Exponentially Stable Adaptive Control of MIMO Systems with Unknown Control Matrix
Anton Glushchenko, Konstantin Lastochkin

TL;DR
This paper introduces a novel adaptive control method for MIMO systems that guarantees exponential convergence and monotonic parameter transients without prior system knowledge, validated through aircraft control experiments.
Contribution
It presents the first adaptive control approach for fully unknown MIMO systems with exponential convergence and monotonicity of control parameters.
Findings
Method achieves exponential convergence of parameters and errors.
Applicable to fully unknown MIMO systems without prior info.
Experimental validation on aircraft control model confirms theoretical results.
Abstract
The scope of this research is a problem of the direct model reference adaptive control of linear time-invariant multi-input multi-output (MIMO) plants without any a priori knowledge about system matrices. To handle it, a new method is proposed, which includes three main stages. Firstly, using the well-known DREM procedure, the plant parametrization is made to obtain the linear regressions, in which the plant matrices and state initial conditions are the unknown parameters. Secondly, such regressions are substituted into the known equations for the controller parameters calculation. Thirdly, the controller parameters are identified using the novel adjustment law with exponential rate of convergence. To the best of the authors knowledge, such a method is the first one to provide the following features simultaneously: 1) it is applicable for the generic completely unknown MIMO systems…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAerospace Engineering and Control Systems · Adaptive Control of Nonlinear Systems · Stability and Control of Uncertain Systems
