Vehicle Control System Research Articles

Design and Application of Fuzzy PID Controller Based on MATLAB

In an era where the development of intelligent technology is a pivotal force propelling economic advancement, enhancing production efficiency, diminishing costs, and reshaping daily life, the field of intelligent vehicle control technology has garnered significant attention among automotive researchers. The dynamics of smart cars during driving often exhibit nonlinearity and are susceptible to external environmental disturbances, such as wind resistance and road surface inequality. Traditional PID controllers have limited ability to suppress these interferences, so this study uses fuzzy PID algorithms to solve the inherent challenges in intelligent vehicle control. Through comprehensive analysis of the current landscape and technological advancements in intelligent vehicle control systems, coupled with rigorous MATLAB/Simulink simulations and real-world testing, the study showed that the overshoot of the adaptive fuzzy PID was reduced by 86% compared to the traditional PID. Consequently, it markedly enhances the precision and stability of speed control in intelligent vehicles, providing a groundbreaking perspective and practical methods for the enhancement of intelligent vehicle control systems with considerable application potential.

Research on Triplex Redundant Flight Control System Based on M1394B Bus

In the modern aviation field, flight control systems’ reliability and safety are paramount. This paper presents a triplex redundant flight control system based on the M1394B bus and designs several key algorithms to enhance system performance. Firstly, a triplex redundant flight control system with a redundant bus structure is constructed based on the characteristics of the M1394B bus. Secondly, a periodic synchronization algorithm with automatic adjustment capabilities is designed to achieve periodic synchronization among the Vehicle Management Computers. An improved voting algorithm based on a sliding window is proposed to enhance the decision-making accuracy and reliability of the control commands output by the flight control system. Additionally, a system reconstruction algorithm is designed to promptly identify and isolate faults, enabling the recovery and reallocation of system resources. Finally, experiments validate the effectiveness of the synchronization algorithm, voting algorithm, and system reconstruction algorithm. The results indicate that the system can effectively address practical engineering challenges and significantly improve reliability and stability. This research provides an essential theoretical foundation and practical reference for the design of future flight control systems for unmanned aerial vehicles and aircraft, holding significant relevance to application.

The docking control system of an autonomous underwater vehicle combining intelligent object recognition and deep reinforcement learning

This study develops a visual-based docking system (VDS) for an autonomous underwater vehicle (AUV), significantly enhancing docking performance by integrating intelligent object recognition and deep reinforcement learning (DRL). The system overcomes traditional navigation limitations in complex and unpredictable environments by using a variable information dock (VID) for precise multi-sensor docking recognition in the AUV. Employing image-based visual servoing (IBVS) technology, the VDS efficiently converts 2D visual data into accurate 3D motion control commands. It integrates the YOLO (short for You Only Look Once) algorithm for object recognition and the deep deterministic policy gradient (DDPG) algorithm, improving continuous motion control, docking accuracy, and adaptability. Experimental validation at the National Cheng Kung University towing tank demonstrates that the VDS enhances control stability and operational reliability, reducing the mean absolute error (MAE) in depth control by 42.03% and pitch control by 98.02% compared to the previous method. These results confirm the VDS's reliability and its potential for transforming AUV docking.

Optimization of autonomous vehicle control system reliability on a commercial scale through LIF dombi methodologies.

The resilient framework of Linguistic Intuitionistic Fuzzy Sets (LIFSs) allows for the representation and management of uncertainties related to intuitionistic judgments and linguistic expressions. Recent advances in passive and active safety systems have reduced highway fatalities. Autonomous vehicles can improve safety, efficiency, and mobility by navigating traffic without a driver. One of the primary benefits associated with this technology is that it reduces the number of traffic collisions that result in millions of fatalities and numerous injuries. In this research work, we devise two novel aggregation operators: the linguistic intuitionistic fuzzy Dombi ordered weighted averaging (LIFDOWA) operator and the linguistic intuitionistic fuzzy Dombi ordered weighted geometric (LIFDOWG) operator, and explore their fundamental structural properties. We provide innovative score and accuracy functions for multiple attribute decision-making (MADM) problems within the framework of LIF knowledge. Moreover, we use these techniques to develop a specialized algorithm for MADM issues that addresses the complexities arising from ambiguous data during the selection process. We also demonstrate the effectiveness of our proposed methods by applying them to solve the MADM scenario of selecting an optimal approach to improve the credibility of autonomous vehicle control systems on a commercial scale. In addition, we also compare and evaluate the authenticity and practicability of the newly designed techniques in comparison to existing methodologies.

