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This paper presents a preliminary study on the use of reinforcement learning to control the torque vectoring of a small rear wheel driven electric race car in order to improve vehicle handling and vehicle stability. The reinforcement learning algorithm used is Neural Fitted Q Iteration and the sampling of experiences is based on simulations of the vehicle behavior using the software CarMaker. The cost function is based on the position of the states on the phase-plane of sideslip angle and sideslip angular velocity. The resulting controller is able to improve the vehicle handling and stability with a significant reduction in vehicle sideslip angle.
This article describes the implementation of the project ,,Autonomous Driving" which was introduced earlier [7]. Furthermore, it shows the concept of the vehicle diagnosis in detail. The vehicle diagnosis monitors a test car on a proving ground and substitutes the surveillance of a human test driver. Therefore, the human diagnostics capabilities have to be replaced by measuring equipment in order to automate the detection of malfunctions. The components of the vehicle diagnosis system and its operating principle will be shown here. Two test vehicles were equipped with sensors, computers and actuators (driving robots). The vehicles can be driven by a robot while the sensor system observes the entire surroundings in order to be able to avoid obstacles. Furthermore, the vehicle control can be overruled by an electronic copilot in case of an emergency.
The use of actor-critic algorithms can improve the controllers currently implemented in automotive applications. This method combines reinforcement learning (RL) and neural networks to achieve the possibility of controlling nonlinear systems with real-time capabilities. Actor-critic algorithms were already applied with success in different controllers including autonomous driving, antilock braking system (ABS), and electronic stability control (ESC). However, in the current researches, virtual environments are implemented for the training process instead of using real plants to obtain the datasets. This limitation is given by trial and error methods implemented for the training process, which generates considerable risks in case the controller directly acts on the real plant. In this way, the present research proposes and evaluates an open-loop training process, which permits the data acquisition without the control interaction and an open-loop training of the neural networks. The performance of the trained controllers is evaluated by a design of experiments (DOE) to understand how it is affected by the generated dataset. The results present a successful application of open-loop training architecture. The controller can maintain the slip ratio under adequate levels during maneuvers on different floors, including grounds that are not applied during the training process. The actor neural network is also able to identify the different floors and change the acceleration profile according to the characteristics of each ground.
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