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Extended prescribed performance control with input quantization for nonlinear systems
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2022 (English)In: International Journal of Robust and Nonlinear Control, ISSN 1049-8923, E-ISSN 1099-1239, Vol. 32, no 6, p. 3801-3821Article in journal (Refereed) Published
Abstract [en]

For a class of MIMO nonlinear systems, comprised of interconnected subsystems in Brunovsky canonical form, with uncertain yet locally Lipschitz nonlinearities among subsystems and quantized inputs, the target is to form a closed-loop system that exhibits extended prescribed performance on states tracking (accuracy determined by maximum overshoot, minimum convergence rate, maximum steady-state error, and control gains), yet still a low-complexity control structure, without requiring any identification, approximation, and filtering techniques, regardless of uncertainties. In this article, a static, decentralized, continuous, yet computationally inexpensive controller is designed, without requiring parameters of quantizers. Inheriting the merit of pioneering prescribed performance control (PPC) methodology, it is required that the reference signal is (Formula presented.) function only, while, an essential difference and new feature is that, the pioneering PPC guarantees that state tracking errors are constrained by boundary functions (also known as prescribed performance functions, PPFs) only, while, the proposed extend PPC scheme achieves that state tracking errors are constrained by boundary functions and control gains simultaneously, in other words, without “tighter” PPFs, state tracking errors can still be adjusted to arbitrarily small by choosing proper control gains. Finally, comparative simulation results are given to verify the theoretical findings. 

Place, publisher, year, edition, pages
Wiley , 2022. Vol. 32, no 6, p. 3801-3821
Keywords [en]
input quantization, nonlinear system, prescribed performance control, Closed loop control systems, Errors, Boundary function, Control gains, Interconnected subsystems, MIMO nonlinear systems, Performance control, Performance functions, Prescribed performance, Quantisation, State tracking, Tracking errors, Nonlinear systems
National Category
Probability Theory and Statistics Atom and Molecular Physics and Optics Condensed Matter Physics
Identifiers
URN: urn:nbn:se:kth:diva-318401DOI: 10.1002/rnc.5993ISI: 000738312300001Scopus ID: 2-s2.0-85122262202OAI: oai:DiVA.org:kth-318401DiVA, id: diva2:1697575
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QC 20220921

Available from: 2022-09-21 Created: 2022-09-21 Last updated: 2022-09-21Bibliographically approved

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Hu, Xiaoming

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Optimization and Systems Theory
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Probability Theory and StatisticsAtom and Molecular Physics and OpticsCondensed Matter Physics

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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