Co-simulation Platform Research Articles

Nonlinear MPC-Based Acceleration Slip Regulation for Distributed Electric Vehicles

To address the problem in which wheel longitudinal slip rate directly affects the dynamics and handling stability of a vehicle under driving conditions, front and rear dual-motor four-wheel drive electric vehicles (4WD EVs) were selected as the research object in this study. An acceleration slip regulation (ASR) control strategy based on nonlinear model predictive control (NMPC) is proposed. First, the vehicle dynamics model and the Simulink/CarSim co-simulation platform were built. Second, an ASR controller with intervention and exit mechanisms was designed with the control objective of tracking reference speed or optimal slip rate. Then, considering the problem that the left and right wheels could not freely distribute torque under the condition of a split road surface, the motor output torque was determined in accordance with the wheel with the larger slip rate to enhance passibility. Finally, on the basis of the built Simulink/CarSim co-simulation platform, slip rate control simulation experiments were performed on a snow-covered road, a wet asphalt road, a docking road, and a split road. The designed controller can better track target slip rate and it exhibits better dynamic performance and stability than the method with PID control under different road conditions, especially under low speed and low adhesion road conditions, and its robustness can also meet the requirements.

Design and Dynamic Modelling of Knee Exoskeleton for Disabled People through ADAMS-Simulink Co-simulation

This paperis written to outline the progress and findings of an undergraduate research project aimed at the designingand modelling of an exoskeleton designof the knee jointfor rehabilitation and gait support.It focuses on the rehabilitation potential of the proposed exoskeleton design on patients with Hemiplegia and Monoplegia conditions with the objective of seeking a feasible, simple means of joint actuation to reduce the complexity of the design.Exoskeleton designsare able toprovide rehabilitation and improvethe overall quality of life of disabled people globally. However, a common issue found almost everywhere is the costly nature of the exoskeletons that are available in the market now as they are exclusivedevices, which makesthem inaccessible and impractical tothe general public, especially in developing countries. Initially,the paper focuseson the review of relevant literatureand previous researchand evaluatingthe designs that have been developed as of now.A thorough analysis of the work done by previous researchers and companies was conducted to gather data on the underlying engineering principles and techniques used for the exoskeleton development as well as any limitations or restrictions to the process. Conceptual designing of thepossible solutionswas developed using the understanding and knowledge gathered during the literature review. The selection of the best solution was based on the analysis of the pros and cons of all the solutions. The chosendesign, utilising a 4-bar mechanism, was then modelled using SolidWorks software to provide a clear visualisation of the system.The kinematic and dynamic analysis of the mechanism was evaluated to analyse the possibility of using the proposed exoskeleton design by replicating the model in ADAMS multibody software. The dynamic analysis was conducted by using the co-simulation platform between ADAMS and Simulink to enable the addition and control of feedback loops within the system.The results of the analysis show that the design can achieve the required motions of the human gait cycle, especially during the swing phase of the gait cycle. The analysis of the actuation torques and reaction forces on the human body showed that an acceptable torque range was possible during the swing phase of the gait cycle.KEYWORDS: Exoskeleton, Walking-Aid, Rehabilitation, Multibody dynamics

Torque Distribution Based on Dynamic Programming Algorithm for Four In-Wheel Motor Drive Electric Vehicle Considering Energy Efficiency Optimization

The improvement of both the stability and economy of the four in-wheel motor drive (4IWMD) electric vehicle under complex drive cycles is currently a difficult problem in this field. A torque distribution method with the comprehensive goals of optimal torque distribution and energy efficiency, considering economy through energy efficiency for the 4IWMD electric vehicle, is proposed in this paper. Each component of the 4IWMD electric vehicle is modelled. The dynamic programming (DP) control algorithm is utilized for torque distribution between the front and rear in-wheel motors to obtain optimal torque distribution and energy efficiency in the 4IWMD electric vehicle. The simulation is performed on a co-simulation platform with the software of AVL Cruise and MATLAB/Simulink, considering a straight road. Compared to the fuzzy logic control algorithm, the simulation results are very promising, as the energy consumption of the electric vehicle was reduced by 22.68%, 20.73% and 21.84% under the WLTC, NEDC and customized IM240 driving cycle conditions, respectively, with the proposed DP control algorithm. The hardware-in-the loop (HIL) experimental results also indicate that the effectiveness of the proposed DP algorithm is verified under the NEDC, WLTC and IM240 driving cycles, when a straight road is considered. The proposed DP control algorithm not only reduces the vehicle energy consumption and guarantees the optimization of torque distribution, but also increases the driving range of the vehicle.

