Abstract
Model predictive control (MPC) is one of the few control frameworks allowing to systematically integrate input and/or state constraints to realize safe control. Nonetheless, traditional MPC requires a full model of the system dynamics and model mismatch degrades performance. In this article, a data-driven MPC scheme is developed for robot manipulators to reduce dependencies on system models and also design parameters. First, an incremental MPC is designed using an approximated model from time-delay estimation (TDE) that allows for prediction based on recent information from sensors instead of a full model. Then, a data-driven parameter estimation method updates the TDE parameter online to reduce dependence on parameters and improve accuracy of this approximated model. A recursive least squares algorithm is used and equipped with a novel strategy to adapt the forgetting factor based on the variation of the identified parameters. The resulting data-driven MPC allows for efficient implementation and we demonstrate its superior tracking performance in experiments with a 3-DoF robot manipulator.
Original language | English |
---|---|
Journal | IEEE/ASME Transactions on Mechatronics |
DOIs | |
State | Accepted/In press - 2024 |
Keywords
- Incremental system
- model predictive control (MPC)
- recursive least squares
- time-delay estimation (TDE)