Multiple Model Reference Adaptive Tracking Control of Multivariable Systems with Blending

被引：0
|
作者：
Lovi, Alex [1 ]
Fidan, Baris [2 ]
Nielsen, Christopher [1 ]
机构：
[1] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
[2] Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada
来源：
2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC) | 2022年
基金：
加拿大自然科学与工程研究理事会;
关键词：
Multiple model; Adaptive control; Polytopic uncertainty; Model reference adaptive control;
D O I：
10.1109/CDC51059.2022.9993291
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper develops a multiple fixed model blending-based adaptive online parameter identification scheme and a multiple model reference adaptive control (MMRAC) for multiple input, multiple output (MIMO) linear time-invariant (LTI) systems with polytopic parameter uncertainty. The parameter identification scheme produces bounded estimates that asymptotically converge to the unknown system's matrices. The proposed MMRAC is developed by combining the proposed adaptive parameter identification scheme with a state-space model reference control approach. This MMRAC scheme is proven to asymptotically track signals generated by a LTI reference model. A set of simulation test results are presented to illustrate the stability and effectiveness of the proposed MMRAC scheme.
引用
收藏
页码：1362 / 1367
页数：6
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