LAUSR

This paper presents an adaptive and weighted model predictive control (MPC) algorithm for autonomous driving with disturbance estimation and prediction. Unexpected and unpredictable disturbances in the real world limit the performance of MPC. To overcome this limitation, this paper proposes adaptive and weighted prediction methods with a sliding mode observer and a weighting function with the grey prediction model. The sliding mode observer is designed for disturbance estimation with finite stability conditions, and the estimated disturbance is predicted using the grey prediction model. Based on the adaptive and weighted prediction method, the length of prediction horizon and cost value of each predicted state are adjusted in real time to eliminate any negative impact on future predicted states. Meanwhile, a variation in the cost value, which is caused by prediction horizon adaptation and weighted prediction, may harm the control performance as it can excessively increase or decrease the model uncertainty. Therefore, an input weighting factor is adapted in the MPC cost function based on an exponential weighting function. The performance of the proposed adaptive control algorithm is evaluated using CarMaker software under longitudinal and lateral autonomous driving scenarios.