Autonomous driving using GA-optimized neural network based adaptive LPV-MPC controller
Autonomous vehicles are complex systems that operate in dynamic environments, where automated driving seeks to control the coupled longitudinal and lateral vehicle dynamics to follow a certain behavior. Model predictive control is one of the most promising tools for this type of application due to its optimal performance and ability to handle input and output constraints. This paper addresses autonomous driving by introducing an adaptive linear parameter varying model predictive controller (LPV-MPC), whose prediction model is adapted online by a neural network. Moreover, the controller’s cost function is optimized by an improved Genetic Algorithm. The proposed controller is evaluated on a challenging track subject to variable wind disturbances.
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