PPT-PID2018 Benchmark Challenge: Model Predictive Control Using A General Purpose Optimal

Sina Dehghan PhD student in ME MESA Mechatronics Embedded Systems and Automation LAB University of California Merced E sdehghanucmercededu Under supervision of YangQuan Chen . 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. 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. Dr. Imtiaz Hussain. email: . imtiaz.hussain@faculty.muet.edu.pk. URL :. http://imtiazhussainkalwar.weebly.com/. Lecture-41-42. Design of Control Systems in Sate Space. Quadratic Optimal Control. Outline. 442. Fall 2015. Kris Hauser. Toy Nonlinear Systems. Cart-pole. Acrobot. Mountain car. Optimal Control. So far in our discussion, we have not explicitly defined the criterion for determining a “good” control. 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. 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. CS 659. Kris Hauser. Control Theory. The use of . feedback. to regulate a signal. Controller. Plant. Desired signal x. d. Signal x. Control input u. Error e = x-x. d. (By convention, x. d. = 0). x’ = f(x,u). Optimal Control of Flow and Sediment in River and Watershed National Center for Computational Hydroscience and Engineering (NCCHE) The University of Mississippi Presented in 35th IAHR World Congress, September 8-13,2013, Chengdu, Alan L. Jennings & Ra. úl Ordóñez, . ajennings1. , . raul.ordonez@notes.udayton.edu. Electrical and Computer Engineering, University of Dayton. Frederick G. Harmon, . frederick.harmon@afit.edu. Motivation and IntroductionHow to employ data for optimal control? Plant DisturbanceInputController CostsConstraints State •Model-Free RL simultaneously parameterize -Poor data efficiency-Dynamic Identification . of . Dynamic Models . of . Biosystems. Julio R. . Banga. IIM-CSIC, Vigo, . Spain. julio@iim.csic.es. CUNY-Courant Seminar in Symbolic-Numeric Computing. CUNY . Graduate. . Center. , Friday, . Paula A. Gonzalez-Parra. 1. Leticia Velazquez. 1,2. Sunmi Lee. 3. Carlos Castillo-Chavez. 3. 1. Program in Computational Science, University of Texas at El Paso. 2 . Department in Mathematical Sciences, University of Texas at El Paso.