A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive output feedback tracking control of robot manipulators using position measurements only
Expert Systems with Applications: An International Journal
Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints
Fuzzy Sets and Systems
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
Automatica (Journal of IFAC)
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Stable neural controller design for unknown nonlinear systems using backstepping
IEEE Transactions on Neural Networks
Neural-network hybrid control for antilock braking systems
IEEE Transactions on Neural Networks
Backstepping wavelet neural network control for indirect field-oriented induction motor drive
IEEE Transactions on Neural Networks
Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Accuracy analysis for wavelet approximations
IEEE Transactions on Neural Networks
Wavelet network-based motion control of DC motors
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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This paper proposes a wavelet based adaptive backstepping controller for a class of nonlinear, nonregular systems i.e. the nonlinear systems lacking well defined relative degree. The controller is designed to provide the desired performance in presence of actuator constraints. Proposed controller comprises of wavelet based backstepping controller and a robust controller. Wavelet backstepping controller is the principal controller while robust controller is designed to achieve the desired performance by attenuating the effect of approximation error caused by wavelet identifier. Wavelet networks, which are having superior learning capability in comparison to conventional neural network, are used for approximation of unknown system dynamics. Also the adaptation laws are derived in the sense of Lyapunov function and Barbalat's lemma, assuring the stability of the system. To deal with actuator constraints, the system is augmented with additional dynamics based on the error analysis so as to recover the unconstrained response rapidly while preserving the system stability.