Stable adaptive systems
Multilayer feedforward networks are universal approximators
Neural Networks
An approximate observer for a class of nonlinear systems
Systems & Control Letters
High-gain observers in the state and estimation of robots having elastic joints
Systems & Control Letters
A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems
Automatica (Journal of IFAC)
On-line system identification of complex systems using Chebyshev neural networks
Applied Soft Computing
Adaptive output feedback tracking control of robot manipulators using position measurements only
Expert Systems with Applications: An International Journal
Brief paper: Robust adaptive observer for nonlinear systems with unmodeled dynamics
Automatica (Journal of IFAC)
A Nonlinear State Observer Design for 2-DOF Twin Rotor System Using Neural Networks
ACT '09 Proceedings of the 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies
PID Control Using Presearched Genetic Algorithms for a MIMO System
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The Chebyshev-polynomials-based unified model neural networks forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive observers for unknown general nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A stable neural network-based observer with application to flexible-joint manipulators
IEEE Transactions on Neural Networks
Robust Adaptive Observer Design for Uncertain Systems With Bounded Disturbances
IEEE Transactions on Neural Networks
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This paper addresses the problem of observer design for the twin rotor multi-input-multi-output (MIMO) system which is a nonlinear system. Exact knowledge of the dynamics of twin rotor MIMO system (TRMS) is difficult to obtain but it is highly desired that the observer can dominate the effects of unknown nonlinearities and unmodeled dynamics independently to prevent the state estimations from diverging and to get precise estimations. The unknown nonlinearities are estimated by Chebyshev neural network (CNN) whose weights are adaptively adjusted. Lyapunov theory is used to guarantee stability for state estimation and neural network weight errors. A comparative experimental study is presented to demonstrate the enhanced performance of the proposed observer.