PPT-Model Predictive Control for Humanoid Balance and Locomotio

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 eeethzch httpcontrolethzch Abstract This paper gives an overview of robustness in Model Predictive Control MPC After reviewing the basic concepts of MPC we survey the uncertainty descriptions considered in the MPC literature and the techniques propos 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. Modelling. and Control. Ton Backx. Emeritaatsviering. . Joos. . Vandewalle. Outline. History. Process performance and process control. Model predictive control essentials. Process modeling. Current developments. 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. 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. 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. Large-scale MPC. سمینار درس کنترل پیش بین. استاد درس : دکتر توحید خواه. . ارائه دهنده :فرزانه کرمی . 89223914. دی ماه 1390. به نام خدا . 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 . . OF Low-Lift Chillers Pre-Cooling . Thermo-Active Building Systems. Nick Gayeski, PhD Building Technology, MIT. IBPSA Model Predictive Control Workshop June 2011. Advisors: Dr. Leslie K. Norford, Dr. Peter R. Armstrong. Control. (MPC). By Finn Aakre Haugen. (. finn.haugen@usn.no. ). Course PEF3006 Process Control. Fall . 2017. 1. USN. PEF3006 Process Control. Haugen. 2017. 2. "MPC is the only advanced control technique that is more advanced than standard PID to have a significant and widespread impact on industrial process control.". 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.