Automated tuning of nonlinear model predictive controller by reinforcement learning
One of the major challenges of model predictive control (MPC) for robotic applications is the non-trivial weight tuning process while crafting the objective function. This process is often executed using the trial-and-error method by the user. Consequently, the optimality of the weights and the time...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/143042 |