Longitudinal Predictive Control for Vehicle-Following Collision Avoidance in Autonomous Driving Considering Distance and Acceleration Compensation.

In response to the widespread adoption of vehicle-following systems in autonomous applications, the demand for collision warning to enable safer functionalities is increasing. This study provides an approach for automated vehicle guidance to follow the preceding vehicles longitudinally and puts emphasis on the performance of collision avoidance. The safety distance model is established, which contains a distance compensation algorithm to deal with the special case on curved roads. By introducing the algorithm of velocity and distance prediction, the collision risks are detected and measured in real time. The objective function is established based on optimal control theory to solve the desired following acceleration. The control system designed with the method of proportion integration differentiation combines throttle percentage and brake pressure as outputs to compensate acceleration. In the Carsim and Simulink co-simulation platform, the control system for longitudinal collision avoidance is simulated and analysed for four typical working conditions: the preceding vehicle drives at a constant speed on straight and curved roads, while the preceding vehicle drives at various speeds on straight and curved roads. The results validate the feasibility and effectiveness of the proposed method, which can be used for the longitudinal control of vehicle-following active collision avoidance.

Adaptive co-simulation method and platform application of drive mechanism based on Fruit Fly Optimization Algorithm

The simulation of the control rod drive mechanism is a co-simulation process of multi-physics coupling. It has the problem of conflict between simulation accuracy and efficiency in the simulation process based on the centralized simulation idea. Therefore, a multidisciplinary co-simulation method and simulation platform with adaptive step size are proposed in this paper. According to the characteristics of the simulation nodes, the fruit fly optimization method is used to determine the adaptive step size of different simulation stages. A multi-disciplinary co-simulation platform is constructed based on the Isight which can integrate co-simulation tool. The co-simulation process of the control rod drive mechanism is completed with the method and platform. The results show that the simulation accuracy of the platform built in this paper is increased by 23%, and the simulation efficiency is increased by 64%, which verifies that the proposed method has good compatibility in terms of simulation accuracy and efficiency.

Linear quadratic regulator based on extended state observer–based active disturbance rejection control of autonomous vehicle path following control

This article studies the control problem of autonomous vehicle path following with coordination of active front steering and differential steering. A hierarchical control scheme including upper layer and lower layer is proposed. In the upper layer controller, a linear quadratic regulator based on extended state observer is proposed to generate the front-wheel steering angle and external yaw moment, where extended state observer is used to estimate and compensate for the system uncertainty and external disturbance which enhances the capability of the vehicle to suppress the disturbance. A brake force distribution scheme based on the theory of control allocation is proposed in the lower layer controller to optimize and coordinate each wheel brake force to achieve differential steering. Finally, the effectiveness of proposed control scheme is verified in a co-simulation platform based on CarSim/Simulink; it can be concluded that the linear quadratic regulator based on extended state observer scheme not only has a few parameters need to be tuned, but also has the capability of active disturbance rejection.

Torque allocation strategy based on economy and stability for electric vehicle considering controllability after motors failure

To reduce the energy consumption and improve the stability of distributed drive electric vehicles, a torque allocation strategy based on an economy and stability optimisation function (ESOF) and a fuzzy proportional-integral-derivative rule control (FPRC) strategy are proposed while considering motor efficiency, braking energy recovery and motor failure. First, the vehicle dynamics and motor equivalent models are established. Subsequently, a torque prediction model and fuzzy controller for the vehicle are designed to calculate the total desired torque and yaw moment, respectively. A torque optimisation function is established to minimise power losses in the electric motor and maximise braking energy recovery, and it is solved using an improved genetic algorithm. While satisfying vehicle driving constraints, the ESOF-based controller can effectively coordinate the operation of each motor in the high-efficiency range under driving and braking conditions. After one motor fault is detected, the ESOF-based controller is replaced with an FPRC-based controller to distribute the vehicle demand torque. A co-simulation platform integrating MATLAB/Simulink and CarSim is developed to verify the effectiveness of the proposed ESOF-based controller in the New European Driving Cycle (NEDC) and Federal Test Procedure 75 (FTP75) driving cycles. The effectiveness of the FPRC-based controller in step steering condition is verified using the co-simulation platform. The simulation results indicate that the vehicle economy and driving range of the ESOF-based controller improved compared with the results afforded by the typical torque distribution strategy based on the front–rear axle dynamic load ratio. The average efficiencies of the motors in the NEDC and FTP75 driving cycles increased by 2.94% and 2.4%, respectively. More importantly, the FPRC-based controller can more significantly improve the steering stability of a vehicle with motor failure compared with the ESOF-based controller.

