Verification of Learning Strategies for Servomechanism Time Suboptimal Control

M. Alexik (Slovakia)


Sliding mode, hardware in loop simulation, neural nets.


This paper describes three strategies for realisation of time sub optimal learning algorithm applied for position servomechanism control. This servomechanism was realised in laboratory and its control was realised in real time. The necessity of learning algorithm usage results from demand of time sub optimal control of position servomechanism even its loads is changed in large range. Instantaneous value of moment of inertia is not known, so it is not possible to use deterministic time optimal control with switching curved line. Author derived three different learning strategies for “recovery” time sub optimal trajectory. The effectiveness (algorithm learning time) is different for every strategy. Strategies of time sub optimal switching curved line finding are based on sliding mode control. It is combined with: 1.) progressive search of suitable slope of switching line, 2) real time continuous identification of servo mechanism parameters and computing of switching curved line, 3) off line computing of servo mechanism inverse neurons model with switching curved line computing followed by real time classification with time suboptimal control.

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