Open AccessData-Weighting Periodic RLS Based Adaptive Control Design and Analysis without Linear Growth Condition
Author(s) -
Ronghu Chi,
Zhongsheng Hou,
Shangtai Jin
Publication year - 2014
Publication title -
journal of applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.307
H-Index - 43
eISSN - 1687-0042
pISSN - 1110-757X
DOI - 10.1155/2014/191256
Subject(s) - weighting , control theory (sociology) , estimator , adaptive control , mathematics , controller (irrigation) , parametric statistics , constant (computer programming) , interval (graph theory) , lyapunov function , control (management) , feature (linguistics) , computer science , mathematical optimization , nonlinear system , statistics , artificial intelligence , medicine , linguistics , physics , philosophy , combinatorics , quantum mechanics , biology , agronomy , radiology , programming language
A new periodic recursive least-squares (PRLS) estimator is developed with data-weighting factors for a class of linear time-varying parametric systems where the uncertain parameters are periodic with a known periodicity. The periodical time-varying parameter can be regarded as a constant in the time interval of a periodicity. Then the proposed PRLS estimates the unknown time-varying parameter from period to period in batches. By using equivalent feedback principle, the feedback control law is constructed for the adaptive control. Another distinct feature of the proposed PRLS-based adaptive control is that the controller design and analysis are done via Lyapunov technology without any linear growth conditions imposed on the nonlinearities of the control plant. Simulation results further confirm the effectiveness of the presented approach
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