PPT-Model Predictive Control for Humanoid Balance and Locomotion

Benjamin Stephens Robotics Institute Compliant Balance and Push Recovery Full body compliant control Robustness to large disturbances Perform useful tasks in human environments Motivation Improve the performance and usefulness of complex robots simplifying controller design by focusing on simpler models that capture important features of the desired behavior. edu httpwwwcscmuedu bstephe1 Abstract This paper presents a balance controller that allows a humanoid to recover from large disturbances and still maintain an upright posture Balance is achieved by integral control which decouples the dynamics and p Carnegie Mellon University. 9. th. IEEE-RAS International Conference on Humanoid Robots. December 8, 2009. Modeling and Control of Periodic Humanoid Balance Using the Linear Biped Model. Introduction. Benjamin Stephens. Robotics Institute. Compliant Balance and Push Recovery. Full body compliant control. Robustness to large disturbances. Perform useful tasks in human environments. Motivation. Improve the performance and usefulness of complex robots, simplifying controller design by focusing on simpler models that capture important features of the desired behavior. Modelling. and Control. Ton Backx. Emeritaatsviering. . Joos. . Vandewalle. Outline. History. Process performance and process control. Model predictive control essentials. Process modeling. Current developments. Enabling robots to move. Many bio-inspired methods:. Walk, jump, run, slide, skate, swim, fly, roll. Exception: . Powered wheel. Human invention. What’s the benefit?. Power vs. Attainable Speed . # of actuators. Benjamin Stephens. Thesis Proposal. Carnegie Mellon, Robotics Institute. November 23, 2009. Committee:. Chris . Atkeson. (chair). Jessica. . Hodgins. Hartmut. Geyer. Jerry Pratt (IHMC). 2. Thesis Proposal Overview. Enabling robots to move. Many bio-inspired methods:. Walk, jump, run, slide, skate, swim, fly, roll. Exception: . Powered wheel. Human invention. What’s the benefit?. Power vs. Attainable Speed . # of actuators. Team: The Game of Life . Charlie Andres, Long Du, Taylor Gallegan, Jessica Santos, Christopher Werner. 4/7/17. Uconn Goldenson Center Case Study; Case Study Courtesy of Prudential. Project Goals. Using Predictive Analytics in Experience Studies. Control in Buildings. Tony . Kelman. MPC Lab, Berkeley Mechanical Engineering. Email. : . kelman@berkeley.edu. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. Sina Dehghan. , PhD student in ME. MESA. (Mechatronics, Embedded Systems and Automation) . LAB. University of California, Merced. E: sdehghan@ucmerced.edu . Under supervision of:. YangQuan Chen . . and Electrical Drives. Ralph M. Kennel, Technische Universitaet Muenchen, Germany. kennel@ieee.org. 1. Outline. Introduction. Predictive Control Methods. Trajectory Based Predictive Control. Hysteresis Based Predictive Control. 9. th. IEEE-RAS International Conference on Humanoid Robots. December 8, 2009. Modeling and Control of Periodic Humanoid Balance Using the Linear Biped Model. Introduction. 2. Motivation. 3. Simple models for complex systems. Pieter Abbeel. Stanford University. (Variation hereof) presented at Cornell/USC/UCSD/Michigan/UNC/Duke/UCLA/UW/EPFL/Berkeley/CMU. Winter/Spring 2008. In collaboration with: Andrew Y. Ng, Adam Coates, J. Zico Kolter, Morgan Quigley, Dmitri . Many bio-inspired methods:. Walk, jump, run, slide, skate, swim, fly, roll. Exception: . Powered wheel. Human invention. What’s the benefit?. Power vs. Attainable Speed . # of actuators. Structural complexity.