PPT-Optimal Control on a Discrete Time Influenza Model

Paula A GonzalezParra 1 Leticia Velazquez 12 Sunmi Lee 3 Carlos CastilloChavez 3 1 Program in Computational Science University of Texas at El Paso 2 Department in Mathematical Sciences University of Texas at El Paso. David K. . Guilkey. Demographic Applications:. Single Spell. 1. Time until death. 2. Time until retirement. 3. Time until first marriage. 4. Time until first birth. Multiple Spell. 1. Time until birth of each child. 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. Feng Gu. Way to study a system. . Cited from Simulation, Modeling & Analysis (3/e) by Law and . Kelton. , 2000, p. 4, Figure 1.1. Model taxonomy. Modeling formalisms and their simulators . Discrete time model and their simulators . 12. Discrete Optimization Methods. 12.1 Solving by . Total Enumeration. If model has only a few discrete decision variables, the most effective method of analysis is often the most direct: enumeration of all the possibilities. [12.1] . Advanced Mechanical Design. December 2008. Shaghayegh. . Kazemlou. Advisor. : . Dr. . Shahab. . Mehraeen. Louisiana State University. Part I: Grid-connected Renewable System. Part II: . Converter D. 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. Spring 2012. Optimal Control. Static optimization (finite dimensions). Calculus of variations (infinite dimensions). Maximum principle (. Pontryagin. ) / minimum principle. Based on state space models. 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. 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 . . 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, . Chapter 12: Optimal Control Theory. Kenju. . Doya. , Shin Ishii, . Alexandre. . Pouget. , and Rajesh . P.N.Rao. Summarized by . Seung-Joon. Yi. Chapter. overview. Discrete . Control. Dynamic programming. F RIDA 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, .