Learning MPC (LMPC)相关资料
这是关于LMPC非常好的资料:
Learning MPC (LMPC): Learning Safe Set and Cost to Go
This set of of papers shows repetitively learning cost-to go and safe invariant sets for systems with known models.
Publications:
-
U. Rosolia and F. Borrelli, "Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework," in IEEE Transactions on Automatic Control, vol. 63, no. 7, pp. 1883-1896, July 2018.
-
Rosolia, Ugo, and Francesco Borrelli. "Learning model predictive control for iterative tasks: A computationally efficient approach for linear system." IFAC-PapersOnLine 50.1 (2017): 3142-3147.
-
U. Rosolia, X. Zhang and F. Borrelli, "Simple Policy Evaluation for Data-Rich Iterative Tasks," 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 2855-2860.
GitHub repositories:
-
LMPC GitHub
-
LMPC Racing
Video of experimental tests
-
LMPC on the Berkeley Autonomous Race Car (BARC) Platform
-
LMPC for Autonomous Racing on Hyundai Genesis (Track Test)
Efficient LMPC: Learning Convex Safe Sets for Known Nonlinear Systems
This set of of papers shows a computationally efficient extension of LMPC for nonlinear systems with known models.
Publications:
-
U. Rosolia and F. Borrelli, "Learning How to Autonomously Race a Car: A Predictive Control Approach," in IEEE Transactions on Control Systems Technology.
-
Rosolia, U., & Borrelli, F. (2019). Minimum Time Learning Model Predictive Control. ArXiv, abs/1911.09239.
-
Nair, S.H., Rosolia, U., & Borrelli, F. (2020). Output-Lifted Learning Model Predictive Control for Flat Systems. ArXiv, abs/2004.05173.
Robust LMPC: Learning Cost to Go and Safe Set under Disturbance
This set of of papers shows extension of LMPC to systems under a model disturbance. The support/PDF of disturbance is known.
Publications:
-
Rosolia, U., Zhang, X., & Borrelli, F. (2019). Robust Learning Model Predictive Control for Linear Systems. ArXiv, abs/1911.09234.
-
U. Rosolia, X. Zhang and F. Borrelli, "Robust learning model predictive control for iterative tasks: Learning from experience," 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, VIC, 2017, pp. 1157-1162.
-
U. Rosolia and F. Borrelli, "Sample-Based Learning Model Predictive Control for Linear Uncertain Systems," 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France, 2019, pp. 2702-2707.
-
U. Rosolia, X. Zhang and F. Borrelli, "A Stochastic MPC Approach with Application to Iterative Learning," 2018 IEEE Conference on Decision and Control (CDC), Miami Beach, FL, 2018, pp. 5152-5157.
Learning Model Uncertainty with Guaranteed Safety
This set of of papers shows learning of uncertain model parameters in robust/stochastic MPC. Disturbance supports are known
Publications:
-
M. Bujarbaruah, X. Zhang and F. Borrelli, "Adaptive MPC with Chance Constraints for FIR Systems," 2018 Annual American Control Conference (ACC), Milwaukee, WI, 2018, pp. 2312-2317.
-
M. Bujarbaruah, X. Zhang, U. Rosolia and F. Borrelli, "Adaptive MPC for Iterative Tasks," 2018 IEEE Conference on Decision and Control (CDC), Miami Beach, FL, 2018, pp. 6322-6327.
-
Bujarbaruah, M., Zhang, X., Tanasković, M., & Borrelli, F. (2019). Adaptive MPC under Time Varying Uncertainty: Robust and Stochastic. ArXiv, abs/1909.13473.
-
Bujarbaruah, M., Nair, S.H., & Borrelli, F. (2019). A Semi-Definite Programming Approach to Robust Adaptive MPC under State Dependent Uncertainty. ArXiv, abs/1910.04378.
-
Nair, S.H., Bujarbaruah, M., & Borrelli, F. (2019). Modeling of Dynamical Systems via Successive Graph Approximations. ArXiv, abs/1910.03719.
-
Bujarbaruah, M., & Vallon, C. (2019). Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint. ArXiv, abs/1912.04408.
-
Papadimitriou, D.G., Rosolia, U., & Borrelli, F. (2020). Control of Unknown Nonlinear Systems with Linear Time-Varying MPC. ArXiv, abs/2004.03041.
-
Bujarbaruah, M., Zhang, X., Tseng, H.E., & Borrelli, F. (2018). Adaptive MPC for Autonomous Lane Keeping. ArXiv, abs/1806.04335.
Learning Disturbance Support While Allowing Failure
This set of of papers shows learning of unknown disturbance supports with known confidence. Safety during learning is ensured with a guaranteed probability.
Publications:
-
Bujarbaruah, M., Shetty, A., Poolla, K., & Borrelli, F. (2019). Learning Robustness with Bounded Failure: An Iterative MPC Approach. ArXiv, abs/1911.09910.
GitHub repositories:
-
coming soon
Video of experimental tests
-
Catching on Kendama
Learning Environment Safety Constraints While Allowing Failure
This set of of papers shows learning of unknown safety constraints. True constraint satisfaction is ensured with a guaranteed probability
Publications:
-
Bujarbaruah, M., Vallon, C. & Borrelli, F.(2020).Learning to Satisfy Unknown Constraints in Iterative MPC. ArXiv, abs/2006.05054.
Learning a Policy for Different Environments
This set of papers shows the development of data-driven control policies for solving tasks in unknown environments, while being able to guarantee constraint satisfaction before beginning the task.
Publications:
-
Vallon, C., & Borrelli, F. (2019). Task decomposition for iterative learning model predictive control. ArXiv, abs/1903.07003.
-
Vallon, C., & Borrelli, F. (2020). Task Decomposition for MPC: A Computationally Efficient Approach for Linear Time-Varying Systems. ArXiv, abs/2005.01673.
-
Vallon, C., & Borrelli, F. (2020). Data-Driven Hierarchical Predictive Learning in Unknown Environments. ArXiv, abs/2005.05948.
-
Shen, X., Zhu, E. L., Stürz, Y. R., & Borrelli, F. (2020). Collision avoidance in tightly-constrained environments without coordination: a hierarchical control approach. arXiv preprint arXiv:2011.00413.
GitHub repositories:
-
TDMPC GitHub
-
Strategy-Guided Control GitHub - MATLAB, Python & ROS
Learning a Policy for Fast Online MPC
This set of of papers shows fast implementation of explicit MPC using neural networks with online suboptimality check.
Publications:
-
X. Zhang, M. Bujarbaruah and F. Borrelli, "Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks," 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 354-359.
-
Zhang, X., Bujarbaruah, M., & Borrelli, F. (2019). Near-Optimal Rapid MPC using Neural Networks: A Primal-Dual Policy Learning Framework. ArXiv, abs/1912.04744.
GitHub repositories:
-
Primal-Dual Policy Learning