Vehicle Cruise Control System Research Articles

Study on multi-objective adaptive cruise control of intelligent vehicle based on multi-mode switching

Aiming at the problems of slow response, poor comfort and high fuel consumption of the existing adaptive cruise control system under complex operating conditions, a multi-objective adaptive cruise control method of intelligent vehicles based on multi-mode switching is proposed. Firstly, the overall scheme of the adaptive cruise control system is designed by using the hierarchical control structure, and the multi-mode switching strategy is designed by using the fuzzy control theory to realize the division and switching of the working modes during vehicle cruise. Secondly, based on the variable spacing strategy and longitudinal kinematics model, the multi-objective of safety, following performance, fuel economy and comfort is analyzed and carried out, and a quadratic multi-objective optimization function based on multi constraints is obtained. Then, the model predictive control algorithm based on particle swarm optimization (PSO) is used to transform multi-objective function into a standard form with predictive control increment as the optimization variable, and the optimal control rate is solved. Finally, the simulation experiment is carried out by setting three complex working conditions: the preceding vehicle uniform speed change, the preceding vehicle rapid speed change and the adjacent vehicle cut in. The results show that the proposed method can meet the requirements of safety, comfort and fuel economy, and can improve the adaptability and friendliness of intelligent vehicle cruise control system.

MNEARO: A meta swarm intelligence optimization algorithm for engineering applications

Artificial rabbits (AR) optimization is a newly proposed swarm intelligence algorithm. The idea of AR optimization (ARO) is inspired by the nature behaviors of the AR including the detour foraging, the random hiding and the energy shrink. Although ARO has better performance than many classical natural heuristic algorithms, it still has some disadvantages such as lacking the diversity of the population and easy to trap in the local optimal. To mitigate the influence of the above defects, this paper respectively introduces the mutation strategy, the prey identification strategy and the elite opposition-based learning strategy into the original algorithm, and proposes a new meta swarm intelligence optimization algorithm termed MNEARO. Firstly, the mutation strategy is introduced into the two natural behaviors of ARO to expand its exploration range in the solution space and increase the probability of finding the global optimal. Secondly, replacing the captured individuals with the predators that take better action trajectories to achieve the population evolution. Another candidate population is generated by the prey identification strategy, thus improving the population diversity and dynamics of ARO. Finally, the elite opposition-based learning strategy improves the exploitation ability of ARO and the overall quality of the population by selecting some individuals with the better fitness values as the elites and using the greedy selection before the end of each iteration. A series of performance tests are conducted on the CEC2020 test set in four dimensions and the CEC2022 test set in the highest dimension respectively to verify that the proposed MNEARO has better convergence and robustness. In addition, the practicality of MNEARO is further highlighted through five mechanical optimization designs, two truss topology optimization designs and the vehicle cruise control system. The experimental results show that the proposed MNEARO has a strong competitiveness in solving complex optimization problems with different dimensions.

Enhancing time-domain performance of vehicle cruise control system by using a multi-strategy improved RUN optimizer

This paper addresses the pressing concern of traffic safety by focusing on the optimization of vehicle cruise control systems. While traditional control techniques have been widely employed, their design procedures can be time-consuming and suboptimal. To overcome these limitations, metaheuristic algorithms have been introduced as promising solutions for complex optimization problems. In this study, an improved Runge Kutta optimizer (IRUN) is developed and applied to enhance the control performance of a real PID plus second-order derivative (RPIDD2) controller for vehicle cruise control systems. The IRUN optimizer incorporates advanced strategies such as quadratic interpolation, Laplacian segment mutation, Levy flight, and information-sharing-based local search mechanisms. By integrating these strategies, the IRUN algorithm demonstrates enhanced optimization capabilities, making it well-suited for tuning the controller. The proposed approach utilizes a master–slave system, where the ideal reference model sets the desired response and the RPIDD2 controller adjusts its parameters accordingly. The integral of the square error is employed as the objective function to evaluate the control system's performance. Statistical analyses, convergence analyses, and stability evaluations and robustness analysis are performed to demonstrate the effectiveness of the IRUN-based RPIDD2 controller. Comparative studies are conducted against established approaches using PID, fractional-order PID (FOPID), and RPIDD2 controllers, showcasing the superiority and effectiveness of the proposed approach. Overall, this paper presents a comprehensive study on enhancing the time-domain performance and stability of vehicle cruise control systems, providing significant improvements in control accuracy and efficiency. The subsequent sections delve into the proposed approach, experimental setup, and obtained results, further emphasizing the significance and potential impact of this research.

Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design

This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter (P{I}^{lambda }DN{D}^{2}{N}^{2}) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based P{I}^{lambda }DN{D}^{2}{N}^{2} controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a P{I}^{lambda }DN{D}^{2}{N}^{2} controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.

Security Time-Varying Formation Control for Multi-Agent Systems Under Denial-of-Service Attacks via Unknown Input Observer

The paper investigates the security time-varying formation control problem when the communication network of multi-agent systems (MASs) suffers from Denial-of-Service (DoS) attacks based on switched controller, state observers and unknown input reconstruction (UIR). To begin with, for the follower agents with unknown inputs, through an interval observer, the estimations of unknown input (UI) and system state can be obtained asymptotically and simultaneously by a Luenberger-like state observer (LLSO) combining with a UIR. After this, a distributed control protocol (DCP) on formation tracking is proposed based on the UIR and the state estimation, which can compensate the UI successfully. Moreover, the time space is divided into three parts utilizing the transmission interruption caused by DoS attacks, and the gain of the controller is designed corresponding to three different DoS attack duration, respectively. With the help of some concept in switched system, it is demonstrated that the given formation pattern can be reached under the proposed DCP. Finally, to verify the effectiveness of the proposed methods, a vehicle cruise control system used as the simulation example is given.

A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

Enhanced reptile search algorithm with Lévy flight for vehicle cruise control system design

Enhanced reptile search algorithm with Lévy flight for vehicle cruise control system design

Design and implementation of the whole process collaborative system for software development

With the increasingly urgent and arduous task of software development, software engineering development tools that can efficiently respond to the continuous changes in software requirements, rationally plan the software development process, and control the development cost will be widely used in software engineering. In response to actual needs, the supporting tool is designed to meet the system requirements, software requirements and software design and development. First, we use visualization modeling optimization technology of structured analysis and design method to build demand analysis models and system design models; Secondly, the dynamic verification technology of data model logical consistency is adopted to verify the logical consistency of data model in evolution; finally, the verification of the system model is achieved through hierarchical flow diagrams, fully expanded flow diagrams and statistical views. The vehicle cruise control system is selected for application verification, and the tool meets the requirements of software development and improves the software quality.

On-line learning of a fuzzy controller for a precise vehicle cruise control system

Usually, vehicle applications need to use artificial intelligence techniques to implement control strategies able to deal with the noise in the signals provided by sensors, or with the impossibility of having full knowledge of the dynamics of a vehicle (engine state, wheel pressure, or occupants’ weight).This work presents a cruise control system which is able to manage the pedals of a vehicle at low speeds. In this context, small changes in the vehicle or road conditions can occur unpredictably. To solve this problem, a method is proposed to allow the on-line evolution of a zero-order TSK fuzzy controller to adapt its behaviour to uncertain road or vehicle dynamics.Starting from a very simple or even empty configuration, the consequents of the rules are adapted in real time, while the membership functions used to codify the input variables are modified after a certain period of time. Extensive experimentation in both simulated and real vehicles showed the method to be both fast and precise, even when compared with a human driver.

Variable Universe Fuzzy Control of Vehicle Cruise Control System Based on FSM Theory

A vehicle Cruise Control System(CCS) was designed based on Finite State Machine(FSM) theory and variable universe fuzzy logical theory. The system can control throttle angle and brake pressure coordinately by modeling vehicle powertrain. The simulation results show that vehicle under the proposed system controlled can be drived along the state switch curve, so as to make the vehicle follow the desired acceleration command accurately. And this control system provides satisfactory tracking performances and suitability insinuations such as traction, vehicle tracking and braking.