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Abstract This paper addresses the critical need to quantify vehicle trajectory uncertainty in autonomous driving under environmental variability. We focus on predicting the posterior distribution of vehicle trajectories over a fixed horizon, given an initial state and a sequence of actions. We propose and compare three approaches: a probabilistic seq2seq model based on stochastic variational Gaussian processes, sequential Monte Carlo simulation with a single-step Gaussian process model, and a hybrid model that leverages the strengths of both methods. Each approach incorporates a baseline vehicle kinematics model to enhance stability and convergence. We evaluate these methods using a dataset generated from the CARLA simulator, assessing both point error metrics and probabilistic prediction metrics. This research introduces novel approaches to quantifying vehicle trajectory uncertainty through various uncertainty quantification techniques, with the goal of improving the safety and reliability of autonomous vehicle control systems.

Motion Control System for USV Target Point Convergence.

The goal of this paper is to establish a motion control system for unmanned surface vehicles (USVs) that enables point-to-point tracking and dynamic positioning. This includes the heading control and path following control of USVs. A hardware and software platform for USVs using microcontrollers is designed. This paper presents the construction of a kinematics and dynamics model for an unmanned catamaran. The motion process is divided into two segments. In the target point tracking segment, the heading coordinate system and the ship coordinate system are established. Based on these, a control method using differential steering to track the desired yaw angle is designed to improve the tracking efficiency. And the accuracy of heading keeping and path following is improved by combining the cascade PID controller. In the dynamic positioning segment, a self-adjusting mechanism is designed, thereby enhancing the flexibility of thrust distribution and improving the accuracy of the USV's positioning retention in wind and wave environments. Finally, experimental validation is carried out to verify the effectiveness of the design proposed in this paper by issuing control commands and saving the return data through the upper computer, and then analyzing the return data with MATLAB (R2022b, MathWorks, Natick, MA, USA).

Design of Neural Network-Based Multi-Fault Tolerant Control System for Unmanned Aerial Vehicles

Actuator failure seriously threatens the flight safety of unmanned helicopters. Considering the problem of multiple faults such as actuator bias and failure in unmanned helicopters, a composite fault-tolerant flight control algorithm is proposed. For actuator bias fault, a nonlinear fault observer is designed to estimate it in real time; for actuator failure fault, a same-dimensional auxiliary system is constructed and processed by neural network technology. The composite fault-tolerant flight controller of the unmanned helicopter is designed by backstepping method, and the Lyapunov stability theory is used to prove that the error signals of the closed-loop system are bounded and convergent. Simulation results show that the proposed control algorithm can improve the fault tolerance of the unmanned helicopter when multiple actuator faults occur, ensuring its safe flight.

Principles of construction of control systems for unmanned vehicles

Principles of construction of control systems for unmanned vehicles

Design of motion control system for unmanned wheeled electric vehicle

Abstract The unmanned wheeled electric vehicle is a weapon platform that draws significant attention in current and future battlefields, and research on its motion control is fundamental. This paper introduces the design of a 6×6 unmanned wheeled electric vehicle and discusses the motion control methods applicable to this vehicle. The electric drive system of this vehicle comprises six independent asynchronous motors and corresponding motor controllers. A comprehensive controller designed based on DSP connects the six motor controllers. As it is an experimental platform, the motion control algorithms are deployed on an onboard computer, which connects to the comprehensive controller. Using the Backstepping method, the kinematic control algorithm for the 6×6 unmanned wheeled electric vehicle is designed. The global asymptotic stability of the control system is proven through the Lyapunov function, and its control performance is verified via simulation experiments. Finally, the system is deployed and tested on the actual vehicle. Experimental results show that the motion control system designed in this paper enables the unmanned wheeled electric vehicle to achieve effective motion control. The control laws derived from the Backstepping method are relatively simple and have low requirements for the sampling period. In practical systems, output speed magnitude and rate of change must be limited to stay within allowable system parameters.

