Premium
Nonlinear adaptive tracking‐control synthesis for functionally uncertain systems
Author(s) -
Zwierzewicz Ze
Publication year - 2010
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1114
Subject(s) - control theory (sociology) , nonlinear system , controller (irrigation) , convergence (economics) , adaptive control , exponential stability , sort , matlab , stability (learning theory) , compensation (psychology) , computer science , state variable , control (management) , mathematics , artificial intelligence , psychology , physics , machine learning , psychoanalysis , agronomy , economics , economic growth , operating system , quantum mechanics , thermodynamics , information retrieval , biology
Abstract The paper is concerned with the problem of adaptive tracking system control synthesis. It is assumed that a nonlinear, feedback linearizable object dynamics (model structure) is (partially) unknown and some of its nonlinear characteristics can be approximated by a sort of functional approximators. It has been proven that proportional state feedback plus parameter adaptation are able to assure its asymptotic stability. This form of controller permits online compensation of unknown model nonlinearities and exogenous disturbances, which results in satisfactory tracking performance. An interesting feature of the system is that the whole process control is performed without requisite asymptotic convergence of approximator parameters to the postulated ‘true’ values. It has been noticed that the parameters play rather a role of slack variables on which potential errors (that otherwise would affect the state variables) cumulate. The system's performance has been tested via Matlab/Simulink simulations via an example of ship path‐following problem. Copyright © 2009 John Wiley & Sons, Ltd.