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Control of vehicular platoons has been a problem of interest in the controls domain for the past 40 years. This problem gained a lot of popularity when the California PATH (Partners for Advanced Transportation Technology) program was operational. String stability is an important design criterion in this problem and it has been shown that lead vehicle information is essential to achieve it. This work builds upon the existing framework and presents a controller form for each follower in the string where the lead vehicle information is used explicitly to analytically demonstrate string stability. The discussion is focused on using information from immediate neighbors to achieve string stability. Recent developments in distributed control are an attractive framework for control design where each agent has access to states of the neighbors and not all agents in the network. In this work, the aim is to design sparse H2 controllers and then perform a check on string stability.

The motion of a vehicle along a desired path is possible due to steering action of the driver. Hence, vehicle dynamics and control simulations should take into consideration the action of the driver. This work presents a preview based vehicle steering controller using Neural Networks which can be used in the vehicle lateral dynamics simulations. The training data for the Neural Network is being obtained using a steering controller from the existing literature and its gains are determined using Optimization. Three different architectures are being designed and conclusions are presented. These Neural Network models are validated by testing against real track data.

Ride quality is concerned with the feel of the passenger in the environment of moving vehicle. It is one of the key indices in determining comfort levels of a vehicle. Although, “ride comfort” evaluation is subjective in nature, researchers have developed mathematical models to study and evaluate vehicle ride performance. Some popular models for vehicle ride analysis are - quarter car model, 2 DOF and 4 DOF half car model. These models model the chassis as a rigid body. This work removes this assumption and models the chassis as a flexible beam on a spring damper system at the front and rear using Euler beam theory. This elastic model has 2 DOF - vehicle bounce and pitch, and has been compared with the rigid 2 DOF model. Euler beam theory and Lagrangian mechanics are used to derive the equations of motion. Finite element method is being used to validate this model. Experimental validation of the natural frequencies of this flexible beam is presented.

Ride is considered to be one of the criteria for evaluating the performance of a vehicle. However, the evaluation of ride quality of a car varies from person to person. There are many mathematical models which are used to study vehicle ride dynamics. The development of electronics, sensors and actuators has enabled the use of control systems to enhance the ride quality of a vehicle. This work deals with some of the commonly used ride models viz. Quarter Car model, 2 Degree of Freedom (DoF) Half Car Model and 4 DoF Half Car Model. A complete analysis of these models from a designer's perspective, e.g., variation of suspension stiffness, damping coefficient etc. is being presented. The response of these models to random excitation is being studied. The above mentioned models are based on the rigid body assumption. They do not incorporate a tire model which takes into account the stiffness and damping properties of the tire.

The planar rigid bicycle model is one of the most popular models used in vehicle dynamics. It has widely been used in studying vehicle handling characteristics and designing steering control system for vehicles. This paper analyses a modified dynamic model called the "Elastic Bicycle Model." This model improves upon the classical bicycle model by taking into account the flexibility of the vehicle frame by using concepts from the Euler beam theory. Complete set of the resulting dynamic equations of this model are presented. Non-dimensional versions of the equations are used to investigate the steady state response of the model. Finally, the results of the response study obtained by modeling a small truck with an elastic model and the classical bicycle model are presented. These include the steady state solutions as function of different parameters as well as a transient solution in response to a saw-tooth steering input and a step input. Octave® has been used for simulation purpose.

This paper focuses on developing a Low Cost IMU integrated with GPS for vehicle state estimation. Knowledge of vehicle states can help design control systems which take these states as an input. Technological advancements in Micro-Electro-Mechanical Sensors (MEMS) have made accelerometers and gyroscopes economical. However, these MEMS based low cost sensors have inherent noise which accumulates with the passage of time and therefore makes their output unreliable. GPS measurements can be used to rectify the inertial sensor errors. Calibration as well as hardware implementation has been discussed in detail. Emphasis is on measurement of body slip angle. Simulations as well as actual results are being presented. Conclusion is being made on the performance of this system.