Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions
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Robots and automated systems are being introduced to unknown and dynamic environments where they are required to handle disturbances, unmodeled dynamics and parametric uncertainties. Robust and adaptive control strategies are required to achieve high performance in these dynamic environments. In this paper, we propose a novel adaptive model predictive controller (MPC) that combines a model predictive controller with an underlying L_1 adaptive controller to improve trajectory tracking of a system subject to unknown and changing disturbances. The L_1 adaptive controller forces the system to behave in a predefined way, as specified by a reference model. A higher-level MPC then uses this reference model to calculate the optimal input given a cost function while taking into account input constraints. We focus on experimental validation of the proposed approach and demonstrate its effectiveness in experiments on a quadrotor. We show that the proposed approach has a lower trajectory tracking error compared to non-predictive, adaptive approaches and a predictive, non-adaptive approach even when external wind disturbances are applied.
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