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Gaganyaan programme is India's prestigious human space exploration endeavour. During the re-entry of the spacecraft, achieving the minimum terminal velocity is paramount to ensure the crew's safety upon landing. Therefore, acquiring accurate in-flight velocity data is essential for comprehensively understanding the landing dynamics and facilitating post-flight data analysis and validation. Moreover, terminal velocity plays a pivotal role in the qualification of parachute systems during platform-drop tests where the objective is to minimize the terminal velocity for safe impact. Terminal velocity also serves as a critical design parameter for the crew seat attenuation system. In addition to terminal velocity, it is equally necessary to characterize the horizontal velocities acting on the decelerating body due to various factors such as parachute sway and wind drift. This data also plays a central role in refining our systems for future enhancements.

The purpose of the Air Generation System is to provide a constant supply of conditioned fresh air to meet the necessary oxygen availability and to prevent carbon dioxide (CO2) concentrations for the occupants in an aircraft. The engine bleed energy or electrical load energy consumed towards this circumstance accounts to be approx. 5% of total fuel burn and in turn, contributes to the global emissions of greenhouse gases. This paper studies the improvement areas of the present conventional system such as fuel burn consumption associated with an aircraft environmental control system (ECS) depending on, the amount of bleed and ram air usage, electric power consumption. Improved systems for propulsion, power generation, sustainability, hybridization, and environmental control can be desirable for an aircraft.

This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.

Under complex and extreme operating conditions, the road adhesion coefficient emerges as a critical state parameter for tire force analysis and vehicle dynamics control. In contrast to model-based estimation methods, intelligent tire technology enables the real-time feedback of tire-road interaction information to the vehicle control system. This paper proposes an approach that integrates intelligent tire systems with machine learning to acquire precise road adhesion coefficients for vehicles. Firstly, taking into account the driving conditions, sensor selection is conducted to develop an intelligent tire hardware acquisition system based on MEMS (Micro-Electro-Mechanical Systems) three-axis acceleration sensors, utilizing a simplified hardware structure and wireless transmission mode. Secondly, through the collection of real vehicle experiment data on different road surfaces, a dataset is gathered for machine learning training.

As the automotive industry accelerates its virtual engineering capabilities, there is a growing requirement for increased accuracy across a broad range of vehicle simulations. Regarding control system development, utilizing vehicle simulations to conduct ‘pre-tuning’ activities can significantly reduce time and costs. However, achieving an accurate prediction of, e.g., stopping distance, requires accurate tire modeling. The Magic Formula tire model is often used to effectively model the tire response within vehicle dynamics simulations. However, such models often: i) represent the tire driving on sandpaper; and ii) do not accurately capture the transient response over a wide slip range. In this paper, a novel methodology is developed using the MF-Tyre/MF-Swift tire model to enhance the accuracy of ABS braking simulations.

Battery electric vehicles are quickly gaining momentum to improve vehicle fuel efficiency and emission reduction. However, they must be designed to provide adequate range on a single charge combined with good acceleration performance, top speed, gradeability, and fast charging times. The paper presents a model for sizing the power train of an electric vehicle, including the power electronic converter, electric motor, and battery pack. A major assumption is that an optimal wheel slip rate can be achieved by modern vehicles using slip control systems. MATLAB/Simulink was used to model the vehicle powertrain. Simulations were conducted based on different speed and acceleration profiles. The purpose of the study focused on the motor and power electronics sizing requirements to achieve optimal range and performance.

The tire cornering stiffness plays a vital role in the functionality of vehicle dynamics control systems, particularly when it comes to stability and path tracking controllers. This parameter relies on various external variables such as the tire/ambient temperature, tire wear condition, the road surface state, etc. Ensuring a reliable estimation of the cornering stiffness value is crucial for control systems. This ensures that these systems can accurately compute actuator requests in a wide range of driving conditions. In this paper, a novel estimation method is introduced that relies solely on standard vehicle sensor data, including data such as steering wheel angles, longitudinal acceleration, lateral acceleration, yaw rate, and vehicle speed, among others. Initially, the vehicle's handling characteristics are deduced by estimating the understeer gradient.

