Data-Driven Hierarchical Predictive Learning in Unknown Environments

@article{Vallon2020DataDrivenHP,
  title={Data-Driven Hierarchical Predictive Learning in Unknown Environments},
  author={Charlott Vallon and F. Borrelli},
  journal={2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)},
  year={2020},
  pages={104-109}
}
  • Charlott Vallon, F. Borrelli
  • Published 2020
  • Computer Science, Engineering
  • 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)
We propose a hierarchical learning architecture for predictive control in unknown environments. We consider a constrained nonlinear dynamical system and assume the availability of state-input trajectories solving control tasks in different environments. A parameterized environment model generates state constraints specific to each task, which are satisfied by the stored trajectories. Our goal is to find a feasible trajectory for a new task in an unknown environment. From stored data, we learn… Expand
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References

SHOWING 1-10 OF 23 REFERENCES
Cautious Model Predictive Control Using Gaussian Process Regression
Iterative Learning-Based Path Optimization for Repetitive Path Planning, With Application to 3-D Crosswind Flight of Airborne Wind Energy Systems
Task Decomposition for Iterative Learning Model Predictive Control
The voice of optimization
Learning-Based Model Predictive Control for Autonomous Racing
Adaptive MPC with Chance Constraints for FIR Systems
Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking
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