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Found 5 papers, 1 papers with code
Learning Hierarchical Control Systems for Autonomous Systems with Energy Constraints
This paper focuses on the design of hierarchical control architectures for autonomous systems with energy constraints.
Data-Driven Strategies for Hierarchical Predictive Control in Unknown Environments
In addition to task-invariant system state and input constraints, a parameterized environment model generates task-specific state constraints, which are satisfied by the stored trajectories.
Learning to Satisfy Unknown Constraints in Iterative MPC
We propose a control design method for linear time-invariant systems that iteratively learns to satisfy unknown polyhedral state constraints.
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint
This paper proposes an Adaptive Stochastic Model Predictive Control (MPC) strategy for stable linear time-invariant systems in the presence of bounded disturbances.
Task Decomposition for Iterative Learning Model Predictive Control
A task decomposition method for iterative learning model predictive control is presented.