Формування траєкторії керованих літальних апаратів

Currently, there is an increased interest in the creation of flight control systems for unmanned aerial vehicles, which, taking into account real conditions, would ensure its trajectory and thereby determine the effectiveness of the use of the aerial vehicle. Aircraft navigation, in particular ballistic cruise missiles, requires the formation of high-precision control in order to achieve both final results and local optimization tasks at each of the flight stages: take-off, departure to set trajectories, maneuvering, etc. The development of admissible control and its optimization by computer-mathematical methods of modeling and optimization is a time-consuming process and requires significant expenditures of various types of resources. An element of the effectiveness of such a mathematical apparatus for guaranteeing and increasing the reliability and effectiveness in achieving the set goal is the speed of calculations, which, in turn, requires the simplification of the mathematical model by obtaining functional dependencies for calculating the flight trajectory, avoiding complex mathematical calculations. The complete working model for calculating the desired trajectory of the aircraft will be determined by the specified control functions and will be provided by a system of partial differential equations with time, and the necessary parameters for calculating all aerodynamic forces and moments are tabular data in most cases. A mathematical model for solving the task of forecasting navigation for such complex controlled systems is proposed to be carried out using two coordinate systems: starting and speed. Moreover, all calculations of the velocity vector, which depends on the net effect of three aerodynamic forces acting on the aircraft, should be carried out in the speed coordinate system, and coordinate control of the flight along the entire dynamic trajectory should be determined in the starting system

Integrated Chassis Control for Multi-Axle Vehicles: A Comprehensive Review

Multi-axle vehicles (MAVs) are used to transport heavy loads, navigate challenging terrain, and perform specific tasks. However, designing vehicle control systems for such vehicles is challenging. MAVs, such as trucks, buses, military vehicles, and multi-purpose vehicles, require sophisticated control techniques to increase the stability and ride comfort of such vehicles. This paper provides a comprehensive review of standalone control systems and integrated control systems (ICSs) for the steering, driving/braking, and suspension systems of MAVs, with a particular focus on the optimal tyre force distribution (TFD) in ICSs for MAVs. It also discusses various objective functions and optimisation methods used for the TFD. The findings indicate that optimising TFD significantly improves the stability of MAVs. As such, future studies may consider examining optimising three-axis TFD for MAVs to further improve vehicle stability.

Design and Application of Driving Resistance Test Device for Aircraft Tire and Soil Pavement

In view of the lack of soil bins for studying the surface interaction between aircraft wheels and soil, this study designed an indoor test bench for aircraft wheels and soil, including a soil container, loading vehicle, and intelligent measurement and control system, to test key parameters such as tire speed and wheel frictional resistance. The test system is capable of achieving speed regulation ranging from 0 to 30 km/h. The vertical load adjustment range with an adjustment interval of 10 kg spans from 90 to 140 kg. The soil type, compaction degree, and other conditions can be modified as per requirements to vary multiple test conditions, thereby enabling us to explore their influence on the driving resistance of the wheels. Moreover, the test data can be collected and processed in real time. A performance test of a wheel–soil table was carried out. The results show that the wheel–soil table test system is stable and reliable and can determine the relationship between the tire and soil, and the structural design of the test system meets the use requirements. In addition, it achieves the target test speed, data acquisition frequency, and stability. In terms of functionality and operational difficulty, the data acquisition of the entire test process is automated, and the test system achieves better informationization than previous methods. The overall operation of the wheel–soil platform is stable and powerful; thus, the model test platform design goal is achieved, and the testing requirements are met.

