MFV3D Book Archive > System Theory > Download Modeling and Identification of Linear Parameter-Varying by Roland Toth PDF

Download Modeling and Identification of Linear Parameter-Varying by Roland Toth PDF

By Roland Toth

Through the prior two decades, the framework of Linear Parameter-Varying (LPV) structures has develop into a promising process theoretical method of h- dle the controlof mildly nonlinear and particularly place established structures that are universal in mechatronic purposes and within the method ind- test. The beginning of the program type was once initiated by way of the necessity of engineers to accomplish greater functionality for nonlinear and time-varying dynamics, c- mon in lots of business purposes, than what the classical framework of Linear Time-Invariant (LTI) keep an eye on delivers. even though, it was once additionally a p- mary aim to maintain simplicity and “re-use” the strong LTI effects by means of extending them to the LPV case. The development endured in keeping with this philosophy and LPV keep watch over has turn into a good confirmed ?eld with many promising purposes. regrettably, modeling of LPV structures, in particular in keeping with measured facts (which is named method identi?cation) has visible a restricted improvement sincethebirthoftheframework. Currentlythisbottleneck oftheLPVfra- paintings is halting the move of the LPV concept into business use. with out solid types that ful?ll the expectancies of the clients and with no the und- status how those types correspond to the dynamics of the appliance, it truly is di?cult to layout excessive functionality LPV keep an eye on strategies. This publication goals to bridge the space among modeling and regulate by way of investigating the elemental questions of LPV modeling and identi?cation. It explores the lacking info of the LPV procedure concept that experience hindered the formu- tion of a good verified identi?cation framework.

Show description

Read or Download Modeling and Identification of Linear Parameter-Varying Systems PDF

Best system theory books

Nature's patterns: Flow

From the swirl of a wisp of smoke to eddies in rivers, and the massive chronic hurricane approach that's the great place on Jupiter, we see comparable varieties and styles anywhere there's circulation - no matter if the stream of wind, water, sand, or flocks of birds. it's the complicated dynamics of circulation that constructions our surroundings, land, and oceans.

Systemic Yoyos: Some Impacts of the Second Dimension (Systems Evaluation, Prediction and Decision-Making)

A unique option to research difficulties and inspire Systemic ThinkingReal-Life Case reports Illustrate the appliance of the Systemic Yoyo version in various parts Written through the co-creator of the systemic yoyo version, Systemic Yoyos: a few affects of the second one size exhibits how the yoyo version and its technique might be hired to review many unsettled or super tricky difficulties in smooth technology and expertise.

Stochastic Differential Equations: An Introduction with Applications

This publication supplies an advent to the fundamental conception of stochastic calculus and its functions. Examples are given through the textual content, in an effort to inspire and illustrate the idea and exhibit its value for lots of purposes in e. g. economics, biology and physics. the elemental thought of the presentation is to begin from a few uncomplicated effects (without proofs) of the simpler circumstances and increase the speculation from there, and to pay attention to the proofs of the better instances (which however are frequently sufficiently common for plenty of reasons) as a way to be capable to succeed in quick the elements of the speculation that's most crucial for the functions.

Simulation-Based Algorithms for Markov Decision Processes

Markov determination procedure (MDP) types are time-honored for modeling sequential decision-making difficulties that come up in engineering, economics, desktop technological know-how, and the social sciences. Many real-world difficulties modeled by means of MDPs have large kingdom and/or motion areas, giving a gap to the curse of dimensionality and so making functional answer of the ensuing types intractable.

Additional resources for Modeling and Identification of Linear Parameter-Varying Systems

Example text

26) with monic transfer function H0 (q) such that H0 , H0−1 ∈ H2 (E) and e is a zero-mean white noise process with variance σe2 . 25), are available. Under the given assumptions, the so-called one-step ahead prediction of y(k) based on {y(k − 1), y(k − 2), . } and {u(k), u(k − 1), . } is yˆ := (1 − H0(q)−1 )y + H0 (q)−1 G0 (q)u. 27) In prediction-error identification, a parameterized model (G(q, θ ), H(q, θ )) is hypothesized where θ ⊂ Θ represents the parameter vector, the coefficients of the model, and Θ ∈ Rn is the allowed parameter space.

The basis functions, that provide bases for the space H2 (Hilbert space of complex functions that are squared integrable on the unit circle), are generated by a cascaded network of stable all-pass filters, whose pole locations represent the prior knowledge about the system at hand. 13) i=0 ∞ where {wi }∞ i=0 is the set of constant coefficients and Φ∞ = {φi }i=0 with φ0 = 1 represents the sequence of OBFs. 1) for constant scheduling p(k) = p can be represented as a linear combination of a given Φ∞ .

In Chap. 7 the modeling of NL systems in a LPV form is investigated and the available solutions for this problem are studied. Using the framework of the LPV behavioral approach, a new mechanism is introduced that solves the LPV modeling issue of such systems. This approach together with the discretization methods of Chap. 6 are developed with the intention to assist the model-structure selection phase of the identification cycle based on first principle knowledge. In Chap. 8 the basis-selection problem of OBFs-based LPV model structures is considered.

Download PDF sample

Rated 4.94 of 5 – based on 5 votes