Model Adaptive Control Research Articles

A distributed control wireless system for environmental humidity determination based on adaptive controller model

Environmental conditioning is a major research application area in the concept of control theory. The parameter of interest is mostly measured and evaluated against a reference in determining the initialisation threshold for the designed systems. This paper has implemented a system for controlling the heat parameters of a dwelled environment based on the integration of Global System for Mobile Communication (GSM) features into a Wireless Sensor Network (WSN) system. A model is developed to describe the temperature evolution in the constituent electronic units of the thermally active system, as a function for determining the environmental humiture impact. The generated heat and the forced cooled air by convective flow across the neighbouring devices, and consequently, the networked wireless nodes within the defined space are also modelled and analysed for adaptive control implementation. Both linear and non-linear terms are presented in the discrete set of equations representing the mathematically modelled system. Two scenarios were simulated and analysed: centralized model adaptive control (CMAC) and decentralized model adaptive control (DMAC). The evaluation of the control methods is ascertained by comparing with classic P/PI and on/off controllers, using a very low inoccupation temperature reference while increasing the occupation period in the adaptive control prediction horizon. The decentralized model adaptive controller provides improved performance in the reduction of the consumption index by about 7.2%, while the distributed and centralized schemes show an improvement in the thermal comfort with about 39.2% and reduction in the consumed energy by about 12.2%. A suitable conclusion is being made on the optimal placement of the nodes to ensure maximum convective cooling of the space, derived from the simulated results of the models and the adjourning tempering algorithm. The uniqueness and existence of the solutions are established through the convergence of the steady-state coefficients. The research has introduced a novel designer-friendly relative humidity-enabled adaptive control with significant range extension for acceptable indoor conditions in the design of low-energy naturally-conditioned facilities in sub-Saharan Africa.

Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

Optimal Model Predictive Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

Research of adaptive nonlinear model by integrated irrigation of water and soils in greenhouse for environment and ecological balance

In view of the high control cost and water consumption of the current commercial greenhouse automatic irrigation system, this paper presents an adaptive nonlinear model identification control algorithm based on the integrated irrigation of water, fertilizer and medicine in the greenhouse, and establishes an adaptive nonlinear model identification control model based on the integrated irrigation of water, fertilizer and medicine in the greenhouse: the adaptive blood equalizer theory is analyzed Based on the known theory, the LMS adaptive equalizer is simulated. The simulation results show that the denoising effect of the adaptive equalizer based on LMS algorithm is very significant, and the algorithm has small amount of calculation and is easy to be implemented in engineering. It has strong applicability. Under the premise of achieving effective greenhouse irrigation effect, the control system reduces the water consumption by 30%, and reduces the opening time of the irrigation system and the control cost.

Digital twin modeling for loaded contact pattern-based grinding of spiral bevel gears

To distinguish with the conventional tooth flank grinding only considering geometric accuracy, an innovative digital twin modeling is proposed for loaded contact pattern based grinding of spiral bevel gears. Where, data-driven grinding simulation, sensitivity analysis strategy, adaptive decision and control are developed. Focusing on loaded contact pattern optimization, numerical loaded tooth contact analysis (NLTCA) considering noncentrosymmetric problem and tooth flank roughness is developed for data-driven relationship establishment. Then, an adaptive data-driven tooth flank grinding decision and control model is established. Where, the universal motion concept (UMC) machine settings is selected as the optimal design variable. It is actually an infinite approximation to the target tooth flank in form of an adaptive control system. Moreover, with point-to-point material removal distribution, the different optimization strategies are proposed for accurate tooth flank grinding. In particular, the overcutting problem on the tooth flank grinding programming is investigated. Finally, Levenberg-Marquardt method is applied to solve the established nonlinear lease square model for the accurate machine tool settings having modification variations. Thus, this accurate data-driven digital twin modeling can achieve loaded contact pattern-based grinding. The provided numerical and test instances can verify the proposed digital twin modeling.

