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Semi-autonomous control systems applied to automobiles are Advanced Driver Assistance Systems (ADAS) that have gained importance from similar devices with applications in robotics. The control sharing between humans and automatic controllers is the main characteristic of these systems, and can be accomplished through various different manners. However, the use of Artificial Intelligence (AI) techniques for this purpose remains unexplored. In this paper we propose the design of a semi-autonomous control system applied to military vehicles through the use of Fuzzy Inference Systems for the definition of the controller intervention level. Simulations of a vehicle being operated in highly dangerous situations, represented by the existence of hostile military threats or by unexpected maneuvers that could put the stability of the car at risk were performed.

A Fuzzy Controller is designed to simulate a human driver controlling a high speed car through a known trajectory. The implementation of such controller uses the Fuzzy MatLab Toolbox, which is than transformed in a Simulink block and applied a Stationary Kinematical Model of a vehicle. Two different error generation procedures were tested analyzed as the controller's feedback input. Previously studied controllers, such as the PD and PDD, submitted to equivalently high speeds, present an increasing position and orientation error. The Fuzzy Controller, fed with the future basis error generation procedure, is able to reduce in up to 5% the final lap time.

This research work details an optimization method to identify the path of a pre-defined track through which a car with specific acceleration limits completes the circuit in its lowest time. The strategy adopted here uses the evolutionary approach of a Genetic Algorithm's based optimization method to determine the accelerations suffered by the vehicle through the circuit, from pre-defined profiles. Considering an Oriented Particle vehicle model, those acceleration profiles can be integrated to find a specific trajectory path. The results are quite relevant compared with other methods used, such as the Optimal Control and the conventional Least Squares optimization. Receiving as the initial estimation the lane centerline of the track, the first Genetic Algorithm tests managed to reduce the lap time in about 10%.

In this paper are presented the preliminary results of a research work that have been done by the PUC-Rio Vehicular Systems Group aiming to establish a methodology to reproduce the ground vehicles control by humans. The vehicle dynamic model, the path conditions and the control strategies are discussed. We are aiming to employ this methodology to optimize the vehicles behavior in several operation conditions and emergency situations, accidents reconstruction and collisions analysis. The main results here concerns to the representation of a closed path and the use of classical and modern control strategies to follow such trajectory, comparing the behaviors of a linear and a nonlinear vehicle dynamic models, with several degrees of freedom, using physical constraints, such as steering angles and lateral acceleration, among others, to define the control elements parameters.

High speed competition vehicles are required to cover a determined number of laps in a closed trajectory circuit in a time that is the least possible, in the limits of the governing dynamic and driving characteristics of these vehicles. Optimization is a methodology that can be used in order to simulate trajectories and driving techniques used by the competition drivers and to investigate the effects of several parameters in limit conditions of car stability. In this work it is first presented a vehicle model considering the sufficient characteristics for trajectory analysis, influenced by pertinent geometric and physical parameters.

One of the most interesting and challenging applications of the engineering is the solution of the inverse problems, or in other words, those problems which the answer, or the consequence is known, but the cause is not, or the conditions that took such result are the unknowns, mainly when the problem involves a great number of variables and parameters. In this kind of problems we find the scientific approach of the ground vehicle accident reconstruction. Another theme directly related it is the collisions analysis, as in the context of an accident reconstruction, or in the context of the vehicle crashworthiness, associated to it structural integrity and their occupants' passive safety. In this paper is discussed the application of the genetic algorithms, for the treatment of the inverse problem in collisions of ground vehicles, using models of rigid or flexible vehicles.

One of the most interesting and challenging applications in engineering is the solution of inverse problems. In this kind of problem we have as an example the scientific approach of the ground vehicle accident reconstruction. Another directly related theme is collisions analysis, either in the context of an accident reconstruction, or in the context of the vehicle crashworthiness, associated to its structural integrity and their occupants’ passive safety. In this paper the application of optimization techniques for the treatment of ground vehicles collision inverse problem using models of rigid or flexible bodies is discussed. We present an example of the classic approach, the conjugated directions method, and one of the modern, the genetic algorithms.

The mechanical part of an internal combustion engine has been modelled, using the Bond Graphs technique. Given the gas pressure inside the cylinder, the model provides the velocity and torque at the flywheel, as a function of the physical properties of the mobile parts of the engine. The first model obtained was from the slider - crank mechanism for one degree of freedom, representing a mono cylindric engine. Using the modularity of the Bond Graphs technique, three identical models have been coupled, forming a four ILLEGIBLE A valves' command system has been included and the damping coefficient, related with friction losses and the heat generation, was determined taking in consideration of the engine efficiency. All the models have been simulated by computer and the results obtained confirms the experimental data known for commercial engines.

This work presents the mathematical model of a system that have a vehicle running on a structure (bridge). We use the Bond Graphs technique that, through a generalized approach, make possible the individual representation to each subsystem (Vehicle and bridge structure). Using this methodology we can construct the graph for whole system, permitting then the knowledge of the interaction between the two models. We show some simulation results that permit the development of designs to each subsystem by changing theirs parameters.

In this paper we describe the main steps of the Vehicle Dynamics design using the generalized procedures of System Dynamics Modeling, Simulation and Analysis. The methodology discussed is illustrated by a vehicle adaptation design, where the best kind of damper, for the passengers comfort, must be selected. The Bond Graphs technique is used for the mathematical model development of a seven degrees of freedom vehicle, and three dampers constitutive relations are defined. Digital simulation is employed for the vehicle behavior valuation, and we compare the roll angle for the three dampers. Finally the analysis techniques for the optimization of the damper's selection are discussed. The paper is concluded claiming the importance of this approach in engineering design involving high technology and complexity.