An efficient technique for developing the active sound control system in electric vehicle

The sound characteristic is a critical metric to manifest the brand differentiation of electric vehicle (EV), and the sound design with more diverse acoustic characteristics has become a hot issue in current research of EV technology. In this paper, an efficient technique of active sound design (ASD) is explored to develop the control system of active sound generation (ASG) with powerful sound quality for EV. Firstly, an optimization algorithm of sound synthesis based on multi-frequency superimposition is proposed to improve the harmonic interference phenomenon in the synthesized sounds. Subsequently, an adaptive sound control strategy is formulated, where an iterative accumulation method is proposed to calculate the “virtual engine speed” of EV, and a gain table is presented to divide the running state. Besides, an ASG system is developed based on the proposed ASD technique. The tests result demonstrate that there are 18 target order sounds are reproduced perfectly, and the powerful sound quality interior EV is enhanced while the original interior acoustic environment of EV is retained, which confirms the effectiveness of the proposed control technique of ASG system. The proposed ASD technique here accelerates the change from silence to sound quality in the electric vehicles, which has important theoretical significance and engineering value.

USING THE ARDUINO PLATFORM IN THE RESEARCH OF INTERNAL COMBUSTION ENGINES

Modern measuring systems used in the study of internal combustion engines require significant investments. The universal Arduino platform offers ready-made powerful hardware modules for data collection (shields) and control, which work in a wide range of frequencies and signal amplitudes, provide their analysis and processing, allow to implement equipment management, and have a relatively low cost and free software with standard libraries. These facts testify to the high relevance of the use of the Arduino platform in the study of internal combustion engines. The purpose of this work is to determine the possibilities and features of using the Arduino hardware and software platform in the study of processes occurring in energy power plants with internal combustion engines, early prototyping of their control and diagnostic systems. The paper considers the possibility of hardware and software connection of sensors for measuring basic physical quantities to the Arduino platform: crankshaft rotation frequency (based on induction sensors and Hall sensors), pressure, air and fuel flow, temperatures (resistance sensors and thermocouples). In addition, the characteristics (dependence of the measurement parameter on the output value) of some popular sensors used in the study of internal combustion engines are given. It is important to consider that the equipment used for the study of processes in the ICE must meet the certification requirements. In the case of Arduino, this certification may not be available. However, the recommendations for the use of various sensors, shields and Arduino boards can be applied if the measured quantity is not the determining factor. It is important to consider these limitations and use Arduino with an understanding of the capabilities and limitations of the platform. The use of Arduino in the educational process in the training of specialists in the field of power engineering and in the research of energy installations of vehicles will allow students to apply the knowledge, skills and abilities acquired in the learning process at the hardware and software levels, which will create additional motivation for further study devices and principles of operation of automated and automatic vehicle control systems.

Improved unmanned aerial vehicle control for efficient obstacle detection and data protection

<p>The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.</p>

Research on Stability Control System of Two-Wheel Heavy-Load Self-Balancing Vehicles in Complex Terrain

Research on Stability Control System of Two-Wheel Heavy-Load Self-Balancing Vehicles in Complex Terrain

Stochastic optimization of levitation control system for maglev vehicles subjected to random guideway irregularity

The dynamic performance of maglev vehicle-bridge coupled systems subjected to random guideway irregularity, especially the levitation stability of the running vehicle subsystem, is a critical issue affecting the safe operation and the comfort riding of maglev railways. This study is devoted to stochastic optimization of the levitation control system considering the coupled vibration of maglev vehicle and bridge under the random guideway irregularity. To alleviate the computational burden in the stochastic optimization process, the explicit expressions of the critical responses of the maglev vehicle-bridge coupled systems and the sensitivities of critical responses with respect to the feedback gains of the proportional-integral-derivative (PID) levitation controller are respectively constructed in terms of the guideway irregularity by virtue of the dimension-reduced formulation capability of the explicit time-domain method (ETDM), enabling stochastic dynamic analysis and stochastic sensitivity analysis to be executed at high efficiency. The stochastic optimization problem is formulated as the minimization of the standard deviation of the air gap variation with the constraint on the standard deviation of the current variation, and the optimal values of the PID gains are searched by the gradient-based method of moving asymptotes (MMA), in which the statistical moments and moment sensitivities of the critical responses required in the optimization process are obtained by ETDM. A numerical example with a 2-degree-of-freedom maglev vehicle moving on a multi-span simply-supported bridge is used to demonstrate the efficacy of ETDM for the stochastic analysis of maglev vehicle-bridge coupled systems under guideway irregularity. An engineering example involving a 3-car maglev train traversing a multi-span simply-supported bridge is further presented to show the effectiveness of the ETDM-based approach for the stochastic optimization of large-scale engineering problems. The levitation stability of the maglev train under the action of random guideway irregularity is considerably improved with the use of optimal feedback gains.

