LPV system identification using series expansion models

Tóth, Roland; Heuberger, Psc Peter; Hof, van den Pmj Paul

doi:10.1142/9789814355452_0010

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Most often, an LPV model is obtained by linearizing a nonlinear model at several time points. It can also be derived through system identification methods [34] or when the state-space matrices contain parameters with variations explicitly defined over time.…”

Section: The Lpv Modelmentioning

confidence: 99%

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Synthesis Methodology for Discrete MIMO PID Controller with Loop Shaping on LTV Plant Model via Iterated LMI Restrictions

Konkov,

Mitrishkin

2024

Mathematics

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This paper presents a methodology for synthesizing discrete MIMO PID controllers through iterative solutions of LMIs. It justifies the necessity of direct synthesis of discrete controllers in digital control systems. The proposed methodology allows for synthesizing an LTI controller for an LTV plant model, ensuring the robust stability of the synthesized system. Robustness is further ensured by the small-gain theorem and a novel approach to loop shaping, enabling the use of arbitrary shape functions. As a result, this methodology provides a broad statement of the problem with performance criteria necessary for practical application in control systems. Numerical examples are used to illustrate the methodology, which is implemented using the MATLAB toolbox, freely available for use.

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Section: The Lpv Modelmentioning

confidence: 99%

“…With each new iteration, we become closer to the global optimum µ i −→ µ. The MIMO PID controller matrices {K P , K I , K D }, which must be found during the solution of the LMIs (36), are linearly included in the LMIs (36) via Equations ( 32)- (34).…”

Section: The Convex-concave Proceduresmentioning

confidence: 99%