NDLI: Conservative to confident: Treating uncertainty robustly within Learning-Based Control

Content Provider	IEEE Xplore Digital Library
Author	Ostafew, C.J. Schoellig, A.P. Barfoot, T.D.
Copyright Year	2015
Description	Author affiliation: Univ. of Toronto Inst. for Aerosp. Studies, Toronto, ON, Canada (Ostafew, C.J.; Schoellig, A.P.; Barfoot, T.D.)
Abstract	Robust control maintains stability and performance for a fixed amount of model uncertainty but can be conservative since the model is not updated online. Learning-based control, on the other hand, uses data to improve the model over time but is not typically guaranteed to be robust throughout the process. This paper proposes a novel combination of both ideas: a robust Min-Max Learning-Based Nonlinear Model Predictive Control (MM-LB-NMPC) algorithm. Based on an existing LB-NMPC algorithm, we present an efficient and robust extension, altering the NMPC performance objective to optimize for the worst-case scenario. The algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modelled as a Gaussian Process (GP) based on experience collected during previous trials as a function of system state, input, and other relevant variables. Nominal state sequences are predicted using an Unscented Transform and worst-case scenarios are defined as sequences bounding the 3σ confidence region. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, GPS-denied environments. The paper presents experimental results from testing on a 50 kg skid-steered robot executing a path-tracking task. The results show reductions in maximum lateral and heading path-tracking errors by up to 30% and a clear transition from robust control when the model uncertainty is high to optimal control when model uncertainty is reduced.
Starting Page	421
Ending Page	427
File Size	1802604
Page Count	7
File Format	PDF
e-ISBN	9781479969234
DOI	10.1109/ICRA.2015.7139033
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Prediction algorithms Robustness Robots Optimal control Transforms Computational modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Learning and performing place-based mobile manipulation

Fast robust model predictive control of high-dimensional systems

From time-optimal schedule to robust event-based control

On energy-based robust passive control of a robot manipulator

LP based MPC Algorithm in Distributed Real-Time Systems with End-to-End Tasks

On reinforcement learning for robots

Optimization Based Weighting Matrices Design for Norm Optimal Iterative Learning Control

PSO-based fuzzy predictive control

Prioritized optimal control: A hierarchical differential dynamic programming approach

Conservative to confident: Treating uncertainty robustly within Learning-Based Control

Similar Documents

Learning and performing place-based mobile manipulation

Fast robust model predictive control of high-dimensional systems

From time-optimal schedule to robust event-based control

On energy-based robust passive control of a robot manipulator

LP based MPC Algorithm in Distributed Real-Time Systems with End-to-End Tasks

On reinforcement learning for robots

Optimization Based Weighting Matrices Design for Norm Optimal Iterative Learning Control

PSO-based fuzzy predictive control

Prioritized optimal control: A hierarchical differential dynamic programming approach

Conservative to confident: Treating uncertainty robustly within Learning-Based Control