Personalized vital signs control based on continuous action-space reinforcement learning with supervised experience

Vital signs reflect patients’ current health status. Different patients are suitable for distinct references of vital signs due to the disease type and individual physique. Personalized Vital Signs Control (PVSC) helps clinicians with the recommendation of the optimal references for real-time treatment. However, a data-driven approach needs to overcome the unclear ground truth problem, requires complex feature integration and continuous value space, and is expected to ensure the safety of fragile patients. But none of the existing approaches can overcome all of these challenges simultaneously. This work emphasizes PVSC as a sequence decision-making problem and applies multiple reinforcement learning methods to it. We propose a novel adaptive medical control model. The model combines the deep deterministic policy gradient reinforcement learning algorithm, supervised experience knowledge, and recurrent neural network module, named PVSC-RL. We test the model on a real medical database, MIMIC-III, with 15,232 sepsis and 13,608 heart failure records. Experimental results show that using the policy of PVSC-RL, the survival rate of sepsis and heart failure patients by blood glucose control and blood pressure control can be increased by 3.45%, 15.21%, 4.19%, 12.13%. Meanwhile, the safety rate is also be increased by 3.09%, 1.02%, 3.54%, 4.04%.

FEATURES OF ADAPTIVE BRAKE CONTROL OF THE SECONDARY BRAKE SYSTEM OF A MULTI-AXLE VEHICLE

Problem. A malfunction of the service braking system of a wheeled vehicle (CTS) significantly affects road safety, especially when operating multi-axle vehicles with large masses. One of the ways to increase the level of road safety of multi-axle vehicles, when braking them using a spare (emergency) braking system, is the introduction of automated adaptive braking systems into the design of the brake drive of vehicles. The definition of the limits of the use of the adaptive braking system on vehicles with many axles is almost not disclosed in the scientific and technical literature, therefore, the issue of using such a system on vehicles with a large number of axles requires additional research. Purpose. The purpose of this work is to develop a simulation model for adaptive control of the braking process of a multi-axle vehicle using a spare (emergency) braking system, taking into account the simulation of the dynamics of the drive and the variability of the adhesion properties between the tire of the vehicle wheel and the road surface. Methodology. To achieve this goal, it is necessary to develop a simulation model of the brake drive in an adaptive mode, implement a model of the interaction of the tire with the road surface, and implement a model of the braking dynamics of a multi-axle vehicle in the event of a malfunction of its service brake system. Originality. The proposed key criterion (Kr) for changing the throttle section in electro-pneumatic pressure modulators, which provide adaptive air inlet or outlet from the corresponding brake chambers of the drive, during simulation, made it possible to simulate the operation of the drive circuits in the adaptive mode. It has been established that, depending on the potential for the realization of the adhesion between the tires of automobile wheels and the road surface, the pressure in the electro-pneumatic brake drive with its adaptive regulation can be increased by no more than 0.04 MPa.

A novel adaptive Runge–Kutta controller for nonlinear dynamical systems

This paper introduces a new Runge–Kutta (RK) integration-based adaptive controller by considering control law as an ODE for nonlinear MIMO systems. It is aimed to derive a novel adaptive controller by regarding the control law as an ODE with limited information about control law structure. Adaptive parameters are adjusted via an RK predictive system model where Levenberg–Marquardt (LM) technique is deployed. The adjustment mechanism enables to utilize RK both in adaptive controller and system model. The performance evaluation has been delved into on Van de Vusse (VdV) system for diverse situations, and reasonable results have been acquired for introduced adaptation mechanism.

Prediction and control for local bearing contact-based collaborative grinding of non-orthogonal aerospace spiral bevel gears

It is very different with the recent tooth flank grinding, an innovative collaborative optimization considering both geometric accuracy and local bearing contact evaluations is proposed for non-orthogonal aerospace spiral bevel gears. In particular, its data-driven prediction and adaptive control is developed. Firstly, tooth flank grinding using double helical method is simulated to establish mathematical model of the whole tooth flank including work flank and root fillet. Then, prediction of local bearing contact evaluations including geometric topography, loaded contact pressure and loaded contact deformation is performed by correlating with numerical loaded tooth contact analysis (NLTCA). Where, loaded contact pressure and its distribution considering time-varying meshing characteristics are developed, respectively. Moreover, an adaptive collaborative control model is established by extending the conventional machine tool settings modification only considering geometric accuracy to case that collaborative optimization. In the normal direction, loaded contact deformation of material removal point is predicated in the geometric accuracy control. In tooth flank tangential plane, the material removal points are constrained within the prescribed boundary. Finally, the sensitivity analysis strategy is used to select the optimal design variables, and control mode is solved for accurate machine tool settings. The given numerical instances can verify the proposed method.

A novel distributed cooperative approach for mixed platoon consisting of connected and automated vehicles and human-driven vehicles

Connected and automated vehicles (CAVs) have the advantages of improving road safety, enhancing road utilization and reducing exhaust emissions. The CAVs will coexist with human-driven vehicles (HDVs) on the road for a longer period of time. This paper proposes a novel model to mimic the mixed platoon on basic freeway sections. An adaptive control model based on multi-agent system is proposed to model CAVs using vehicle-to-vehicle (V2V) communication, and a similar form car-following model is utilized to describe HDVs. The adaptive consensus control and a more flexible, effective and practical spacing policy are embedded into the CAVs control model. Then a unified platoon model is established for the mixed platoon and a distributed cooperative approach is presented. We derive the stability conditions of the mixed platoon system. The performance of the mixed platoon model is numerically tested and verified under different scenarios such as different penetration rates of HDVs, different vehicle orders and different reaction-time delays of drivers.

Gimbal control of inertially stabilized platform for airborne remote sensing system based on adaptive RBFNN feedback model

The attitude stabilization precision of inertially stabilized platform (ISP) directly affect the resolution of remote sensing image by stabilizing the airborne remote sensing system and holding the line of sight. Therefore, an adaptive control model based on radial basis function neural network (RBFNN) is designed to improve the attitude stabilization precision of ISP gimbal in this article. The adaptive control law and the RBFNN weight adaptive law are designed based on the Lyapunov stability theorem, and the inputs of state feedback control and disturbance observer are combined into the control input. Both simulation and experimental results indicate that the adaptive control based on RBFNN can improve the attitude stabilization precision of ISP gimbal compared to the state feedback control.

On the adequacy of the adaptive control model and the use of L-distribution of the rotary-conveyor milking process on “Karusel” type installations

The article reflects the degree of adequacy of the use of G-distribution and the constructed mathematical model of adaptive control, which allows increasing the productivity of conveyor-ring milking machines of the “Karusel” type. This paper discusses the results of research on the process of milking a herd of cows on a carousel-type milking machine. Based on the methods of variational statistics the influence of the parameters of the statistical distribution of the duration of milking performance of a milking machine is studied, a theoretical analysis of the timeline of its work is carried out. Models and algorithms for adaptive speed control of milking pipeline with the use of specialized software, including the system of analytical calculations Maple are developed. This software allows you to perform analytical transformations of large amounts of information and display the results of calculations in tabular graphical form and in animation mode.

Adaptive and multi-path progression signal control under connected vehicle environment

Through wireless communications, enriched information from connected vehicles (CVs) can describe traffic information near an intersection and would supplement a data source for an effective signal control. This paper proposes an adaptive traffic signal control system in a CV environment. At the intersection level, an adaptive control model is developed to assign optimal green times by minimizing the total vehicle delay. When the CV penetration rate is low, a method depending on limited CV data is presented to estimate the vehicle arrival information. At the corridor level, a real-time optimization model is formulated to design the dynamic progression plan for critical paths (i.e. paths with high flows). These two optimization models are solved by the dynamic programming technique. A real-world arterial is modeled in VISSIM to evaluate the effectiveness and efficiency of the proposed traffic signal control system. Simulations with various CV penetration rates and demand levels are conducted to compare the proposed system with fixed coordination and adaptive signal control systems. Results indicate that the proposed system outperforms both base systems by reducing 15.67% and 13.81% of the average delay, respectively, when the CV penetration rate is relatively high. Moreover, the proposed system can improve the arterial performance under all tested scenarios with various CV penetration rates and demand levels. Since the tested demand scenarios can reflect the real traffic fluctuations, it can be proved that the proposed control system could be applied in the field to boost the overall traffic efficiency along the arterial.

