Browse Topic: Control systems
This Aerospace Recommended Practice (ARP) provides recommended requirements for the testing of electromechanical actuators (EMAs). General test considerations are also provided. While many EMA configurations include motor control electronics, the specific tests required for the electronic hardware, software, or firmware are outside the scope of this document.
Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and
The road network is a critical component of modern urban mobility systems, with signalized traffic intersections playing a pivotal role. Traditionally, traffic light phase timings and durations at intersections are designed by transportation engineers using historical traffic data. Some modern intersections employ trigger-based mechanisms to improve traffic flow; however, these systems often lack global awareness of traffic conditions across multiple intersections within a network. With the increasing availability of traffic data and advancements in machine learning, traffic light systems can be enhanced by modeling them as agents operating in an environment. This paper proposes a Reinforcement Learning (RL) based approach for multi-agent traffic light systems within a simulation environment. The simulation is calibrated using real-world traffic data, enabling RL agents to learn effective control strategies based on realistic scenarios. A key advantage of using a calibrated simulation
Wind Tunnels are complex and cost-intensive test facilities. Thus, increasing the test efficiency is an important aspect. At the same time, active aerodynamic elements gain importance for the efficiency of modern cars. For homologation, such active aero-components pose an extra level of test complexity as their control strategies, the relevant drive cycles and their aerodynamics in different positions must be considered for homologation-relevant data. Often, active components have to be manually adjusted between test runs, which is a time-consuming process because the vehicle is not integrated into the test automation. Even if so, designing a test sequence stepping through the individual settings for each component of a vehicle is a tedious task in the test session. Thus, a sophisticated integration of the wind tunnel control system with a test management system, supporting the full homologation process is one aspect of a solution. The other is the integration of the vehicle’s active
This document applies to safety observers or spotters involved with the use of outdoor laser systems. It may be used in conjunction with AS4970.
This article introduces a comprehensive cooperative navigation algorithm to improve vehicular system safety and efficiency. The algorithm employs surrogate optimization to prevent collisions with cooperative cruise control and lane-keeping functionalities. These strategies address real-world traffic challenges. The dynamic model supports precise prediction and optimization within the MPC framework, enabling effective real-time decision-making for collision avoidance. The critical component of the algorithm incorporates multiple parameters such as relative vehicle positions, velocities, and safety margins to ensure optimal and safe navigation. In the cybersecurity evaluation, the four scenarios explore the system’s response to different types of cyberattacks, including data manipulation, signal interference, and spoofing. These scenarios test the algorithm’s ability to detect and mitigate the effects of malicious disruptions. Evaluate how well the system can maintain stability and avoid
The segment manipulator machine, a large custom-built apparatus, is used for assembling and disassembling heavy tooling, specifically carbon fiber forms. This complex yet slow-moving machine had been in service for nineteen years, with many control components becoming obsolete and difficult to replace. The customer engaged Electroimpact to upgrade the machine using the latest state-of-the-art controls, aiming to extend the system's operational life by at least another two decades. The program from the previous control system could not be reused, necessitating a complete overhaul.
Model-based developers are turning to DevOps principles and toolchains to increase engineering efficiency, improve model quality and to facilitate collaboration between large teams. Mature DevOps processes achieve these through automation. This paper demonstrates how integrating modern version control (Git) with collaborative development practices and automated quality enforcement can streamline workflows for large teams using Simulink. The focus is on enhancing model consistency, enabling team collaboration, and development speed.
The electric vehicle market, vehicle ECU computing power, and connected electronic vehicle control systems continue to grow in the automotive industry. The results of these advanced and expanded vehicle technologies will provide customers with increased cost savings, safety, and ride quality benefits. One of these beneficial technologies is the tire wearing prediction. The improved prediction of tire wear will advise a customer the best time to change tires. It is expected that this prediction algorithms will be essential part for both the optimization of the chassis control systems and ADAS systems to respond to changed tire performance that varies with a tire’s wear condition. This trend is growing, with many automakers interested in developing advanced technologies to improve product quality and safety. This study is aimed at analyzing the handling and ride comfort characteristics of the tire according to the depth of tire pattern wear change. The handing and ride comfort
Abstract Real-world driving data is an invaluable asset for several types of transportation research, including emissions estimation, vehicle control development, and public infrastructure planning. Traditional methods of real-world driving data collection use expensive GPS-based data logging equipment which provide advanced capabilities but may increase complexity, cost, and setup time. This paper focuses on using the Google Maps application available for smartphones due to the potential to scale-up real-world driving data logging. Samples of the potential data processing and information that can be gathered by such a logging methodology is presented. Specifically, two months of Google Maps driving data logged by a rural Michigan resident on their smartphone may provide insights on their driving range, duration, and geographic area of coverage (AOC) to guide them on future vehicle purchase decisions. Aggregating such statistics from crowd-sourcing real-world driving data via Google
As the complexity of electrified powertrains and their architectures continue to grow and thrive, it becomes increasingly important and challenging for the supervisory torque controller to optimize the torque commands of the electric machines. The hybrid architecture considered in this paper consists of an internal combustion engine paired with at least one electric motor and a DC-DC switching converter that steps-up the input voltage, in this case the high voltage battery, to a higher output voltage level allowing the electric machines to operate at a greater torque range and increased torque responsiveness for efficient power delivery. This paper describes a strategy for computing and applying the losses of the converter during voltage transformation to determine the optimal engine and electric motor torque commands. The control method uses a quadratic fit of the losses at the power limits of the torque control system and on optimal motor torque commands, within the constraints of
Improving the efficiency of Battery Electric Vehicles (BEVs) is crucial for enhancing their range and performance. This paper explores the use of virtual tools to integrate and optimise various systems, with a particular focus on thermal management. The study considers global legislative drive cycles and real-world scenarios, including hot and cold weather conditions, charging cycles, and towing. A virtual vehicle model is developed to include major contributors to range prediction and optimisation, such as thermal systems. Key components analysed include high voltage (HV) and low voltage (LV) consumers (compressors, pumps, fans), thermal system performance and behaviour (including cabin climate control), thermal controllers, and thermal plant models. The emergent behaviour resulting from the interaction between hardware and control systems is also examined. The methodology involves co-simulation of hardware and control models, encompassing thermal systems (coolant, refrigerant, cabin
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