Péter Gáspár, Zoltán Szabó, József Bokor

Discrete Feedback Systems 2.

Modern Control


State Estimation and Kalman filtering

In the everyday life we experience that we cannot rely on sensors to give us perfect information on the data and events that we want to measure and track. Most of the strategies that cope with this fact are based on Bayesian probability. Roughly speaking Bayesian probability determines what is likely to be true based on past information. E.g., concerning the heading of a car we can use past information – an estimation of the heading angle and some information on the dynamics of the car – to more accurately infer information about the present or future. Sensors are also noisy and a suitable prediction helps us make a better estimate.

Discrete Feedback Systems 2.

Tartalomjegyzék


Kiadó: Akadémiai Kiadó

Online megjelenés éve: 2019

ISBN: 978 963 454 373 2

The classical control theory and methods that we have been presented in the first volume are based on a simple input-output description of the plant, expressed as a transfer function, limiting the design to single-input single-output systems and allowing only limited control of the closed-loop behaviour when feedback control is used. Typically, the need to use modern linear control arises when working with models which are complex, multiple input multiple output, or when optimization of performance is a concern. Modern control theory revolves around the so-called state-space description. The state variable representation of dynamic systems is the basis of different and very direct approaches applicable to the analysis and design of a wide range of practical control problems. To complete the design workflow, finally some introduction into system identification theory is given.

Hivatkozás: https://mersz.hu/gaspar-szabo-bokor-discrete-feedback-systems-2//

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