Research on Adaptive Relationship Between Trust and Privacy in Cloud Service

To prevent privacy leakage, cloud services need to take corresponding methods, so participants often face the dilemma of service utility and privacy protection. In this paper, we propose a dynamic adaptive access control model based on trust permission and privacy protection to solve the problem of privacy disclosure and utility in the cloud service. Firstly, we add the concept of obligation and purpose into access control and establish the privacy information tree and privacy policy tree. Secondly, we establish a new trust evaluation and give the corresponding weight algorithm. Thirdly, we quantify the privacy information with the normal space and correlation coefficient method. Further, we propose a tradeoff relationship model between trust permission and privacy protection, each participant can select the corresponding parameters according to the actual requirement and personal preference. Experimental analysis and comparison results verify the feasibility, effectiveness, and superiority of our method. Finally, we summarize the work of this paper and point out the future development direction.

Computer intelligent software system for optimisation of adaptive management of investment design based on deterministic and stochastic network formalisation

The paper discusses the development and creation of an intelligent computer system for optimising the control of investment projecting processes under conditions of uncertainty. This system is designed to automate the modelling of investment projecting processes and optimise the adaptive control of decision-making in their implementation based on network economic and mathematical modelling, as well as methods and tools for the development of intelligent software systems. The basis of the system is the use of a new method of network formalisation and optimisation of adaptive project control in the form of an appropriate methodology for solving the investment projecting problems under consideration. The proposed method also contains a part that implements a neural network stochastic model of adaptive control in the presence of risks. The paper describes the main blocks of the developed intelligent computer software system, its capabilities and presents the results of evaluating the effectiveness of its application.

METHODOLOGY OF SYSTEM MODELING OF ADAPTIVE MANAGEMENT OF MACHINE-BUILDING PRODUCTION

The Model Paradigm of digital control in production systems is based on the intelligent integra-tion of virtual and real processes with production objects (their system models). In this case, the platform (environment) for the intelligent integration of virtual and real processes is the information environment of the subject-oriented area. The functions of specialists, in this case, are reduced to the role of experts who implement the formal predicate knowledge prescribed in the corresponding methods and job descriptions (documents) and analyze the updated data of real production from the corresponding databases and knowledge bases. This ensures the properties of identifiability and traceability of production facilities and their processes. The spatial and temporal distribution of the holding's production facilities determine the basic coordinate system for their system modeling and management. Automation of intellectual processes of organization, planning, monitoring and management of interconnected production facilities in space and time, taking into account the in-fluence of the external environment, in real and virtual aspects, is an important and urgent task. System modeling is one of the main components of the organization of any process, including pro-duction. It should also be noted that the system modeling process itself is an intellectual, laborious and ob-jective process. Purpose of the study. To develop a methodology for system modeling (system de-scription) and adaptive management of production activities by individual enterprises as part of a machine-building holding. Develop a methodology for implementing this system model. Research methods used in the work: – principles of system engineering methodology (process approach, life cycle, etc.); – an integrated approach and structural analysis of the diversity of information processes in the interaction of individual production enterprises within the holding; – functional modeling of dynamic systems. Results. A methodology of system modeling (system description) of multilevel adaptive control of production systems distributed in space and time is proposed, taking into account the influence of the external environment. On the example of a machine-building holding, it is shown that this in-tellectualized system dynamic model can be implemented in the form of an Information-Advisory System. Conclusion. The proposed Methodology of System Analysis and Modeling of Adaptive Mana¬gement of Machine-Building Production as a part of the holding allows solving urgent prob-lems of effective management of production activities, taking into account the influence of the ex-ternal environment.