A Novel Combined Decision and Control Scheme for Autonomous Vehicle in Structured Road Based on Adaptive Model Predictive Control

In the research of autonomous vehicles, most existing studies treat the decision/planning and control as two separate problems. This idea originates from robotics. But since there are essential differences between robot and autonomous vehicle, the structure in Robotics may not be suitable for autonomous vehicles. Considering decision/planning and control separately may affect the performance of autonomous vehicle under complex driving conditions. To fill in the research gap, this paper proposes a novel scheme which considers the local motion planning and control in a combined manner. Firstly, the local motion planning is transformed into the longitudinal control problem based on the proposed scenario adaptive MPC, by which the motion behavior (driving along the global path, car-following, lane-change) can be automatically decided. Then, the lateral MPC controller is designed to track the global path and conduct the local motion commands. To ensure the performance of the path tracking control and a smooth lane-change process simultaneously, an adaptive weight mechanism is introduced in the lateral controller. Comprehensive case studies including both straight and curve road are conducted based on Carsim-Simulink co-simulation platform. The results show that the proposed algorithm can not only ensure the vehicle safety in complex driving conditions, but also ensure that the vehicle can drive at its desired velocity as much as possible by intelligently judging the most proper motion behaviors.

Optimum allocation of multiple type and number of DG units based on IEEE 123-bus unbalanced multi-phase power distribution system

The increasing demand for clean energy has resulted in the growth of renewable energy-based distributed generation (DG) penetration in the power distribution networks (PDN). The crucial point here is to find the optimum allocation of DGs to minimize the power loss and enhance the system performance overall. In this study, the Equilibrium Optimizer (EO) algorithm-based method is proposed to determine the location, size, connection type, and power factor of three different types of multiple DGs based on a three-phase 123-bus unbalanced PDN. The complex 123-bus UPDN has been very little studied to date, and most existing research works on the DG allocation problem have been conducted using a co-simulation platform. In this work, an unbalanced three-phase backward forward load flow (UBFLF) algorithm is written and executed under the MATLAB environment. This approach has decreased the complexity of co-simulation and provided advantages like optimizing the power factor value and the connection type (Delta/Wye) of DGs easily. Six more recent and well-established optimization algorithms such as PSO, JFO, BO, SMA, FDA, and GBO are also applied under the same test conditions to solve the Type-III DGs allocation problem. The results obtained are compared with the proposed method to show the effectiveness of the method in terms of elapsed time, statistical variables (such as standard deviation, median, variance and so on), converge speed, and best/worst case active power loss values. The efficacy of the presented method is also validated by comparing the results obtained using three distinct platforms as MATLAB/Simulink, IEEE-PES data, and OpenDSS. The detailed and comprehensive analyses show that the proposed approach demonstrates the lowest power loss, more enhancement in voltage profile, and the superiority of the EO algorithm in the application of a 123-bus unbalanced power distribution network.

Motion Control of Autonomous Vehicles Based on Offset Free Model Predictive Control Methods

Abstract Model predictive control (MPC) is the mainstream method in the motion control of autonomous vehicles. However, due to the complex and changeable driving environment, the perturbation of vehicle parameters will cause the steady-state error problem, which will lead to the degradation of controller performance. In this paper, the offset-free MPC control method is proposed to solve the steady-state error problem systematically. The core idea of this method is to model the model mismatch, control input offset, and external disturbances as disturbance terms, then use filters to observe these disturbances and finally eliminate the influence of these disturbances on the steady-state error in the MPC solution stage. This paper uses the Kalman filter as an observer, which is integrated into our latest designed MPC solver. Based on state-of-the-art sparse quadratic programming (QP) solver operator splitting solver for quadratic programs (OSQP), an offset free model predictive control (OF-MPC) framework based on disturbance observation and MPC is formed. The proposed OF-MPC solver can efficiently deal with common model mismatch problems such as tire stiffness mismatch, steering angle offset, lateral slope disturbance, and so on. This framework is very efficient and completes all calculations in less than 7 ms when the horizon length is 50. The efficiency and robustness of the algorithm are verified on our newly designed robot operating system (ROS)-Unreal4-carsim real-time cosimulation platform and real vehicle experiments.

