Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.

Connect & Drive:design and evaluation of cooperative adaptive cruise control for congestion reduction

Road throughput can be increased by driving at small inter-vehicle time gaps. The amplification of velocity disturbances in upstream direction, however, poses limitations to the minimum feasible time gap. This effect is covered by the notion of string stability. String-stable behavior is thus considered an essential requirement for the design of automatic distance control systems, which are needed to allow for safe driving at time gaps well below 1 s. Using wireless inter-vehicle communications to provide real-time information of the preceding vehicle, in addition to the information obtained by common Adaptive Cruise Control （ACC） sensors, appears to significantly decrease the feasible time gap, which is shown by practical experiments with a test fleet consisting of six passenger vehicles. The large-scale deployment of this system, known as Cooperative ACC （CACC）, however, poses challenges with respect to the reliability of the wireless communication system. A solution for this scalability problem can be found in decreasing the transmission power and/or beaconing rate, or adapting the communications protocol. Although the main CACC objective is to increase road throughput, the first commercial application of CACC is foreseen to be in truck platooning, since short distance following is expected to yield significant fuel savings in this case.

Predictive cruise control for heavy trucks based on slope information under cloud control system

With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control(PCC) system,lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method(RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also,compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity.Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.

An Improved Cooperative Adaptive Cruise Control(CACC) Algorithm Considering Invalid Communication

For the Cooperative Adaptive Cruise Control （CACC） Algorithm, existing research studies mainly focus on how inter-vehicle communication can be used to develop CACC controller, the influence of the communication delays and lags of the actuators to the string stability. However, whether the string stability can be guaranteed when inter-vehicle communication is invalid partially has hardly been considered. This paper presents an improved CACC algorithm based on the sliding mode control theory and analyses the range of CACC controller parameters to maintain string stability. A dynamic model of vehicle spacing deviation in a platoon is then established, and the string stability conditions under improved CACC are analyzed. Unlike the traditional CACC algorithms, the proposed algorithm can ensure the functionality of the CACC system even if inter-vehicle communication is partially invalid. Finally, this paper establishes a platoon of five vehicles to simulate the improved CACC algorithm in MATLAB/Simulink, and the simulation results demonstrate that the improved CACC algorithm can maintain the string stability of a CACC platoon through adjusting the controller parameters and enlarging the spacing to prevent accidents. With guaranteed string stability, the proposed CACC algorithm can prevent oscillation of vehicle spacing and reduce chain collision accidents under real-world circumstances. This research proposes an improved CACC algorithm, which can guarantee the string stability when inter-vehicle communication is invalid.

Target Vehicle Selection Algorithm for Adaptive Cruise Control Based on Lane-changing Intention of Preceding Vehicle

To improve the ride comfort and safety of a traditional adaptive cruise control(ACC)system when the preceding vehicle changes lanes,it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle.First,the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine,and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset.Second,according to the lane-changing intention and collision threat of the preceding vehicle,the target vehicle selection algorithm is studied under three different conditions:safe lane-changing,dangerous lane-changing,and lane-changing cancellation.Finally,the effectiveness of the proposed algorithm is verified in a co-simulation platform.The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver.In the case of a dangerous lane change,the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system;thus,it can effectively avoid collisions and improve the safety of the subject vehicle.

Longitudinal Control Strategy for Vehicle Adaptive Cruise Control Systems

A new longitudinal control strategy for vehicle adaptive cruise control （ACC） systems is presented. The running relationship between the ACC vehicle and the detected target vehicle is described by the relative velocity and the deviation between the actual headway distance and the prescribed safety distance. Based on this, two state space models are built and the linear quadratic optimal control theory is used to yield desired velocity for the ACC-equipped vehicle when with the target vehicle detected. By switching among four control modes, the desired velocity profile is designed to deal with different running situations. A velocity controller, which includes a PID controller for throttle openness and a neural network controller for brake application, is developed to achieve the desired velocity profile. The proposed control strategy is applied to a non-linear vehicle model in a simulation environment and is shown to provide the ACC vehicle comfortable ride and satisfying safety.

