Abstract
This article deals with experimental identification and control of laboratory helicopter model CE 150 manufactured by company Humusoft. Structure of the identified system was approximated by linear black-box models. Discrete Input/Output Auto-Regressive Moving Average model with eXternal input (ARMAX) and its state space equivalent were used. Parameters of the models were estimated by regression techniques using System Identification Toolbox for Matlab. Acquired models were validated using simulations, residual analysis and real-time control. Input/output data necessary for identification were obtained by measurements from laboratory model and were processed using Real-Time Toolbox for Matlab. Based on acquired mathematical models input/output and state space controllers were designed (input/output pole placement with integration, state space pole placement with integration and observer). Designed controllers were implemented in Matlab environment using the Real Time Toolbox and their performance was verified by real-time control of the helicopter model.
Highlights
A great deal of attention has been centered on the field of automation in various domains of industry thanks to increased demand for safety, fault proof and efficient usage of natural resources
If there is no information about the dynamics of identified system available a priori we call the corresponding model a black-box model
This article was devoted to experimental identification and control of the laboratory helicopter model CE 150 manufactured by company Humusoft
Summary
A great deal of attention has been centered on the field of automation in various domains of industry thanks to increased demand for safety, fault proof and efficient usage of natural resources. Performance and reliability of control algorithms which are the key part of automation depend heavily on the mathematical model of controlled system To obtain such a model several approaches are available and we term this procedure as system identification. If there is no information about the dynamics of identified system available a priori we call the corresponding model a black-box model Knowledge about such system is gained through the analysis of the input/output measurements and is called experimental identification. Situation most widely encountered in practice is when both analytical and experimental identification are used and the resulting model is called a grey-box model Authoritative account on this subject can be found in [1], [2], [3]
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