Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions
| Autor: | Karime Pereida, Angela P. Schoellig |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Adaptive control Computer science 02 engineering and technology Tracking error Vehicle dynamics Model predictive control 020901 industrial engineering & automation Control theory 0202 electrical engineering electronic engineering information engineering Trajectory Robot 020201 artificial intelligence & image processing Parametric statistics |
| Zdroj: | IROS |
| DOI: | 10.1109/iros.2018.8594267 |
| Popis: | 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, nonadaptive approach, even when external wind disturbances are applied. |
| Databáze: | OpenAIRE |
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