Description:
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Advanced control methodologies have helped the development of modern vehicles that are capable of path planning and path following. For instance, Model Predictive Control (MPC) employs a predictive model to predict the behavior of the physical system for a specific time horizon in the future. An optimization problem is solved to compute optimal control actions while handling model uncertainties and nonlinearities. However, these prediction routines are computationally intensive and the computational overhead grows with the complexity of the model.
<jats:italic>Switching MPC</jats:italic>
addresses this issue by combining multiple predictive models, each with a different precision granularity. In this artcle, we proposed a novel switching predictive control method based on a model reduction scheme to achieve various model granularities for path following in autonomous vehicles. A state-based model with tunable parameters is proposed to operate as a reconfigurable predictive model of the vehicle. A runtime switching algorithm is presented that selects the best model using machine learning. We employed a metric that formulates the tradeoff between the error and computational savings due to model reduction. Our simulation results show that the use of the predictive model in the switching scheme as opposed to single granularity scheme, yields a 45% decrease in execution time in tradeoff for a small 12% loss in accuracy in prediction of future outputs and no loss of accuracy in tracking the reference trajectory.
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