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Recent advancements in simulation software and computational hardware make it realizable to simulate a full vehicle system comprised of multiple sub-models developed in different modeling languages. The so-called, co-simulation allows one to develop a control strategy and evaluate various aspects of a vehicle system, such as fuel efficiency and vehicle drivability, in a cost-effective manner. In order to study the feasibility of the synchronized parallel processing in co-simulation this paper presents two co-simulation frameworks for a complete vehicle system with multiple heterogeneous subsystem models. In the first approach, subsystem models are co-simulated in a serial configuration, and the same sub-models are co-simulated in a parallel configuration in the second approach.

In the development of an Adaptive Cruise Control (ACC) system, a model-based design process uses a simulation environment with models for sensor data, sensor fusion, ACC, and vehicle dynamics. Previous work has sought to control the dynamics between two vehicles both in simulation and empirical testing environments. This paper outlines a new modular simulation framework for full model- based design integration to iteratively design ACC systems. The simulation framework uses physics-based vehicle models to test ACC systems in three ways. The first two are Model-in-the-Loop (MIL) testing, using scripted scenarios or Driver-in-the-Loop (DIL) control of a target vehicle. The third testing method uses collected test data replayed as inputs to the simulation to additionally test sensor fusion algorithms. The simulation framework uses 3D visualization of the vehicles and implements mathematical driver comfortability models to better understand the perspectives of the driver or passenger.

A neural network-based computer vision system is developed to estimate position of an excavator manipulator in real time. A camera is used to capture images of a manipulator, and the images are down-sampled and used to train a neural network. Then, the trained neural network can estimate the position of the excavator manipulator in real time. To study the feasibility of the proposed system, a webcam is used to capture images of an excavator simulation model and the captured images are used to train a neural network. The simulation results show that the developed neural network-based computer vision system can estimate the position of the excavator manipulator with an acceptable accuracy.

Currently, several 1D physics-based high-fidelity engine simulation software packages exist and provide reasonably accurate predictions of engine performance. However, most of the current high-fidelity engine simulation packages are developed in conventional programming languages and cannot be directly implemented in today's predominant MATLAB/Simulink simulation environment. In an effort to develop a MATLAB/Simulink-based engine simulation package, a high-fidelity 1D physics-based engine simulation model is currently being developed at The University of Alabama. The proposed model library includes various functional blocks capable of being connected in a logical manner to form a full engine system. Some of the functional blocks include a 1D unsteady flow section, cylinder valve, throttle, flow junction, cylinder, and engine dynamics. In this paper, preliminary simulation results are presented as well as descriptions of the functional blocks.

Hydraulic excavators perform numerous tasks in the construction and mining industry. Although ground grading is a common task, proper grading cannot easily be achieved. Grading requires an experienced operator to control the boom, arm, and bucket cylinders in a rapid and coordinated manner. Due to this reason, automated grade control is being considered as an effective alternative to conventional human-operated ground grading. In this paper, a path-planning method based on a 2D kinematic model and inverse kinematics is used to determine the desired trajectory of an excavator's boom, arm, and bucket cylinders. Then, the developed path planning method and PI control algorithms for the three cylinders are verified by a simple excavator model developed in Simulink®. The simulation results show that the automated grade control algorithm can grade level or with reduced operation time and error.

Engine and drivetrain simulation has become an integral part of the automotive industry. By creating a virtual representation of a physical system, engineers can design controllers and optimize components without producing a prototype, thus reducing design costs. Among the numerous simulation approaches, 1D physics-based models are frequently implemented due to balanced performance between accuracy and computational speed. Several 1D physics-based simulation software packages currently exist but cannot be directly implemented in MALAB/Simulink. To leverage MATLAB/Simulink's powerful controller design and simulation capabilities, a 1D physics-based engine simulation tool is currently being developed at The University of Alabama. Previously presented work allowed the user to connect engine components in a physically representative manner within the Simulink environment using a standard block connection scheme and embedded MATLAB functions.