Effect of a Traffic Speed Based Cruise Control on an Electric Vehicle’s Performance and an Energy Consumption Model of an Electric Vehicle

This paper proposes a cruise control system(CCS)to improve an electric vehicle's range,which is a significant hurdle in market penetration of electric vehicles.A typical driver or a conventional adaptive cruise control(ACC)controls an electric vehicle(EV)such that it follows a lead vehicle or drives close to the speed limit.This driving behaviour may cause the EV to cruise significantly above the average traffic speed.It may later require the EV to slow down due to the traffic ripples,wasting a part of the EV's kinetic energy.In addition,the EV will also waste higher speed dependent dissipative energies,which are spent to overcome the aerodynamic drag force and rolling resistance.This paper proposes a CCS to address this issue.The proposed CCS controls an EV's speed such that it prevents the vehicle from speeding significantly above the average traffic speed.In addition,it maintains a safe inter-vehicular distance from the lead vehicle.The design and simulation analysis of the proposed CCS were in a MATLAB simulation environment.The simulation environment includes an energy consumption model of an EV,which was developed using data collected from an electric bus operation in London.In the simulation analysis,the proposed system reduced the EV's energy consumption by approximately 36.6%in urban drive cycles and 15.4%in motorway drive cycles.Finally,the experimental analysis using a Nissan e-NV200on two urban routes showed approximately 30.8%energy savings.

Robust output feedback cruise control for high-speed train movement with uncertain parameters

In this paper, the robust output feedback cruise control for high-speed train movement with uncertain parameters is investigated. The dynamic of a high-speed train is modeled by a cascade of cars connected by flexible couplers, which is subject to rolling mechanical resistance, aerodynamic drag and wind gust. Based on Lyapunov＇s stability theory, the sufficient condition for the existence of the robust output feedback cruise control law is given in terms of linear matrix inequalities（LMIs）, under which the high-speed train tracks the desired speed, the relative spring displacement between the two neighboring cars is stable at the equilibrium state, and meanwhile a small prescribed H∞ disturbance attenuation level is guaranteed. One numerical example is given to illustrate the effectiveness of the proposed methods.

Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks

Fuel consumption is one of the main concerns for heavy-duty trucks.Predictive cruise control(PCC)provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information.In this study,a real-time implementable PCC,which simultaneously optimizes engine torque and gear shifting,is proposed for heavy-duty trucks.To minimize fuel consumption,the problem of the PCC is formulated as a nonlinear model predictive control(MPC),in which the upcoming road elevation information is used.Finding the solution of the nonlinear MPC is time consuming;thus,a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method.Dynamic programming(DP)algorithm,as a global optimization algorithm,is used as a performance benchmark for the proposed solver.Simulation,hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller.The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution,with less than 1%deviation for testing roads.Moreover,the proposed co-optimization controller is implementable in a real-truck,and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9%without compromising the truck’s travel time.

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

In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System

With the application of mobile communication technology in the automotive industry,intelligent connected vehicles equipped with communication and sensing devices have been rapidly promoted.The road and traffic information perceived by intelligent vehicles has important potential application value,especially for improving the energy-saving and safe-driving of vehicles as well as the efficient operation of traffic.Therefore,a type of vehicle control technology called predictive cruise control(PCC)has become a hot research topic.It fully taps the perceived or predicted environmental information to carry out predictive cruise control of vehicles and improves the comprehensive performance of the vehicle-road system.Most existing reviews focus on the economical driving of vehicles,but few scholars have conducted a comprehensive survey of PCC from theory to the status quo.In this paper,the methods and advances of PCC technologies are reviewed comprehensively by investigating the global literature,and typical applications under a cloud control system(CCS)are proposed.Firstly,the methodology of PCC is generally introduced.Then according to typical scenarios,the PCC-related research is deeply surveyed,including freeway and urban traffic scenarios involving traditional vehicles,new energy vehicles,intelligent vehicles,and multi-vehicle platoons.Finally,the general architecture and three typical applications of the cloud control system(CCS)on PCC are briefly introduced,and the prospect and future trends of PCC are proposed.

A Progressive Deployment Strategy for Cooperative Adaptive Cruise Control to Improve Trafc Dynamics

Cooperative adaptive cruise control(CACC)vehicles are intelligent vehicles that use vehicular ad hoc networks(VANETs)to share trafc information in real time.Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration.Since reaching a high CACC market penetration level is not occurring in the near future,this study presents a progressive deployment approach that demonstrates to have a great potential of reducing trafc congestions at low CACC penetration levels.Using a previously developed microscopic trafc simulation model of a freeway with an on-ramp—created to induce perturbations and trigger stop-and-go trafc,the CACC system s efect on the trafc performance is studied.The results show signifcance and indicate the potential of CACC systems to improve trafc characteristics which can be used to reduce trafc congestion.The study shows that the impact of CACC is positive and not only limited to a high market penetration.By giving CACC vehicles priority access to high-occupancy vehicle(HOV)lanes,the highway capacity could be signifcantly improved with a CACC penetration as low as 20%.

