Robust adaptive observer design for uncertain systems with bounded disturbances.
This paper presents a robust adaptive observer design methodology for a class of uncertain nonlinear systems in the presence of time-varying unknown parameters with absolutely integrable derivatives, and nonvanishing disturbances. Using the universal approximation property of radial basis function (RBF) neural networks and the adaptive bounding technique, the developed observer achieves asymptotic convergence of state estimation error to zero, while ensuring boundedness of parameter errors. A comparative simulation study is presented by the end.
Stepanyan
Vahram
V
Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0203, USA. vahrams@vt.edu
Hovakimyan
Naira
N
D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity