Adaptive robust tracking control of surface vessels using dynamic constructive fuzzy neural networks

Author

Ning Wang ; Bijun Dai ; Yancheng Liu ; Min Han

Author_Institution

Marine Eng. Coll., Dalian Maritime Univ., Dalian, China

fYear

2014

fDate

6-11 July 2014

Firstpage

377

Lastpage

382

Abstract

In this paper, an adaptive robust dynamic constructive fuzzy neural control (AR-DCFNC) scheme for trajectory tracking of a surface vehicle with uncertainties and unknown time-varying disturbances is proposed. System uncertainties and unknown dynamics are identified online by a dynamic constructive fuzzy neural network (DCFNN) which is implemented by employing dynamically constructive fuzzy rules according to the structure learning criteria. The entire AR-DCFNC system is globally asymptotical stable.

Keywords

adaptive control; asymptotic stability; fuzzy control; fuzzy neural nets; marine control; neurocontrollers; robust control; time-varying systems; AR-DCFNC scheme; DCFNN; adaptive robust dynamic constructive fuzzy neural control scheme; asymptotical stability; dynamic constructive fuzzy neural networks; dynamically constructive fuzzy rules; structure learning criteria; surface vehicle trajectory tracking; surface vessel adaptive robust tracking control; unknown time-varying disturbances; Approximation error; Fuzzy neural networks; Robustness; Sea surface; Vehicle dynamics; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-2073-0

Type

conf

DOI

10.1109/FUZZ-IEEE.2014.6891718

Filename

6891718