Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions
This addresses the challenge of robust control for robots in unknown or changing conditions, offering an incremental improvement over existing adaptive and predictive methods.
The paper tackled the problem of high-accuracy trajectory tracking for robots in dynamic environments with disturbances, proposing an adaptive model predictive controller that combines MPC with an L1 adaptive controller, and demonstrated in quadrotor experiments that it reduces tracking error compared to non-predictive adaptive and predictive non-adaptive approaches, even under wind disturbances.
Robots and automated systems are increasingly 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 that combines model predictive control (MPC) with an underlying $\mathcal{L}_1$ adaptive controller to improve trajectory tracking of a system subject to unknown and changing disturbances. The $\mathcal{L}_1$ adaptive controller forces the system to behave in a predefined way, as specified by a reference model. A higher-level model predictive controller then uses this reference model to calculate the optimal reference input based on a cost function, while taking into account input and state constraints. We focus on the 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.