Cyber-attack research for integrated energy systems by the correlated matrix based object-oriented modeling method

The rise of an integrated energy system requires the integration of multiple sources of energy to be embraced and transited over the power grid; that means the information and communication systems of traditional power systems will be extended, and their complexity will be increased. As the information and communication systems play a more important role in the infrastructure of the power system, a cyber-attack on them may have an impact on the power system and cause a serious threat to the integrated energy system. To analyze the threat to the complex and integrated system, some researchers provide some modeling methods to study the impact of cyber-attacks on the information and communication systems, such as the Attack Tree Model, Attack Graph Model, Petri Net Model for cyber-attack, Attack Description Language Model, State Transition Graph Model, etc. However, these modeling methods have some shortcomings. For example, they cannot describe the systematical cyber-attack and show the secure status during the cyber-attack; it is hard for them to analyze the larger and more complicated information and communication systems. To overcome the shortages, in this paper, a correlated matrix-based object-oriented model is proposed for cyber-attack modeling. With this model, the relationship between the attacker and victim can be directly built; the cyber-attack path and packets from the attacker can be tracked; and the status of the nodes and links can be shown during a cyber-attack. There are two steps to build the cyber-attack model. Firstly, the procedure and approach of the cyber-attack will be modeled by an object-oriented method; then, a correlated matrix model will be built for network topology, attack path, and attack procedure. By combining these two models, the whole cyber-attack model is created. Finally, to demonstrate the modeling method and its benefits, we use the MITM (Man-in-the-middle Attack) attack on measurement data of an AVC (Automation Voltage Control) system as examples, and build a hardware-in-the-loop (HIL) co-simulation platform to verify the model.

Secure Sampled-Data Observer-Based Control for Wind Turbine Oscillation Under Cyber Attacks

Inspired by the recent surge of interest in security analysis of renewable energies, this article proposes a cyber resilient control framework for large-scale wind parks (WPs) against denial of service (DoS) and deception attacks. A detailed cyber-physical model for a doubly-fed induction generator (DFIG)-based WP is first developed. It is shown that the WP is subject to a stability issue known as the sub-synchronous interaction (SSI). Additionally, two cyber threats in the forms of DoS and deception attack attempt to destabilize the WP from the subsynchronous perspective. To stabilize the WP under the SSI phenomenon and these cyber attacks, an observer-based fuzzy control scheme is proposed which requires only periodic samples of the output. Using a looped-Lyapunov functional (LLF) method for stability analysis, the control and observer gains are computed from the solution of some linear matrix inequality (LMI) conditions. The proposed design framework allows including the desired level of cyber resilience to DoS and deception attacks for computing proper control and observer gains. The effectiveness of the proposed controller against the considered threats is demonstrated via electromagnetic transient (EMT) simulations performed using the EMTP-RV software and a co-simulation platform.

Error allocation model and coordination control for stringer–skin T-shape structure welding robot with multiple manipulators

A new kind of 18 degrees of freedom gantry robot with multiple manipulators could be used to complete the double-sided laser beam welding of stringer–skin T-shape structure for aircraft skin. The coordination control combined with error allocation model analysis is proposed to improve the stability and accuracy of the robot in this article. Firstly, the error model of the stringer–skin T-shape structure welding robot end effectors is analyzed. The score of the robot end effectors position error caused by each joint is obtained. Secondly, according to the path-points transformation matrix of the local coordinate system relative to the base coordinate system, the desired position of stringer–skin T-shape structure welding robot end effectors is calculated to be the input of the control model aiming at the dual-beam laser welding process. Then, the double closed-loop control strategy is proposed for stringer–skin T-shape structure welding robot coordinated motion on the basis of the end error analysis results and proportional-integralderivative (PID) method. Finally, ADAMS and MATLAB are used to establish a co-simulation platform to verify the effectiveness of the proposed coordination control strategy.