Accelerating exact constrained shortest paths on GPUs

The recently emerging applications such as software-defined networks and autonomous vehicles require efficient and exact solutions for constrained shortest paths (CSP), which finds the shortest path in a graph while satisfying some user-defined constraints. Compared with the common shortest path problems without constraints, CSP queries have a significantly larger number of subproblems. The most widely used labeling algorithm becomes prohibitively slow and impractical. Other existing approaches tend to find approximate solutions and build costly indices on graphs for fast query processing, which are not suitable for emerging applications with the requirement of exact solutions. A natural question is whether and how we can efficiently find the exact solution for CSP. In this paper, we propose Vine , a framework that parallelizes the labeling algorithm to efficiently find the exact CSP solution using GPUs. The major challenge addressed in Vine is how to deal with a large number of subproblems that are mostly unpromising but require a significant amount of memory and computational resources. Our solution is twofold. First, we develop a two-level pruning approach to eliminate the subproblems by making good use of the GPU's hierarchical memory. Second, we propose an adaptive parallelism control model based on the observations that the degree of parallelism (DOP) is the key to performance optimization with the given amount of computational resources. Extensive experiments show that Vine achieves 18× speedup on average over the widely adopted CPU-based solution running on 40 CPU threads. Vine also has over 5× speedup compared with a GPU approach that statically controls the DOP. Compared to the state-of-the-art approximate solution with preprocessed indices, Vine provides exact results with competitive or even better performance.

A Generalization for Model Reference Adaptive Control and Robust Model Reference Adaptive Control Adaptive Laws for a Class of Nonlinear Uncertain Systems with Application to Control of Wing Rock Phenomenon

Lyapunov's direct method is a primary tool for designing Model Reference Adaptive Control (MRAC) and robust MRAC schemes. In general, Lyapunov function candidates contain two categories of quadratic terms. The first category includes the system tracking error quadratic terms or, in some cases, consist of the system state quadratic terms. The second consists of the parameter estimation error quadratic terms. To design MRAC and Robust MRAC systems, researchers have used a limited variety for choosing quadratic terms. In this study, we consider a general form for the tracking error quadratic terms. We consider a strictly increasing function that belongs to the class of c1, which is a function of state tracking error quadratic terms. It yields a general structure for stable adaptive laws for updating controller parameters. For the MRAC scheme, the global asymptotic stability of the closed-loop system and stability and uniform bounded tracking of robust MRAC schemes are guaranteed. To evaluate the performance of the designed controllers, we consider the single DOF wing rock dynamics.

Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections

Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance our understanding of the future interactions between human-driven vehicles with CAVs at intersections, a simulation framework was developed to model the mixed traffic environment of Automated Vehicles (AV), cooperative AVs, and conventional human-driven vehicles. In order to model AVs driving behavior, Adaptive Cruise Control (ACC) and cooperative ACC (CACC) models are utilized. Particularly, this study explores system improvements due to automation and connectivity across varying CAV market penetration scenarios. ACC and CACC car following models are used to mimic the behavior of AVs and cooperative AVs. Real-world connected vehicle data are utilized to modify and tune the acceleration/deceleration regimes of the Wiedemann model. Next, the driving volatility concept capturing variability in vehicle speeds was utilized to calibrate the simulation to represent the safety performance of a real-world environment. Two surrogate safety measures are used to evaluate the safety performance of a representative intersection under different market penetration rate of CAVs: the number of longitudinal conflicts and driving volatility. At low levels of ACC market penetration, the safety improvements were found to be marginal, but safety improved substantially with more than 40% ACC penetration. Additional safety improvements can be achieved more quickly through the addition of cooperation and connectivity through CACC. Furthermore, ACC/CACC vehicles were found to improve mobility performance in terms of average speed and travel time at intersections.