Show simple item record

dc.contributor.advisorSangbae Kim and Russ Tedrake.en_US
dc.contributor.authorBledt, Gerardoen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-05-23T15:03:48Z
dc.date.available2018-05-23T15:03:48Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/115593
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 67-71).en_US
dc.description.abstractA novel Policy Regularized Model Predictive Control (PR-MPC) framework is developed to allow general robust legged locomotion with the MIT Cheetah quadruped robot. The full system is approximated by a simple control model that retains the key nonlinearities characteristic to legged contact dynamics while reducing the complexity of the continuous dynamics. Nominal footstep locations and feedforward forces for controlling the robot's center of mass are designed from simple physics-based heuristics for steady state legged movement. By regularizing the predictive optimization with these policies, we can exploit the known dynamics of the system to bias the controller towards the steady state gait while remaining free to explore the cost space during transient behaviors and disturbances. The nonlinear optimization makes use of direct collocation on the simplified dynamics to pose the problem with a highly sparse structure for fast computation. A generalized approach to the controller design is independent from specific gait pattern and reference policy and allows stabilization of aperiodic locomotion. Simulation results show dynamic capabilities in a variety of gaits including trotting, bounding, and galloping, all without changing the set of algorithm parameters between experiments. Robustness to sensor and input noise, large push disturbances, and unstructured terrain demonstrate the ability of the predictive controller to adapt to uncertainty.en_US
dc.description.statementofresponsibilityby Gerardo Bledt.en_US
dc.format.extent71 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titlePolicy regularized model predictive control framework for robust legged locomotionen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc1036985468en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record