Journal ArticleDOI
Stable control of nonlinear systems using neural networks
Eli Tzirkel-Hancock,F. Fallside +1 more
TLDR
It is demonstrated that as a result of using sliding control, better use of the network's approximation ability can be achieved, and the asymptotic tracking error can be made dependent only on inherent network approximation errors and the frequency range of unmodelled dynamical modes.Abstract:
A neural-network-based direct control architecture is presented that achieves output tracking for a class of continuous-time nonlinear plants, for which the nonlinearities are unknown. The controller employs neural networks to perform approximate input/output plant linearization. The network parameters are adapted according to a stability principle. The architecture is based on a modification of a method previously proposed by the authors, where the modification comprises adding a sliding control term to the controller. This modification serves two purposes: first, as suggested by Sanner and Slotine,1 sliding control compensates for plant uncertainties outside the state region where the networks are used, thus providing global stability; second, the sliding control compensates for inherent network approximation errors, hence improving tracking performance.
A complete stability and tracking error convergence proof is given and the setting of the controller parameters is discussed. It is demonstrated that as a result of using sliding control, better use of the network's approximation ability can be achieved, and the asymptotic tracking error can be made dependent only on inherent network approximation errors and the frequency range of unmodelled dynamical modes. Two simulations are provided to demonstrate the features of the control method.read more
Citations
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Journal ArticleDOI
Stable adaptive neural control scheme for nonlinear systems
TL;DR: A design methodology is developed that expands the class of nonlinear systems that adaptive neural control schemes can be applied to and relaxes some of the restrictive assumptions that are usually made.
Journal ArticleDOI
Output feedback control of nonlinear systems using RBF neural networks
TL;DR: An adaptive output feedback control scheme for the output tracking of a class of continuous-time nonlinear plants is presented and it is shown that by using adaptive control in conjunction with robust control, it is possible to tolerate larger approximation errors resulting from the use of lower order networks.
Journal ArticleDOI
Persistency of Excitation in Identification Using Radial Basis Function Approximants
TL;DR: In this paper, it is shown that if the regressor vector is constructed out of radial basis function approximants, it will be persistently exciting, provided a kind of "ergodic" condition is satisfied.
Journal ArticleDOI
Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks
TL;DR: A direct adaptive state-feedback controller is proposed for highly nonlinear systems and employs a neural network with flexible structure, i.e., an online variation of the number of neurons that approximates and adaptively cancels an unknown plant nonlinearity.
Journal ArticleDOI
A dual neural network for kinematic control of redundant robot manipulators
Youshen Xia,Jun Wang +1 more
TL;DR: The dual network is presented, which is composed of a single layer of neurons, and the number of neurons is equal to the dimensionality of the workspace, and is proven to be globally exponentially stable.
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