Passivity-Based Gain-Scheduled Control with Scheduling Matrices

This paper considers gain-scheduling of very strictly passive (VSP) subcontrollers using scheduling matrices. The use of scheduling matrices, over scalar scheduling signals, realizes greater design freedom, which in turn can improve closed-loop performance. The form and properties of the scheduling matrices such that the overall gain-scheduled controller is VSP are explicitly discussed. The proposed gain-scheduled VSP controller is used to control a rigid two-link robot subject to model uncertainty where robust input-output stability is assured via the passivity theorem. Numerical simulation results highlight the greater design freedom, resulting in improved performance, when scheduling matrices are used over scalar scheduled signals.

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Publication Details

Published
2024-08-21
DOI
https://doi.org/10.1109/ccta60707.2024.10666540
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.21
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Passivity-Based Gain-Scheduled Control with Scheduling Matrices

James Richard Forbes, Sepehr Moalemi
1 citations
Advanced Memory and Neural Computing
0.21
article

Passivity-Based Gain-Scheduled Control with Scheduling Matrices

James Richard Forbes, Sepehr Moalemi
article en
1 citations

Abstract

This paper considers gain-scheduling of very strictly passive (VSP) subcontrollers using scheduling matrices. The use of scheduling matrices, over scalar scheduling signals, realizes greater design freedom, which in turn can improve closed-loop performance. The form and properties of the scheduling matrices such that the overall gain-scheduled controller is VSP are explicitly discussed. The proposed gain-scheduled VSP controller is used to control a rigid two-link robot subject to model uncertainty where robust input-output stability is assured via the passivity theorem. Numerical simulation results highlight the greater design freedom, resulting in improved performance, when scheduling matrices are used over scalar scheduled signals.

McGill University (CA)
Openalex Percentile: Top 48%
Advanced Memory and Neural Computing
0.21
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