Search Results

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.

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.