and with the infrastructure. However, in real-world scenarios, communication constraints such as limited bandwidth, latency, and data loss can significantly impact the effectiveness of vehicle control systems. Therefore, the goal of this research is to ensure that connected and automated vehicle systems can operate effectively and safely in the face of communication constraints, enabling the widespread adoption of this technology to enhance the efficiency and safety of transportation systems. Their sharing of knowledge and experience has sparked heated discussion in the Conference.Last but not the least is our gratitude. As editors we would like to express our sincere thanks to all the plenary and invited speakers, the members of the Technical Program Committee and the Organizing Committee for the success of the Conference, which has given rise to this present volume of selected papers. Special thanks to the members of Journal of Physics: Conference Series for making this volume published.The Committee of EEICE 2023List of Conference Committee is available in this pdf.

Optimisasi Energi Melalui Pelatihan Mekanisasi Sistem Kontrol Mobil Listrik: Sebuah Pendekatan PKM

The increasing population growth and demand for transportation energy in Indonesia, particularly from fossil fuel-powered vehicles, highlight the urgency of adopting environmentally friendly technologies. The government's focus on electric vehicles is promising, but it requires competent human resources support. The Community Service Program (PKM) titled "Energy Optimization Through Mechanization Training for Vehicle Control Systems: A PKM Approach" at the Faculty of Engineering, Universitas Negeri Makassar, has made a significant contribution. Through the PKM approach, students are actively engaged, enhancing their knowledge and practical skills in implementing energy-efficient technology. The outcomes include improved assembly and integration skills for control system mechanisms and the implementation of technology in personal vehicles. This training also raises environmental awareness and empowers students as agents of change. Positive evaluations from aspects of knowledge, skills, and social impact support the importance of developing competent human resources to facilitate the advancement of energy-efficient vehicle technology in Indonesia.

Structural models of Mealy finite state machines detecting faults in control systems

The subject matter of this article is a control system for unmanned aerial vehicles (UAVs) whose mathematical model is a finite state machine (FSM). The goal is to develop FSM structural models that enable (1) detection of multiple faults of FSM elements caused by an electromagnetic pulse or laser beam, and (2) prevent negative impacts on the controlled object. The tasks to be solved are as follows: to develop FSM structural models to detect invalid input vector X for the whole FSM and in each state, to detect invalid output vector Y for the whole FSM, at each transition and in each state, invalid code of the present (current) state, invalid code of the next state, and invalid transition between states; to determine the possible causes of the faults, which can be the failure in the logic Φ of forming the code of the next state, the invalid input vector X, the failure in the feedback circuit, the failure in the logic Ψ of forming the output vector, the failure in the state register R, the failure in the wire between the FSM input and the input of the logic Ψ; development of a combined structural model for the detection of all listed faults with a minimum number of additional combinational circuits, as well as a structural model that combines all additional combinational circuits. The methods used are: the theory of finite state machines, structural models of FSMs, state encoding methods of FSMs, representation methods of FSMs, and Verilog hardware description language. The following results were obtained: (1) the Mealy FSM structural models were developed to detect all the above mentioned faults, (2) the combined FSM structural models were developed, and (3) the possible causes of faults detected by each FSM structural model were identified. Experimental studies have shown that for the presented FSM structural models, the area overhead averages 3-23%, for one-hot encoding of FSM states, and 2-8%, for binary encoding of FSM states. Conclusions. The scientific novelty of the obtained results consists in the following for the first time FSM faults that are not caused by radiation and cosmic rays but by an electromagnetic pulse affecting the control device are considered; the number of faults is not limited for the state codes as well as for the input and output vectors; the faults can be detected not only in the state register R but also in the input vector X, in the logic Φ of generating the next state code, in the logic Ψ of generating the output signals, and in the feedback circuit; the invalid transitions of FSMs and the transitions to invalid states are also detected; the proposed structural models not only detect FSM failures but also prevent their negative impact on the controlled object; combined structural models allow simultaneous detection of faults in all elements of the FSM. Future research will focus on developing structural models for correcting FSM failures.