| 项目编号: | 1705706
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| 项目名称: | Model predictive control under model structure uncertainty for stochastic systems |
| 作者: | Ali Mesbah
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| 承担单位: | University of California-Berkeley
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| 批准年: | 2017
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| 开始日期: | 2017-09-01
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| 结束日期: | 2020-08-31
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| 资助金额: | 300492
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | model
; system model
; model maintenance
; system fault
; stochastic optimal control
; system dynamics
; real-time control
; model-based control
; model uncertainty
; stochastic model predictive control
; closed-loop control performance
; online model structure adaptation
; model structure
; stochastic system
; different model structure
; active model structure discrimination
; development
; inadequate model structure
; uncertain system
; dual control paradigm
; control performance
; active model structure adaptation
; stochastic system transition
; model structure uncertainty problem
; class i-several rival model structure
; various control application
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| 英文摘要: | Model uncertainty due to inadequate model structure and/or parameters is prevalent in model-based control. In various control applications, the time-varying nature of system dynamics (due to changes in plant and/or disturbance dynamics, or occurrence of system faults and failures) typically increases the uncertainty associated with a system model identified during controller commissioning. The increased model uncertainty over time can eventually lead to degradation of the closed-loop control performance, which will often necessitate some form of model maintenance to restore the control performance. The overarching goal of this research is to develop a framework for integrated stochastic optimal control and active learning of uncertain systems to facilitate online model structure adaptation.
The main research objective of this project is to investigate two different classes of model structure uncertainty problems: Class I-several rival model structures exist for a system (e.g., due to unknown reaction kinetics, or occurrence of system faults) and it is unknown which model structure provides the most accurate description of system dynamics; and Class II-the dynamics of an intrinsically stochastic system are described by a series of models across an operating region (e.g., each model represents a different operating mode) and it is unknown when the transition between the system models occurs. The proposed research will focus on the development of stochastic model predictive control (SMPC) formulations with integrated learning capability for active model structure adaptation for both Classes I and II. Inspired by the dual control paradigm, three research tasks will be pursued: 1. Development of a computationally tractable framework for stochastic optimal control with integrated input design for active model structure discrimination; 2. Development of a SMPC framework that actively switches between different model structures as a stochastic system transitions between different modes/behaviors; and 3. Demonstration of the effectiveness of the SMPC approaches for real-time control of an atmospheric-pressure plasma jet (APPJ) through closed-loop experiments. Prototypical examples of applications of the plasma jet under study include treatment of heat-sensitive (bio)materials and medical therapy. The proposed educational and outreach activities include mentoring undergraduate students, curriculum development for outreach purposes, and conducting science lessons in an underserved school district. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/89158
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Ali Mesbah. Model predictive control under model structure uncertainty for stochastic systems. 2017-01-01.
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