Real-time optimization of energy consumption under adaptive cruise control for connected HEVs

This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle(HEV)that operates with adaptive cruise control(ACC).Real-time energy optimization is an essential ssue such that the HEV powertrain system is as efficient as possible.With connected vehice technique,ACC system shows considerable potential of high energy eficiency.Combining a classical ACC algorithm,a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon.The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle.The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.

CRUISE FUZZY CONTROL FOR AUTOMOBILE WITH CVT

To develop cruise control system of an automobile with the metal pushing V-belt type CVT,the dynamic model of automobile travelling longitudinally is established, and the fuzzy controller of control system is designed. Considering uncertainty system parameter and exterior resistance disturbances, the stability of controller is investigated by simulating. The results of its simulation show that the fuzzy controller designed has practicability.

Vehicle adaptive cruise control design with optimal switching between throttle and brake

For vehicle adaptive cruise control （ACC） systems, the switching performance between throttle and brake determines the driving comfort, fuel consumption and service lives of vehicle mechanical components. In this paper, an ACC algorithm with the optimal switching control between throttle and brake is designed in model predictive control （MPC） framework. By introducing the binary integer variables, the dynamics of throttle and brake are integrated in one model expression for the controller design. Then the ACC algorithm is designed to satisfy not only safe car following, but also the optimal switching between throttle and brake, which leads to an online mixed integer quadratic programming solved by the nested two-loop method. The simulation results show that the proposed ACC algorithm meets the requirements of safe car following, outperforms the traditional algorithms by performing smoother responses, reducing the switching times between throttle and brake, and therefore improves driving comfort and fuel efficiency significantly.

Cooperative Adaptive Cruise Control Using Delay-Based Spacing Policy:A Robust Adaptive Non-Singular Terminal Sliding Mode Approach

This study proposes two speed controllers based on a robust adaptive non-singular terminal sliding mode control approach for the cooperative adaptive cruise control problem in a connected and automated vehicular platoon.The delay-based spacing policy is adopted to guarantee that all vehicles in the platoon track the same target velocity profile at the same position while maintaining a predefined time gap.Factors such as nonlinear vehicle longitudinal dynamics,engine dynamics with time delay,undulating road profiles,parameter uncertainties,and external disturbances are considered in the system modeling and controller design.Different control objectives are assigned to the leading and following vehicles.Then,controllers consisting of a sliding mode controller with parameter adaptive laws based on the ego vehicle’s state deviation and linear coupled state errors,and a Smith predictor for time delay compensation are designed.Both inner stability and strong string stability are guaranteed in the case of nonlinear sliding manifolds.Finally,the effectiveness of the proposed controllers and the benefits of 44.73%shorter stabilization time,11.20%less speed overshoot,and virtually zero steady-state inner vehicle distance deviation are illustrated in a simulation study of a seven-vehicle platoon cooperative adaptive cruise control and comparison experiments with a coupled sliding mode control approach.

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

Study on the Driving Style Adaptive Vehicle Longitudinal Control Strategy

This paper presents a fusion control strategy of adaptive cruise control(ACC) and collision avoidance(CA),which takes into account a driver’s behavioral style. First, a questionnaire survey was performed to identify driver type, and the corresponding driving behavioral data were collected via driving simulator experiments, which served as the template data for the online identification of driver type. Then, the driveradaptive ACC/CA fusion control strategy was designed, and its effect was verified by virtual experiments. The results indicate that the proposed control strategy could achieve the fusion control of ACC and CA successfully and improve driver adaptability and comfort.

Stability analysis of traffic flow with extended CACC control models

To further investigate car-following behaviors in the cooperative adaptive cruise control（CACC） strategy,a comprehensive control system which can handle three traffic conditions to guarantee driving efficiency and safety is designed by using three CACC models.In this control system,some vital comprehensive information,such as multiple preceding cars’ speed differences and headway,variable safety distance（VSD） and time-delay effect on the traffic current and the jamming transition have been investigated via analytical or numerical methods.Local and string stability criterion for the velocity control（VC） model and gap control（GC） model are derived via linear stability theory.Numerical simulations are conducted to study the performance of the simulated traffic flow.The simulation results show that the VC model and GC model can improve driving efficiency and suppress traffic congestion.