Skip to content

Download Adaptive filtering prediction and control by Graham C Goodwin PDF

By Graham C Goodwin

This unified survey of the idea of adaptive filtering, prediction, and keep an eye on specializes in linear discrete-time structures and explores the typical extensions to nonlinear platforms. according to the significance of desktops to functional functions, the authors emphasize discrete-time platforms. Their method summarizes the theoretical and sensible features of a big category of adaptive algorithms.
Ideal for complicated undergraduate and graduate periods, this therapy includes components. the 1st part issues deterministic structures, overlaying types, parameter estimation, and adaptive prediction and regulate. the second one half examines stochastic structures, exploring optimum filtering and prediction, parameter estimation, adaptive filtering and prediction, and adaptive keep an eye on. huge appendices supply a precis of proper historical past fabric, making this quantity principally self-contained. Readers will locate that those theories, formulation, and functions are on the topic of numerous fields, together with biotechnology, aerospace engineering, machine sciences, and electric engineering. 

Show description

Read Online or Download Adaptive filtering prediction and control PDF

Best system theory books

Stabilization, Optimal and Robust Control: Theory and Applications in Biological and Physical Sciences

Platforms ruled by way of nonlinear partial differential equations (PDEs) come up in lots of spheres of analysis. The stabilization and regulate of such structures, that are the point of interest of this publication, are dependent round online game thought. The powerful regulate tools proposed the following have the dual goals of compensating for method disturbances in the sort of method expense functionality achieves its minimal for the worst disturbances and offering the simplest regulate for stabilizing fluctuations with a restricted keep watch over attempt.

System Identification: A Frequency Domain Approach

Method identity is a common time period used to explain mathematical instruments and algorithms that construct dynamical versions from measured facts. Used for prediction, regulate, actual interpretation, and the designing of any electric platforms, they're important within the fields of electric, mechanical, civil, and chemical engineering.

Stochastic control theory. Dynamic programming principle

This ebook bargains a scientific creation to the optimum stochastic keep an eye on concept through the dynamic programming precept, that's a strong device to research keep watch over difficulties. First we think about thoroughly observable keep an eye on issues of finite horizons. utilizing a time discretization we build a nonlinear semigroup with regards to the dynamic programming precept (DPP), whose generator presents the Hamilton–Jacobi–Bellman (HJB) equation, and we symbolize the price functionality through the nonlinear semigroup, in addition to the viscosity answer concept.

Additional resources for Adaptive filtering prediction and control

Sample text

However, the reader should bear in mind the general comments made above so as to view these algorithms within their proper perspective. In the next section we introduce a particular class of on-line parameter estimation algorithms which are particularly attractive because of their simplicity. 2 ON-LINE ESTIMATION SCHEMES As we mentioned in the preceding section, on-line estimation schemes produce an updated parameter estimate within the time span between successive samples. Thus it is highly desirable that the algorithm be simple and easy to implement.

Note that we have not-said anything about 8(t)necessarily converging to 8,. In fact, we have not said that 8(t) converges to anything. However, the properties as given above are of great importance since they have been derived under extremely weak assumptions. For example, no restriction has been imposed on the nature of the data [in particular, $(t) need not necessarily be bounded]. Property (i) ensures that 8(t) is never further from 8, than 8(0) is. Property (ii) implies that the modeling error, e(t), when appropriately normalized is square summable.

The obvious criterion to use is one that can measure how well the final objective is achieved. For example, in a control problem, different models could be judged on the basis of how well the controller developed from each model meets the design specifications. The main difficulty associated with this approach is that it may be necessary to implement fully each and every design (developed from its own model) before a decision can be made on what is the best model. Consequently, it is generally more practicable to decide between models on the basis of simpler indirect criteria, such as minimization of mean square one-step-ahead prediction error, minimization of maximum prediction error, and so on.

Download PDF sample

Rated 4.42 of 5 – based on 31 votes