title: Dynamic neural network-based robust observers for uncertain nonlinear systems.
abstract: A dynamic neural network (DNN) based robust observer for uncertain nonlinear systems is developed. The observer structure consists of a DNN to estimate the system dynamics on-line, a dynamic filter to estimate the unmeasurable state and a sliding mode feedback term to account for modeling errors and exogenous disturbances. The observed states are proven to asymptotically converge to the system states of high-order uncertain nonlinear systems through Lyapunov-based analysis. Simulations and experiments on a two-link robot manipulator are performed to show the effectiveness of the proposed method in comparison to several other state estimation methods.
authors.affiliation: Department of Mechanical Engineering, University of Transport and Communications, Viet Nam. Electronic address: huyendtt214@gmail.com.
authors.forename: H T
authors.initials: HT
authors.lastname: Dinh
authors.affiliation: Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611-6250, USA. Electronic address: rkamalapurkar@ufl.edu.
authors.forename: R
authors.initials: R
authors.lastname: Kamalapurkar
authors.affiliation: Department of Electrical Engineering, Indian Institute of Technology, Delhi, India. Electronic address: sbhasin@ee.iitd.ac.in.
authors.forename: S
authors.initials: S
authors.lastname: Bhasin
authors.affiliation: Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611-6250, USA. Electronic address: wdixon@ufl.edu.
authors.forename: W E
authors.initials: WE
authors.lastname: Dixon
mesh_terms: D000465:Algorithms; D005246:Feedback; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D035501:Uncertainty
keywords: Lyapunov method; Neural networks; Output feedback; Robust adaptive control