Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143042
Title: Automated tuning of nonlinear model predictive controller by reinforcement learning
Authors: Mehndiratta, Mohit
Camci, Efe
Kayacan, Erdal
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Mehndiratta, M., Camci, E., & Kayacan, E. (2018). Automated tuning of nonlinear model predictive controller by reinforcement learning. Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3016-3021. doi:10.1109/iros.2018.8594350
Abstract: 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 required for the process become highly dependent on the skill set and experience of the user. In this study, we present a generic and user-independent framework which automates the tuning process by reinforcement learning. The proposed method shows competency in tuning a nonlinear MPC (NMPC) which is employed for trajectory tracking control of aerial robots. It explores the desirable weights within less than an hour in iterative Gazebo simulations running on a standard desktop computer. The real world experiments illustrate that the NMPC weights explored by the proposed method result in a satisfactory trajectory tracking performance.
URI: https://hdl.handle.net/10356/143042
ISBN: 978-1-5386-8095-7
DOI: 10.1109/iros.2018.8594350
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/iros.2018.8594350
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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