Model Learning and Adaptation in MPC


  • Developed a set of adaptive MPC algorithms for online model learning during MPC design. The algorithms are valid for both parametric and non-parametric model uncertainties.


Relevant Papers


  1. Monimoy Bujarbaruah, Xiaojing Zhang, Marko Tanaskovic, Francesco Borrelli; Adaptive Stochastic MPC under Time Varying Uncertainty, IEEE Transactions on Automatic Control, June 2021 [pdf]

  2. Monimoy Bujarbaruah, Siddharth H. Nair, Francesco Borrelli; A Semi-Definite Programming Approach to Robust Adaptive MPC under State Dependent Uncertainty; IEEE European Control Conference (ECC), May 2020 [pdf, code]

  3. Siddharth H. Nair, Monimoy Bujarbaruah, Francesco Borrelli; Modeling of Dynamical Systems via Successive Graph Approximations; IFAC World Congress, July 2020 [pdf]

  4. Monimoy Bujarbaruah, Charlott Vallon; Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint, Learning for Dynamics and Control (L4DC), June 2020 [pdf]

  5. Monimoy Bujarbaruah, Xiaojing Zhang, Ugo Rosolia, Francesco Borrelli; Adaptive MPC for Iterative Tasks, IEEE Conference on Decision and Control (CDC), Miami, FL, December 2018 [pdf]

  6. Monimoy Bujarbaruah, Xiaojing Zhang, H. Eric Tseng, Francesco Borrelli; Adaptive MPC for Autonomous Lane Keeping, 14th International Symposium on Advanced Vehicle Control (AVEC), Beijing, China, July 2018 [pdf]

  7. Monimoy Bujarbaruah, Xiaojing Zhang, Francesco Borrelli; Adaptive MPC with Chance Constraints for FIR Systems, IEEE American Control Conference (ACC), Milwaukee, WI, June 2018 [pdf]

Constraint Learning and Adaptation in MPC


  • Developed a set of algorithms for learning and adapting state-input constraints during MPC design.


  • The input constraint adapting algorithms were subsequently utilized in a trajectory optimization problem and experimentally tested on a full-scale autonomous truck.


Relevant Papers


  1. Monimoy Bujarbaruah, Charlott Vallon, Francesco Borrelli; Learning to Satisfy Unknown Constraints in Iterative MPC, IEEE Conference on Decision and Control (CDC), Jeju, South Korea, December 2020 [pdf, code]

  2. Lars Svensson, Monimoy Bujarbaruah, Nitin Kapania, Martin Törngren; Adaptive Trajectory Planning and Optimization at Limits of Handling; IEEE International Conference on Intelligent Robots and Systems (IROS), Macau, November 2019 [pdf]

  3. Lars Svensson, Monimoy Bujarbaruah, Arpit Karsolia, Christian Berger, Martin Törngren; Traction Adaptive Motion Planning at the Limits of handling, IEEE Transactions on Control System Technology [pdf]

Primal-Dual Policy Learning for Explicit MPC


  • Developed two safe and near optimal primal-dual MPC policy learning algorithms for MPC using deep neural networks.


  • The developed algorithms were implemented on vehicle chassis control and lane keeping applications using ROS to obtain 10x speedup over the Gurobi solver.


Relevant Papers


  1. Xiaojing Zhang*, Monimoy Bujarbaruah*, Francesco Borrelli; Near-Optimal Rapid MPC using Neural Networks: A Primal-Dual Policy Learning Framework, IEEE Transactions on Control System Technology (*equal contribution) [pdf]

  2. Xiaojing Zhang, Monimoy Bujarbaruah, Francesco Borrelli; Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks; IEEE American Control Conference (ACC), Philadelphia, PA, July 2019 [pdf, code]

Learning Disturbance Distribution Bounds in MPC


  • Developed an algorithm for online learning of disturbance distribution bounds using Bootstrap confidence intervals.


  • Deployed the algorithm with an MPC controller on a UR5e robot which learned to solve the ball-in-cup problem.


Relevant Papers


  1. Monimoy Bujarbaruah, Akhil Shetty, Kameshwar Poolla, Francesco Borrelli; Learning Robustness with Bounded Failure: An Iterative MPC Approach, IFAC World Congress, July 2020 [pdf, code]

  2. Monimoy Bujarbaruah, Tony Zheng, Akhil Shetty, Martin Sehr, Francesco Borrelli; Learning to Play Cup-and-Ball with Noisy Camera Observations, IEEE International Conference on Automation Science and Engineering (CASE), Hong Kong, August 2020 [pdf, code]

Robust MPC for Linear Parameter Varying Systems


  • Developed a set of robust MPC algorithms that handle both parametric model uncertainties and additive disturbances in the system.


  • The algorithms obtained an improved computational complexity vs conservatism trade-off over state-of-the-art tube MPC approaches across various numerical examples. See this repository for examples.


Relevant Papers


  1. Monimoy Bujarbaruah, Ugo Rosolia, Yvonne R. Stürz, Xiaojing Zhang, Francesco Borrelli; Robust MPC for LPV Systems via a Novel Optimization-Based Constraint Tightening, Automatica, September 2022 [pdf, code]

  2. Monimoy Bujarbaruah, Ugo Rosolia, Yvonne R. Stürz, Francesco Borrelli; A Simple Robust MPC for Linear Systems with Parametric and Additive Uncertainty, IEEE American Control Conference (ACC), May 2021 [pdf, code]

Collaborative Robotics with Obstacle Inference


  • Developed an algorithm for learning unknown obstacles and safely completing a transportation task with two robots.


  • The control algorithm is decentralized, and the robots switch leader-follower roles with only local estimates, and implicit communications such as force and torque feedback.

Relevant Papers


  1. Monimoy Bujarbaruah, Yvonne R. Stürz, Conrad Holda, Karl H. Johansson, Francesco Borrelli; Learning Environment Constraints in Collaborative Robotics: A Decentralized Leader-Follower Approach, IEEE International Conference on Intelligent Robots and Systems (IROS), September 2021 [pdf, code, video]

Miscellaneous


  • Solving the physics based puzzle Bubble Ball, a convex optimization based planner-tracker synthesis for nonlinear systems, lane change assistance controller for vehicles with active front steering, and an attitude constrained controller for a spacecraft.

Relevant Papers


  1. He Yin*, Monimoy Bujarbaruah*, Murat Arcak, Andrew Packard; Optimization Based Planner-Tracker Design for Safety Guarantees; IEEE American Control Conference (ACC), July 2020 (*equal contribution) [pdf]

  2. Hotae Lee, Monimoy Bujarbaruah, Francesco Borrelli; Learning How to Solve Bubble Ball, Learning for Dynamics and Control (L4DC), June 2021 [pdf, video]

  3. Monimoy Bujarbaruah, Ziya Ercan, Vladimir Ivanovic, H. Eric Tseng, Francesco Borrelli; Torque Based Lane Change Assistance with Active Front Steering, IEEE International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, October 2017 [pdf]

  4. Monimoy Bujarbaruah, Srikant Sukumar; Lyapunov Based Attitude Constrained Control of a Spacecraft, AAS-AIAA Astrodynamics Specialist Conference, Vail-CO, USA, August 2015 [pdf]