The focus on driver and occupant safety as well as comfort is increasing rapidly while designing commercial vehicles in India. Improvements in the road network have enhanced road transport for commercial vehicles. Apart from the cost of operation and fuel economy, the commercial vehicles must deliver goods within stipulated time. These factors resulted in higher speed of operation for commercial vehicles. The design should not compromise the safety of the vehicle at these higher speeds of operation. The vehicle should obey the driver’s intended direction at all speeds and the response of the vehicle to driver input must be predictable without much larger surprises which can lead to accidents. The commercial vehicles are designed with rigid axle and RCB type steering system. This suspension and steering design combination introduce steering errors when vehicle travel over bump, braked and while cornering.

Virtual simulation is a fundamental tool for the development of new vehicles, both for individual components and for complete subsystems and full vehicles. Many software tools exist in the automotive sector to assess full-vehicle behavior and performance, including multibody software and algorithms based on 14 (or more) degrees-of-freedom vehicle dynamics models. In order to reproduce the testing maneuvers and typical vehicle mission, a key part of such simulation tools is the virtual driver algorithm. It is essential to implement a control logic that reproduces the handling response of the driver, so that the closed-loop maneuvers can be evaluated. However, the response of typical virtual drivers is not always similar to the human driving characteristics. Virtual driver algorithms can perform very fast, precise, and smooth steering and pedal actions, while humans display a more variable, delayed and often not optimal actions.

Direct debugging of a vertical takeoff and landing (VTOL) fixed-wing aircraft’s control system can easily result in risk and personnel damage. It is effectively to employ simulation and numerical methods to validate control performance. In this paper, the attitude stabilization controller for VTOL fixed-wing aircraft is designed, and the controller performance is verified by MATLAB and visual simulation software, which significantly increases designed efficiency and safety of the controller. In detail, we first develop the VTOL fixed-wing aircraft’s six degrees of freedom kinematics and dynamics models using Simulink module, and the cascade PID control technique is applied to the VTOL aircraft’s attitude stabilization control. Then the visual simulation program records the flight data and displays the flight course and condition, which can validate the designed controller performance effectively.

The main objective of platoon control is coordinated motion of autonomous vehicle platooning with small intervehicle spacing while maintaining the same speed and acceleration as the leading vehicle, which can save energy consumption and improve traffic throughput. The conventional platoon control methods are confronted with the problem of manual parameter tuning. In order to addres this isue, a novel bifold platoon control approach leveraging a deep reinforcement learning-based model is proposed, which enables the platoon adapt to the complex traffic environment, and guarantees the safety of platoon. The upper layer controller based on the TD3 tuned PID algorithm outputs the desired acceleration. This integration mitigates the inconvenience of frequent manual parameter tuning asociated with the conventional PID algorithm. The lower layer controller tracks the desired acceleration based on the inverse vehicle dynamics model and feedback control.

Hybrid electric aircraft propulsion is an emerging technology that presents a variety of potential benefits along with technical integration challenges. Developing these new propulsion architectures with their complex control systems, and ultimately proving their benefit, is a multistep process. This process includes concept development and analysis, dynamic simulation, hardware-in-the-loop testing, full-scale testing, and so on. This effort is being revolutionized and indeed enabled by new digital tools that support increasing the technology readiness level throughout the maturation process. As part of this Digital Transformation, NASA has developed a suite of publicly available digital tools that facilitate the path from concept to implementation. This paper describes the NASA-developed tools and puts them in the context of control system development for hybrid electric aircraft propulsion.

In 2017 the National Research Council of Canada developed an evaporation model for controlling engine icing tunnels in real time. The model included simplifications to allow it to update the control system once per second, including the assumption of sea level pressure in some calculations. Recently the engine icing system was required in an altitude facility requiring operation down to static temperatures of -40°C, and up to an altitude of 9.1 km (30 kft) or 30 kPa. To accommodate the larger temperature and pressure range the model was modified by removing the assumption of sea level operation and expanding the temperature range. In addition, due to the higher concentration of water vapor that can be held by the atmosphere at lower pressures, the significance of the effect of humidity on the air properties and the effect on the model was investigated.

Success in metal additive manufacturing (AM) relies on the optimization of a large set of process parameters to achieve materials whose properties and performance meet design and safety requirements. Despite continuous improvements in the process over the years, the quality of AM parts remains a major concern for manufacturers. Today, researchers are starting to move from discrete geometry-dependent build parameters to continuously variable or dynamically changing parameters that are geometry- and scan-path aware. This approach has become known as “feedforward control.” Process Control for Defect Mitigation in Laser Powder Bed Fusion Additive Manufacturing discusses the origins of feedforward control, its early implementations in AM, the current state of the art, and a path forward to its broader adoption. Click here to access the full SAE EDGETM Research Report portfolio.

Lithium-ion and Lithium polymer batteries are fast becoming ubiquitous in high-discharge rate applications for military and non-military systems. Applications such as small aerial vehicles and energy transfer systems can often function at C-rates greater than 1. To maximize system endurance and battery health, there is a need for models capable of precisely estimating the battery state-of-charge (SoC) under all temperature and loading conditions. However, the ability to perform state estimation consistently and accurately to within 1% error has remained unsolved. Doing so can offer enhanced endurance, safety, reliability, and planning, and additionally, simplify energy management. Therefore, the work presented in this paper aims to study and develop experimentally validated mathematical models capable of high-accuracy battery SoC estimation.

Equipment used in aerospace non-destructive inspection presents opportunity for modernization. Many inspection cells in production operate using a widely available control system software that is suitable for most inspection applications with minimal customization. The size and complex geometry of airframe components demand more application-specific system design to ensure the reliability and cycle time required for an aerospace production schedule. Ordinary inspection systems require manual teaching for program generation and lack datum-finding systems required to rerun programs without modification. Integration of offline programming software and machine vision instruments can save inspection technicians hours or shifts per part by eliminating the need for program retraining due to variation in part delivery position. Modernized inspection cells will reduce labor burden on technicians and provide reliable cycle time information to production planners.

Automatic robotic drilling is a widely used way of fastening in the field of aircraft assembly and is worth studying continuously. Drilling accuracy is one of the most remarkable properties of the system, which is directly related to the absolute positioning accuracy of the robot end effector. Due to the kinematic errors and gravity of the robotic system itself, the nominal pose and the actual pose of the end effector are no longer consistent with each other. It is necessary to keep the high positioning accuracy of the system. In this paper, an automatic robotic drilling system with high positioning accuracy is proposed. Generally, there are two methods to improve positioning accuracy: off-line calibration and on-line adjustment. An off-line calibration based on the DH method is proposed to identify and modify the parameter errors of the robot. A 6- degree of freedom industrial robot is integrated into the drilling system.

Aircraft electrification is one of technological innovations to achieve the goal of CO2 emission reduction in civil aviation. In present research, we focus ourselves on an Electric Fuel Metering System (EFMS). Aircraft systems are commonly expected to make not only simplified configuration and improvement of controllability, but also safety and reliability. The electrification of fuel system also requires the similar approach. Therefore, a simple and reliable redundancy concept is a crucial challenge. In addition, stable and responsive controllability that does not affect engine operation is required, especially in fuel system, it is desired to achieve both accurate metering and short settling time without overshoot or undershoot. However, in such a system, the response is nonlinear due to the fuel flow circuit and the motor drive during current limiting.

The aviation industry (passenger and freight), which currently accounts for 2.5% of the global CO2 emissions (1.9% of global greenhouse gas (GHG) emissions), is continuously under pressure to reduce its environmental footprint, given its historical and forecasted environmental track, strongly affected by the remarkable air traffic volume increase rates, albeit with a slower growth in emissions, due to the massive aviation's efficiency improvements, driven by the in the design and technology(more efficient and larger) aircrafts; improved operational practices and increased load factors (more passengers and freight per flight). Nevertheless, it has not been enough to tackle the rapidly increasing CO2 emissions (26% in the 2013-2018 timeframe and expected to continue increasing), which ultimately could grow between 2.4 and 3.6 times by 2050.

Aircraft cabin temperature is controlled by regulating the cabin supply air temperature. This supply air temperature is a critical parameter which varies with respect to aircraft altitude and to be maintained properly to the required value corresponding to that altitude to have a good comfortable condition inside the cockpit. It is affected by many internal factors like engine bleed air flow rate, pressure, and temperature and also with external factors like ambient temperature, pressure, and attitude. Due to huge variations in these parameters especially in a fighter aircraft, the cabin temperature control system of this aircraft is often experience limit cycle oscillations and subsequent cabin temperature fluctuations. To minimize the cabin temperature fluctuations, suitable control logic needs to be considered at the design stage itself to avoid future tuning of such control system which makes additional flight-tests and generates large expenses.