A Data-Driven Algorithm for Enabling Delay Tolerance in Resilient Microgrid Controls Using Dynamic Mode Decomposition

The increased implementation of smart grid technologies in the power distribution grid presents unique opportunities that enable resiliency, but also brings challenges motivating needs for novel solutions and mitigation techniques. The bi-directional power and data flow allow for the grid to operate with increased resiliency, which is the ability to avoid discontinuity of service to end-use loads during extreme events. However, in applications where control of the distribution grid or microgrid relies on communication networks, the degradation of communication systems in the form of loss or high latency can cause maloperation and result in loss of end-use loads. This paper presents a novel framework to enable delay tolerance of centralized microgrid control schemes to mitigate communication system latency impacts and guarantee successful control action. We demonstrate the delay tolerance on a control scheme that operates a battery energy storage system (BESS) to offset the sudden loss of generation and maintain system frequency. During periods of severely degraded communication system performance, the proposed delay-tolerant algorithm compensates for the latency by utilizing a data-driven model generated at the device level using dynamic mode decomposition (DMD) to determine the performance of the communications. The DMD technique predicts the system’s frequency using device-level terminal measurements and provides updated control signals. The HELICS cosimulation platform evaluates the cyber-physical interaction of the power system model in GridLAB-D, the centralized control agent in Python, and the discrete network model in NS-3. The framework is tested and validated on the IEEE-123 node system modified to represent a networked remote microgrid model, and the results show an improvement in the dynamic performance of the control scheme when subject to communication delays.

Behavior Decision Model With Situation Assessment for Intelligent Vehicles Based on Vehicle-to-Everything Information

Under the application of cooperative vehicle infrastructure technology, vehicles can drive more safely and efficiently in urban traffic environments. This paper presents a behavior decision model composed of a situation assessment module and decision process module to make a decision on lane selection and velocity control. Different common traffic elements are considered, such as surrounding obstacles, traffic lights, lane lines, and road velocity limits, to ensure the model outputs safe and legal behaviors. A green-wave passage strategy is also proposed to allow vehicles to pass through traffic intersections at a smoother velocity and to improve driving efficiency. To verify the correctness and rationality of the model, a Prescan/Simulink co-simulation platform including multiple common urban driving scenarios is established. The results show that the model can output correct behaviors with safety and legitimacy in different scenarios and realize vehicles meeting at intersections, vehicle-following, queuing at intersections with traffic lights, lane-changing for obstacle avoidance, and so forth. Compared with the general decision model without the green-wave passage strategy, the driving time is reduced by an average of 8.53%, and the standard deviation of the driving velocity is reduced by an average of 65.42%, which means that the model can reduce the variation of velocity and improve driving efficiency. The model has a certain practical application value.

A feasibility study of using Manufacturing Message Specification report gateway model for IEC61850 inter-substation type-1 messaging over Wide Area Network

This paper presents a feasibility study of using TCP-based MMS report gateway model for transmitting type-1 messages over WAN. In IEC61850-90-5:2012, the Standard suggests to use UDP-based R-GOOSE for exchanging the type-1 messages, i.e. trip signals and other fast messages defined in the Standard, over WAN. However, due to the cyclic retransmission inherited from GOOSE, R-GOOSE messages are repeatedly sent to the network and thus pose unnecessary traffic to the WAN. With the proposed model, unnecessary re-transmissions can be eliminated. It can preserve bandwidth for more IEDs being imposed on the existing smart grid. To achieve this task, two IEC61850-based gateways for translating GOOSE Messages to and from the MMS report over WAN have been modeled and implemented. The authors then make use of an HLA-complied co-simulation platform to simulate a distance protection scenario through the gateways to investigate the communication latency with different links speed and utilization rate of WAN. Simulated results show that the proposed MMS report gateway model works well for different WAN link speed from DS3 to 1000BaseX. Simulation results further indicate that introducing QoS control to the WAN routers is an effective method to warrant the latency for satisfying the IEC61850 standard requirement. The result from this feasibility study reveals that the TCP-based MMS report gateway model indeed can be used as an extended feature for transmitting type-1 messages between substations over WAN. Furthermore, this method is suitable for connecting more IEC61850-based DER to the existing smart grid involving WAN.