Abnormal Driving Area Detection Using Multiple Vehicle Dynamic Model-Based Filter: Design and Experimental Validation

The main concern of remote control systems for autonomous ground vehicles (AGVs) is to perform the given mission according to the purpose of the operator. Although most remote systems are composed of a screen-based architecture, they are insufficient to transfer sufficient information to the remote operator. Therefore, in this paper, we present and experimentally validate an abnormal driving area detection system using an interacting multiple model (IMM) filter for the remote control system. In the proposed IMM filter, the unknown dynamic behavior of the vehicle, which changes according to changes in the driving environment, was lumped into a parameter change of the system model. As a result, we can obtain the probability of each model representing the reliability of each model, but an index can be used to infer the current status of the AGV and the driving environment. The index can help us detect both unusual behavior of the AGV such as skidding or sliding, and areas with low-friction road conditions that are not confirmed by images from the camera sensor. Thus, the remote operator can directly decide whether to continue operating or not. The proposed method is simple but useful and meaningful for the remote operator compared to the image-only method. The overall procedure of the proposed method was experimentally validated via a multi-purpose AGV on rough unpaved proving ground. Nine abnormal driving areas were discovered on the ground. In five of these areas, vehicles consistently exhibited abnormal driving behavior. The remaining four areas were confirmed to be affected by variables such as weather conditions and vehicle tire wear.

Adaptive sliding mode control with nonlinear MPC-based obstacle avoidance using LiDAR for an autonomous surface vehicle under disturbances

This manuscript proposes a path-following and collision avoidance guidance and control system for an autonomous surface vehicle operating in cluttered environments under perturbations. A nonlinear model predictive control strategy handles the state and control constraints for obtaining a guidance law. The approach considers the vessel sideslip angle to provide the desired heading angle, satisfying path-following and obstacle avoidance. Additionally, to solve the nonlinear optimization problem for obtaining the guidance law in real-time, the acados software package is used. Furthermore, in order to guarantee that the vehicle tracks the guidance in presence of model uncertainties and external disturbances, a robust adaptive sliding mode low-level controller is adopted. This controller is finite time convergent, and dynamically adapts its control gain to use only the necessary control inputs. Furthermore, a low-memory and computationally-light obstacle detection strategy employing a LiDAR sensor is presented and proven to run in real-time. Simulation and experimental tests conducted using a customized prototype validate the advantages of the proposed strategy and demonstrate the effectiveness of the path following while evading multiple obstacles in the presence of external disturbances and model discrepancies.

Data-driven neural network model for maximum speed profile based on driver risk

In the field of vehicle control systems, the primary objective is to ensure the safety of road users. The intricate dynamics of road vehicles necessitate a high level of precision. Vehicle safety encompasses a multitude of considerations, including vehicle trajectory, prevailing traffic conditions, road structure, and meteorological factors. This study employs an Artificial Neural Network (ANN) trained with human driver data using SCANeR Studio software to evaluate the risk for the driver. The risk has been defined as a five-level parameter, which depends on the potential danger of a situation, where speed and direction play a crucial role. The system incorporates a simulator, an ANN, and a display interface to present the surroundings and communicate important information to the driver. This research employs a simulated driving scenario comprising a multi-lane roundabout with vehicles travelling in different directions to simulate real-world challenges. Risk estimation is achieved through a Time Delay Neural Network (TDNN) trained with various information about the environment in relation to the driven vehicle. The research employs a Jackknife technique for overall evaluation and introduces an adaptive algorithm for speed limit setting. The findings demonstrate the stability, generality, and practical applicability of the ANN in enhancing road safety.