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Multivariable Adaptive Control via Non-Convex Optimization

通过非凸优化的多变量自适应控制

基本信息

批准号：
9508654
负责人：
Erik Ydstie
金额：
$ 26.1万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
1995
资助国家：
美国
起止时间：
1995-08-01 至 1998-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9508654&HistoricalAwards=false
关键词：
Multivariable Adaptive Control via Non

项目摘要

CTS-9508654 Ydstie Carnegie Mellon The objective of this research is to develop design theory for a singularity free multivariable adaptive controller. This will be done by combining adaptive control theory with non-convex optimization, an approach which represents a departure from current design philosophies based on convexification, switching logic and/or persistent excitation. Fast adaptation based on second order information and nonlinear programming should improve the convergence rate. Matched control and estimation objectives will be applied so that optimal steady state performance can be achieved. The optimization based algorithm will be based on theory and have few ad hoc tuning parameters. The new concepts in this research are the "centered parameter", which gives optimal performance of a parameterized control system, the use of non-convex optimization to solve singularity problems and the use of adaptive filters to match design and optimization objectives. The theory will apply to a broad class of certainty equivalence adaptive control algorithms including H infinity, LQG and predictive control. The PIs will work on implementation of adaptive control to large scale industrial processes. Experiments show that it is in multivariable applications that adaptive control shows greatest potential giving improvement in process operation.

卡内基梅隆大学本研究的目的是发展无奇异性的多变量自适应控制器的设计理论。这将通过将自适应控制理论与非凸优化相结合来实现，非凸优化是一种基于凸化、切换逻辑和/或持续激励的偏离当前设计理念的方法。基于二阶信息和非线性规划的快速自适应算法可以提高算法的收敛速度。匹配的控制和估计目标将被应用，以便可以实现最佳的稳态性能。基于优化的算法将基于理论并且具有很少的自组织调整参数。在这项研究中的新概念是“中心参数”，它给出了参数化控制系统的最佳性能，使用非凸优化来解决奇异性问题和使用自适应滤波器来匹配设计和优化目标。该理论将适用于广泛的一类确定性等价自适应控制算法，包括H ∞，LQG和预测控制。 PI将致力于对大规模工业过程实施自适应控制。实验表明，正是在多变量应用中，自适应控制显示出最大的潜力，从而改善过程操作。