Co-simulation platform for interconnected power systems and communication networks based on PSS/E and OMNeT++

This paper proposes a co-simulator that combines OMNeT++ for communication systems with PSS/E for the electrical transmission network. The co-simulator applies an event-driven synchronization method that minimizes errors due to delays in the synchronization between both simulators. The synchronization method pauses the simulation of the power system at each communication event, while a supervisory module in PSS/E returns control to the event simulator if any condition from a pre-specified set is met. The proposed co-simulator is demonstrated on a protection system based on peer-to-peer communication and used to evaluate the effect of communication latency times on an online state estimator.

A Safe and Efficient Lane Change Decision-Making Strategy of Autonomous Driving Based on Deep Reinforcement Learning

As an indispensable branch of machine learning (ML), reinforcement learning (RL) plays a prominent role in the decision-making process of autonomous driving (AD), which enables autonomous vehicles (AVs) to learn an optimal driving strategy through continuous interaction with the environment. This paper proposes a deep reinforcement learning (DRL)-based motion planning strategy for AD tasks in the highway scenarios where an AV merges into two-lane road traffic flow and realizes the lane changing (LC) maneuvers. We integrate the DRL model into the AD system relying on the end-to-end learning method. An improved DRL algorithm based on deep deterministic policy gradient (DDPG) is developed with well-defined reward functions. In particular, safety rules (SR), safety prediction (SP) module and trauma memory (TM) as well as the dynamic potential-based reward shaping (DPBRS) function are adopted to further enhance safety and accelerate learning of the LC behavior. For validation, the proposed DSSTD algorithm is trained and tested on the dual-computer co-simulation platform. The comparative experimental results show that our proposal outperforms other benchmark algorithms in both driving safety and efficiency.

Finite element method based co-simulation platform for the numerical analysis of motor drive system

PurposeThis study aims to develop a finite element method based co-simulation platform for the numerical analysis of motor drive system. With the rising requirement of industry, the comprehensive design of motor drive systems has attracted increasing attentions. An accurate model, which considers the coupling between motor and its drive system, is vital for the analysis and design of motor drive system.Design/methodology/approachConsidering the coupling relationship between motor and its drive system, a flexible and extensible co-simulation platform of motor drive system is developed with the C++ language and finite element machine model to carry out the comprehensive analysis of motor drive system. The control system simulation program developed with C++ language adopts the same discrete form as the single-chip microcomputer and can simulate the interrupt mechanism, making the simulation closer to the actual control system. With the finite element analysis results of current step, the winding input voltage of next step is calculated by the executable program of control system and is fed into the finite element analysis, forming the two-way coupling analysis of drive system.FindingsPreliminary studies, such as calculation of machine core losses fed by inverters, and control parameters optimization, are conducted with this platform, which shows the flexibility and expansibility of this platform.Originality/valueThe power inverter circuit along with the controller is modeled using the C++ language, and embedded into the finite element machine model to achieve more realistic motor drive system simulation and complex functions.

An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation

The rapid increase in the number of vehicles has brought significant challenges to energy conservation and environmental sustainability. To solve these problems, various frameworks and models based on intelligent connected vehicles (ICVs) have been identified for road capacity improvement and fuel consumption reduction. In this paper, an eco-driving controller with ICVs was first proposed by combining vehicular dynamics with wireless communication technologies, where the nodes that can implement perception and control in a simulated complex traffic environment have been deployed. Then, the information of the surrounding environment, including the preceding vehicles, was obtained through a wireless communication module based on the technology of vehicle to everything (V2X). Besides, the advanced model predictive control (MPC) strategy was integrated into the ICV controller with the objectives of minimizing the driving spacing and improving environmental sustainability. Finally, a co-simulation platform for ICVs based on a robot operating system (ROS) and PreScan software was constructed, and the dynamic characteristics of the controller were verified in three aspects, including car-following behaviors, fuel efficiency improvement, and carbon dioxide emission reduction. The proposed controller showed that it can reduce fuel consumption by 3.71% and reduce carbon dioxide emissions by 3.42%, in the scenarios of a preceding vehicle with constant velocity, and by 6.77% and 7.91%, respectively, in a preceding vehicle with variable velocity scenario. The demonstrated experimental results show that the proposed controller can effectively reduce fuel consumption and emissions during car-following.