Design of Constrained Model-Predictive Control Systems with Increased Autonomy

具有增强自主性的约束模型预测控制系统的设计

• 批准号: 9615979
• 负责人: Michael Nikolaou
• 金额: $ 17.92万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-15 至 2000-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9615979&HistoricalAwards=false
• 关键词: Design; Constrained; Model; Predictive; Control

Abstract - Nikolaou - 9615979 Constrained model predictive control (MPC) is a technique that uses a process model in an on-line optimization scheme to meet various control objectives for a process whose inputs and outputs are constrained. There are always discrepancies between the model that MPC uses and the real world. Such discrepancies may arise from inaccuracies in the initial model used by an MPC system, or from variation of a process with time. Increased MPC autonomy, the main thrust of this project, refers to the capability of an MPC system to handle, with minimal human intervention, a wide range of situations where a process model could be inaccurate. Situations that will be addressed in this research are: o Time-invariant processes for which an inaccurate linear of nonlinear model and bounds on the inaccuracy are available or need to be developed. o Time-varying processes under constrained MPC feedback. More specifically, the following problems will be investigated: o How MPC systems can be structured and tuned (off- or on-line) to ensure robust performance for a certain level of process/process-model mismatch. Conversely, how much modeling accuracy is required for robust MPC performance. o How a constrained MPC systems that monitors its performance, can modify itself if necessary, e.g., by performing simultaneous control and closed-loop process identification.

摘要- Nikolaou - 9615979 约束模型预测控制（MPC）是一种在线优化方案中使用过程模型来满足输入和输出受限的过程的各种控制目标的技术。 MPC使用的模型与真实的世界之间总是存在差异。 这种差异可能是由MPC系统使用的初始模型的不准确性引起的，或者是由过程随时间的变化引起的。 增加MPC自主性是该项目的主要目标，指的是MPC系统在最少的人为干预的情况下处理过程模型可能不准确的各种情况的能力。 将在这项研究中解决的情况是：o时不变的过程，其中一个不准确的线性非线性模型和边界的不准确性是可用的或需要开发。 o约束MPC反馈下的时变过程。 更具体地说，将研究以下问题：o MPC系统如何可以被构造和调整（离线或在线），以确保在一定程度的过程/过程模型失配的鲁棒性能。 相反，稳健的MPC性能需要多大的建模精度。 o监控其性能的受约束MPC系统如何在必要时修改自身，例如，通过执行同时控制和闭环过程识别。